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NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_moveit/src/cumotion_planning_context.cpp | // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// SPDX-License-Identifier: Apache-2.0
#include "isaac_ros_cumotion_moveit/cumotion_planning_context.hpp"
namespace nvidia
{
namespace isaac
{
namespace manipulation
{
bool CumotionPlanningContext::solve(planning_interface::MotionPlanDetailedResponse & res)
{
return cumotion_interface_->solve(planning_scene_, request_, res);
}
bool CumotionPlanningContext::solve(planning_interface::MotionPlanResponse & res)
{
planning_interface::MotionPlanDetailedResponse res_detailed;
bool planning_success = solve(res_detailed);
res.error_code_ = res_detailed.error_code_;
if (planning_success) {
res.trajectory_ = res_detailed.trajectory_[0];
res.planning_time_ = res_detailed.processing_time_[0];
}
return planning_success;
}
} // namespace manipulation
} // namespace isaac
} // namespace nvidia
| 1,514 | C++ | 29.299999 | 89 | 0.752972 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_moveit/src/cumotion_planner_manager.cpp | // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// SPDX-License-Identifier: Apache-2.0
#include "isaac_ros_cumotion_moveit/cumotion_planner_manager.hpp"
#include "moveit/planning_interface/planning_interface.h"
#include "moveit/planning_scene/planning_scene.h"
#include "pluginlib/class_list_macros.hpp"
#include "isaac_ros_cumotion_moveit/cumotion_planning_context.hpp"
namespace nvidia
{
namespace isaac
{
namespace manipulation
{
bool CumotionPlannerManager::initialize(
const moveit::core::RobotModelConstPtr & model,
const rclcpp::Node::SharedPtr & node,
const std::string & parameter_namespace)
{
node_ = node;
for (const std::string & group_name : model->getJointModelGroupNames()) {
planning_contexts_[group_name] =
std::make_shared<CumotionPlanningContext>("cumotion_planning_context", group_name, node);
}
static_cast<void>(model); // Suppress "unused" warning.
static_cast<void>(parameter_namespace); // Suppress "unused" warning.
return true;
}
std::string CumotionPlannerManager::getDescription() const
{
return "Generate minimum-jerk trajectories using NVIDIA Isaac ROS cuMotion";
}
void CumotionPlannerManager::getPlanningAlgorithms(std::vector<std::string> & algs) const
{
algs.clear();
algs.push_back(kCumotionPlannerId);
}
planning_interface::PlanningContextPtr CumotionPlannerManager::getPlanningContext(
const planning_scene::PlanningSceneConstPtr & planning_scene,
const planning_interface::MotionPlanRequest & req,
moveit_msgs::msg::MoveItErrorCodes & error_code) const
{
error_code.val = moveit_msgs::msg::MoveItErrorCodes::SUCCESS;
if (!planning_scene) {
RCLCPP_ERROR(node_->get_logger(), "No planning scene supplied as input");
error_code.val = moveit_msgs::msg::MoveItErrorCodes::FAILURE;
return planning_interface::PlanningContextPtr();
}
if (req.group_name.empty()) {
RCLCPP_ERROR(node_->get_logger(), "No group specified to plan for");
error_code.val = moveit_msgs::msg::MoveItErrorCodes::INVALID_GROUP_NAME;
return planning_interface::PlanningContextPtr();
}
// Retrieve and configure existing context.
const std::shared_ptr<CumotionPlanningContext> & context = planning_contexts_.at(req.group_name);
context->setPlanningScene(planning_scene);
context->setMotionPlanRequest(req);
error_code.val = moveit_msgs::msg::MoveItErrorCodes::SUCCESS;
return context;
}
void CumotionPlannerManager::setPlannerConfigurations(
const planning_interface::PlannerConfigurationMap & pcs)
{
planner_configs_ = pcs;
}
} // namespace manipulation
} // namespace isaac
} // namespace nvidia
// Register the `CumotionPlannerManager` class as a plugin.
PLUGINLIB_EXPORT_CLASS(
nvidia::isaac::manipulation::CumotionPlannerManager,
planning_interface::PlannerManager)
| 3,434 | C++ | 32.349514 | 99 | 0.753058 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_moveit/include/isaac_ros_cumotion_moveit/cumotion_planner_manager.hpp | // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// SPDX-License-Identifier: Apache-2.0
#ifndef ISAAC_ROS_CUMOTION_PLANNER_MANAGER_H
#define ISAAC_ROS_CUMOTION_PLANNER_MANAGER_H
#include <map>
#include <string>
#include <vector>
#include "moveit/planning_interface/planning_interface.h"
#include "moveit/planning_scene/planning_scene.h"
#include "isaac_ros_cumotion_moveit/cumotion_planning_context.hpp"
namespace nvidia
{
namespace isaac
{
namespace manipulation
{
class CumotionPlannerManager : public planning_interface::PlannerManager
{
inline static constexpr char kCumotionPlannerId[] = "cuMotion";
public:
CumotionPlannerManager()
{
}
bool initialize(
const moveit::core::RobotModelConstPtr & model,
const rclcpp::Node::SharedPtr & node,
const std::string & parameter_namespace) override;
bool canServiceRequest(const planning_interface::MotionPlanRequest & req) const override
{
return req.planner_id == kCumotionPlannerId;
}
std::string getDescription() const override;
void getPlanningAlgorithms(std::vector<std::string> & algs) const override;
planning_interface::PlanningContextPtr getPlanningContext(
const planning_scene::PlanningSceneConstPtr & planning_scene,
const planning_interface::MotionPlanRequest & req,
moveit_msgs::msg::MoveItErrorCodes & error_code) const override;
void setPlannerConfigurations(const planning_interface::PlannerConfigurationMap & pcs) override;
private:
std::shared_ptr<rclcpp::Node> node_;
std::map<std::string, std::shared_ptr<CumotionPlanningContext>> planning_contexts_;
planning_interface::PlannerConfigurationMap planner_configs_;
};
} // namespace manipulation
} // namespace isaac
} // namespace nvidia
#endif // ISAAC_ROS_CUMOTION_PLANNER_MANAGER_H
| 2,423 | C++ | 30.076923 | 98 | 0.761865 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_moveit/include/isaac_ros_cumotion_moveit/cumotion_move_group_client.hpp | // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// SPDX-License-Identifier: Apache-2.0
#ifndef ISAAC_ROS_CUMOTION_MOVE_GROUP_CLIENT_H
#define ISAAC_ROS_CUMOTION_MOVE_GROUP_CLIENT_H
#include <future>
#include <memory>
#include "moveit/planning_interface/planning_interface.h"
#include "moveit/planning_scene/planning_scene.h"
#include "moveit_msgs/action/move_group.hpp"
#include "rclcpp/rclcpp.hpp"
#include "rclcpp_action/rclcpp_action.hpp"
namespace nvidia
{
namespace isaac
{
namespace manipulation
{
class CumotionMoveGroupClient
{
using GoalHandle = rclcpp_action::ClientGoalHandle<moveit_msgs::action::MoveGroup>;
public:
CumotionMoveGroupClient(const rclcpp::Node::SharedPtr & node);
bool sendGoal();
void updateGoal(
const planning_scene::PlanningSceneConstPtr & planning_scene,
const planning_interface::MotionPlanRequest & req);
void getGoal();
bool result_ready;
bool success;
moveit_msgs::msg::MotionPlanDetailedResponse plan_response;
private:
void goalResponseCallback(const GoalHandle::SharedPtr & future);
void feedbackCallback(
GoalHandle::SharedPtr,
const std::shared_ptr<const moveit_msgs::action::MoveGroup::Feedback> feedback);
void resultCallback(const GoalHandle::WrappedResult & result);
bool get_goal_handle_;
bool get_result_handle_;
std::shared_ptr<rclcpp::Node> node_;
rclcpp::CallbackGroup::SharedPtr client_cb_group_;
rclcpp_action::Client<moveit_msgs::action::MoveGroup>::SharedPtr client_;
rclcpp_action::Client<moveit_msgs::action::MoveGroup>::SendGoalOptions send_goal_options_;
std::shared_future<GoalHandle::SharedPtr> goal_h_;
std::shared_future<GoalHandle::WrappedResult> result_future_;
moveit_msgs::msg::PlanningScene planning_scene_;
planning_interface::MotionPlanRequest planning_request_;
};
} // namespace manipulation
} // namespace isaac
} // namespace nvidia
#endif // ISAAC_ROS_CUMOTION_MOVE_GROUP_CLIENT_H
| 2,577 | C++ | 30.439024 | 92 | 0.759022 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_moveit/include/isaac_ros_cumotion_moveit/cumotion_interface.hpp | // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// SPDX-License-Identifier: Apache-2.0
#ifndef ISAAC_ROS_CUMOTION_INTERFACE_H
#define ISAAC_ROS_CUMOTION_INTERFACE_H
#include <memory>
#include "moveit/planning_interface/planning_interface.h"
#include "rclcpp/rclcpp.hpp"
#include "isaac_ros_cumotion_moveit/cumotion_move_group_client.hpp"
namespace nvidia
{
namespace isaac
{
namespace manipulation
{
class CumotionInterface
{
public:
CumotionInterface(const rclcpp::Node::SharedPtr & node)
: node_(node),
action_client_(std::make_shared<CumotionMoveGroupClient>(node))
{
}
bool solve(
const planning_scene::PlanningSceneConstPtr & planning_scene,
const planning_interface::MotionPlanRequest & request,
planning_interface::MotionPlanDetailedResponse & response);
bool planner_busy = false;
private:
std::shared_ptr<rclcpp::Node> node_;
std::shared_ptr<CumotionMoveGroupClient> action_client_;
};
} // namespace manipulation
} // namespace isaac
} // namespace nvidia
#endif // ISAAC_ROS_CUMOTION_INTERFACE_H
| 1,698 | C++ | 26.852459 | 75 | 0.750883 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_moveit/include/isaac_ros_cumotion_moveit/cumotion_planning_context.hpp | // SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
// Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
// SPDX-License-Identifier: Apache-2.0
#ifndef ISAAC_ROS_CUMOTION_PLANNING_CONTEXT_H
#define ISAAC_ROS_CUMOTION_PLANNING_CONTEXT_H
#include <memory>
#include <string>
#include "moveit/planning_interface/planning_interface.h"
#include "isaac_ros_cumotion_moveit/cumotion_interface.hpp"
namespace nvidia
{
namespace isaac
{
namespace manipulation
{
class CumotionPlanningContext : public planning_interface::PlanningContext
{
public:
CumotionPlanningContext(
const std::string & context_name,
const std::string & group_name,
const rclcpp::Node::SharedPtr & node)
: planning_interface::PlanningContext(context_name, group_name),
cumotion_interface_(std::make_shared<CumotionInterface>(node))
{
}
~CumotionPlanningContext() override
{
}
bool solve(planning_interface::MotionPlanResponse & res) override;
bool solve(planning_interface::MotionPlanDetailedResponse & res) override;
bool terminate() override
{
return true;
}
void clear() override
{
}
private:
std::shared_ptr<CumotionInterface> cumotion_interface_;
};
} // namespace manipulation
} // namespace isaac
} // namespace nvidia
#endif // ISAAC_ROS_CUMOTION_PLANNING_CONTEXT_H
| 1,893 | C++ | 24.945205 | 76 | 0.745378 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_moveit/config/isaac_ros_cumotion_planning.yaml | planning_plugin: isaac_ros_cumotion_moveit/CumotionPlanner
request_adapters: >-
default_planner_request_adapters/FixWorkspaceBounds
default_planner_request_adapters/FixStartStateBounds
default_planner_request_adapters/FixStartStateCollision
default_planner_request_adapters/FixStartStatePathConstraints
start_state_max_bounds_error: 0.1
num_steps: 32
| 359 | YAML | 38.999996 | 63 | 0.849582 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_examples/setup.py | # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
from glob import glob
import os
from setuptools import find_packages, setup
package_name = 'isaac_ros_cumotion_examples'
setup(
name=package_name,
version='3.0.0',
packages=find_packages(exclude=['test']),
data_files=[
('share/ament_index/resource_index/packages', ['resource/' + package_name]),
('share/' + package_name, ['package.xml']),
(
os.path.join('share', package_name, 'launch'),
glob(os.path.join('launch', '*launch.[pxy][yma]*')),
),
(
os.path.join('share', package_name, 'rviz'),
glob(os.path.join('rviz', '*.rviz')),
),
],
install_requires=['setuptools'],
zip_safe=True,
maintainer='Isaac ROS Maintainers',
maintainer_email='[email protected]',
description='Examples demonstrating Isaac ROS cuMotion with MoveIt',
license='Apache-2.0',
tests_require=['pytest'],
)
| 1,655 | Python | 32.795918 | 84 | 0.673716 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_examples/test/test_flake8.py | # Copyright 2017 Open Source Robotics Foundation, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ament_flake8.main import main_with_errors
import pytest
@pytest.mark.flake8
@pytest.mark.linter
def test_flake8():
rc, errors = main_with_errors(argv=[])
assert rc == 0, \
'Found %d code style errors / warnings:\n' % len(errors) + \
'\n'.join(errors)
| 884 | Python | 33.03846 | 74 | 0.725113 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_examples/test/test_pep257.py | # Copyright 2015 Open Source Robotics Foundation, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ament_pep257.main import main
import pytest
@pytest.mark.linter
@pytest.mark.pep257
def test_pep257():
rc = main(argv=['.', 'test'])
assert rc == 0, 'Found code style errors / warnings'
| 803 | Python | 32.499999 | 74 | 0.743462 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_examples/test/test_copyright.py | # Copyright 2015 Open Source Robotics Foundation, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ament_copyright.main import main
import pytest
@pytest.mark.copyright
@pytest.mark.linter
def test_copyright():
rc = main(argv=[
'.', 'test',
'--exclude',
'./launch/franka_isaac_sim.launch.py',
'./isaac_sim_scripts/start_isaac_sim_franka.py'
])
assert rc == 0, 'Found errors'
| 929 | Python | 31.068964 | 74 | 0.710441 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_examples/launch/franka.launch.py | # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
# To avoid code duplication, we patch and then execute the Franka demo launch file provided by
# the moveit2_tutorials package.
from os import path
from ament_index_python.packages import get_package_share_directory
import yaml
def augment_moveit_config(moveit_config):
"""Add cuMotion and its config to the planning_pipelines dict of a MoveItConfigs object."""
config_file_path = path.join(
get_package_share_directory('isaac_ros_cumotion_moveit'),
'config',
'isaac_ros_cumotion_planning.yaml'
)
with open(config_file_path) as config_file:
config = yaml.safe_load(config_file)
moveit_config.planning_pipelines['planning_pipelines'].append('isaac_ros_cumotion')
moveit_config.planning_pipelines['isaac_ros_cumotion'] = config
def generate_launch_description():
franka_demo_launch_file = path.join(
get_package_share_directory('moveit2_tutorials'),
'launch',
'demo.launch.py'
)
lf = open(franka_demo_launch_file).read()
# Rename generate_launch_description() in base launch file.
lf = lf.replace('generate_launch_description', 'generate_base_launch_description')
# The standard way to make isaac_ros_cumotion_planning.yaml available to MoveIt would be to
# copy the file into the config/ directory within a given robot's moveit_config package.
# It would then suffice to add "isaac_ros_cumotion" to the list of planning_pipelines in the
# MoveItConfigsBuilder, e.g., via the following substitution.
#
# lf = lf.replace('"ompl"', '"isaac_ros_cumotion", "ompl"')
#
# Here we avoid adding the file to the moveit_resources_panda package by loading the file
# manually and augmenting the MoveItConfigs object after it's built.
lf = lf.replace(
'run_move_group_node =',
'augment_moveit_config(moveit_config)\n run_move_group_node ='
)
# Execute modified launch file.
exec(lf, globals())
return generate_base_launch_description() # noqa: F821
| 2,742 | Python | 38.185714 | 96 | 0.71663 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_examples/launch/franka_isaac_sim.launch.py | # SPDX-FileCopyrightText: Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# This launch file was originally derived from
# https://github.com/ros-planning/moveit2_tutorials/blob/efef1d3/doc/how_to_guides/isaac_panda/launch/isaac_demo.launch.py # noqa
#
# BSD 3-Clause License
#
# Copyright (c) 2008-2013, Willow Garage, Inc.
# Copyright (c) 2015-2023, PickNik, LLC.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import os
from ament_index_python.packages import get_package_share_directory
from launch import LaunchDescription
from launch.actions import DeclareLaunchArgument
from launch.substitutions import LaunchConfiguration
from launch_ros.actions import Node
from moveit_configs_utils import MoveItConfigsBuilder
import yaml
def generate_launch_description():
# Command-line arguments
ros2_control_hardware_type = DeclareLaunchArgument(
'ros2_control_hardware_type',
default_value='isaac',
description=(
'ROS2 control hardware interface type to use for the launch file -- '
'possible values: [mock_components, isaac]'
)
)
moveit_config = (
MoveItConfigsBuilder('moveit_resources_panda')
.robot_description(
file_path='config/panda.urdf.xacro',
mappings={
'ros2_control_hardware_type': LaunchConfiguration(
'ros2_control_hardware_type'
)
},
)
.robot_description_semantic(file_path='config/panda.srdf')
.trajectory_execution(file_path='config/gripper_moveit_controllers.yaml')
.planning_pipelines(pipelines=['ompl', 'pilz_industrial_motion_planner'])
.to_moveit_configs()
)
# Add cuMotion to list of planning pipelines.
cumotion_config_file_path = os.path.join(
get_package_share_directory('isaac_ros_cumotion_moveit'),
'config',
'isaac_ros_cumotion_planning.yaml'
)
with open(cumotion_config_file_path) as cumotion_config_file:
cumotion_config = yaml.safe_load(cumotion_config_file)
moveit_config.planning_pipelines['planning_pipelines'].append('isaac_ros_cumotion')
moveit_config.planning_pipelines['isaac_ros_cumotion'] = cumotion_config
# The current Franka asset in Isaac Sim 2023.1.1 tends to drift slightly from commanded joint
# positions, which prevents trajectory execution if the drift exceeds `allowed_start_tolerance`
# for any joint; the default tolerance is 0.01 radians. This is more likely to occur if the
# robot hasn't fully settled when the trajectory is computed or if significant time has
# elapsed between trajectory computation and execution. For this simulation use case,
# there's little harm in disabling this check by setting `allowed_start_tolerance` to 0.
moveit_config.trajectory_execution['trajectory_execution']['allowed_start_tolerance'] = 0.0
# Start the actual move_group node/action server
move_group_node = Node(
package='moveit_ros_move_group',
executable='move_group',
output='screen',
parameters=[moveit_config.to_dict()],
arguments=['--ros-args', '--log-level', 'info'],
)
# RViz
rviz_config_file = os.path.join(
get_package_share_directory('isaac_ros_cumotion_examples'),
'rviz',
'franka_moveit_config.rviz',
)
rviz_node = Node(
package='rviz2',
executable='rviz2',
name='rviz2',
output='log',
arguments=['-d', rviz_config_file],
parameters=[
moveit_config.robot_description,
moveit_config.robot_description_semantic,
moveit_config.robot_description_kinematics,
moveit_config.planning_pipelines,
moveit_config.joint_limits,
],
)
# Static TF
world2robot_tf_node = Node(
package='tf2_ros',
executable='static_transform_publisher',
name='static_transform_publisher',
output='log',
arguments=['--frame-id', 'world', '--child-frame-id', 'panda_link0'],
)
hand2camera_tf_node = Node(
package='tf2_ros',
executable='static_transform_publisher',
name='static_transform_publisher',
output='log',
arguments=[
'0.04',
'0.0',
'0.04',
'0.0',
'0.0',
'0.0',
'panda_hand',
'sim_camera',
],
)
# Publish TF
robot_state_publisher = Node(
package='robot_state_publisher',
executable='robot_state_publisher',
name='robot_state_publisher',
output='both',
parameters=[moveit_config.robot_description],
)
# ros2_control using FakeSystem as hardware
ros2_controllers_path = os.path.join(
get_package_share_directory('moveit_resources_panda_moveit_config'),
'config',
'ros2_controllers.yaml',
)
ros2_control_node = Node(
package='controller_manager',
executable='ros2_control_node',
parameters=[ros2_controllers_path],
remappings=[
('/controller_manager/robot_description', '/robot_description'),
],
output='screen',
)
joint_state_broadcaster_spawner = Node(
package='controller_manager',
executable='spawner',
arguments=[
'joint_state_broadcaster',
'--controller-manager',
'/controller_manager',
],
)
panda_arm_controller_spawner = Node(
package='controller_manager',
executable='spawner',
arguments=['panda_arm_controller', '-c', '/controller_manager'],
)
panda_hand_controller_spawner = Node(
package='controller_manager',
executable='spawner',
arguments=['panda_hand_controller', '-c', '/controller_manager'],
)
return LaunchDescription(
[
ros2_control_hardware_type,
rviz_node,
world2robot_tf_node,
hand2camera_tf_node,
robot_state_publisher,
move_group_node,
ros2_control_node,
joint_state_broadcaster_spawner,
panda_arm_controller_spawner,
panda_hand_controller_spawner,
]
)
| 8,349 | Python | 35.946902 | 130 | 0.662714 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_examples/launch/ur.launch.py | # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
# To avoid code duplication, we patch and then execute the launch file provided by the
# ur_moveit_config package.
from os import path
from ament_index_python.packages import get_package_share_directory
import yaml
def cumotion_params():
# The standard way to make isaac_ros_cumotion_planning.yaml available to MoveIt would be to
# copy the file into the config/ directory within a given robot's moveit_config package.
# It would then suffice to add "isaac_ros_cumotion" to the list of planning_pipelines.
# Here we avoid adding the file to the ur_moveit_config package by loading the file manually
# and adding its contents to the parameter list.
config_file_path = path.join(
get_package_share_directory('isaac_ros_cumotion_moveit'),
'config',
'isaac_ros_cumotion_planning.yaml'
)
with open(config_file_path) as config_file:
config = yaml.safe_load(config_file)
return (
{'planning_pipelines': ['ompl', 'isaac_ros_cumotion']},
{'isaac_ros_cumotion': config}
)
def generate_launch_description():
ur_moveit_launch_file = path.join(
get_package_share_directory('ur_moveit_config'),
'launch',
'ur_moveit.launch.py'
)
lf = open(ur_moveit_launch_file).read()
# Rename generate_launch_description() in base launch file.
lf = lf.replace('generate_launch_description', 'generate_base_launch_description')
# Add required parameters to the move_group node. This substitution relies on the fact that
# the string "moveit_controllers," appears only once in the base launch file.
lf = lf.replace(
'moveit_controllers,',
'moveit_controllers, *cumotion_params(),'
)
# Execute modified launch file.
exec(lf, globals())
return generate_base_launch_description() # noqa: F821
| 2,565 | Python | 35.657142 | 96 | 0.710331 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_cumotion_examples/isaac_sim_scripts/start_isaac_sim_franka.py | # -*- coding: utf-8 -*-
# Copyright (c) 2020-2024, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
# Portions contributed by PickNik, LLC under BSD 3-Clause License
#
# Copyright (c) 2023, PickNik, LLC.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# * Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
# This Isaac Sim example is derived from
# https://github.com/ros-planning/moveit2_tutorials/blob/efef1d3/doc/how_to_guides/isaac_panda/launch/isaac_moveit.py
# which in turn was derived from an example provided with Isaac Sim 2022.2.1, found at
# standalone_examples/api/omni.isaac.ros2_bridge/moveit.py
#
# flake8: noqa
import sys
import re
import os
import carb
import numpy as np
from omni.isaac.kit import SimulationApp
FRANKA_STAGE_PATH = "/Franka"
FRANKA_USD_PATH = "/Isaac/Robots/Franka/franka_alt_fingers.usd"
CAMERA_PRIM_PATH = f"{FRANKA_STAGE_PATH}/panda_hand/geometry/realsense/realsense_camera"
BACKGROUND_STAGE_PATH = "/background"
BACKGROUND_USD_PATH = "/Isaac/Environments/Simple_Room/simple_room.usd"
GRAPH_PATH = "/ActionGraph"
REALSENSE_VIEWPORT_NAME = "realsense_viewport"
CONFIG = {"renderer": "RayTracedLighting", "headless": False}
# Example ROS2 bridge sample demonstrating the manual loading of stages
# and creation of ROS components
simulation_app = SimulationApp(CONFIG)
# More imports that need to compare after we create the app
from omni.isaac.core import SimulationContext # noqa E402
from omni.isaac.core.utils.prims import set_targets
from omni.isaac.core.utils import ( # noqa E402
extensions,
nucleus,
prims,
rotations,
stage,
viewports,
)
from pxr import Gf, UsdGeom # noqa E402
import omni.graph.core as og # noqa E402
import omni
# enable ROS2 bridge extension
extensions.enable_extension("omni.isaac.ros2_bridge")
simulation_context = SimulationContext(stage_units_in_meters=1.0)
# Locate Isaac Sim assets folder to load environment and robot stages
assets_root_path = nucleus.get_assets_root_path()
if assets_root_path is None:
carb.log_error("Could not find Isaac Sim assets folder")
simulation_app.close()
sys.exit()
# Preparing stage
viewports.set_camera_view(eye=np.array([1.2, 1.2, 0.8]), target=np.array([0, 0, 0.5]))
# Loading the simple_room environment
stage.add_reference_to_stage(
assets_root_path + BACKGROUND_USD_PATH, BACKGROUND_STAGE_PATH
)
# Loading the franka robot USD
prims.create_prim(
FRANKA_STAGE_PATH,
"Xform",
position=np.array([0, -0.64, 0]),
orientation=rotations.gf_rotation_to_np_array(Gf.Rotation(Gf.Vec3d(0, 0, 1), 90)),
usd_path=assets_root_path + FRANKA_USD_PATH,
)
# add some objects, spread evenly along the X axis
# with a fixed offset from the robot in the Y and Z
prims.create_prim(
"/cracker_box",
"Xform",
position=np.array([-0.2, -0.25, 0.15]),
orientation=rotations.gf_rotation_to_np_array(Gf.Rotation(Gf.Vec3d(1, 0, 0), -90)),
usd_path=assets_root_path
+ "/Isaac/Props/YCB/Axis_Aligned_Physics/003_cracker_box.usd",
)
prims.create_prim(
"/sugar_box",
"Xform",
position=np.array([-0.07, -0.25, 0.1]),
orientation=rotations.gf_rotation_to_np_array(Gf.Rotation(Gf.Vec3d(0, 1, 0), -90)),
usd_path=assets_root_path
+ "/Isaac/Props/YCB/Axis_Aligned_Physics/004_sugar_box.usd",
)
prims.create_prim(
"/soup_can",
"Xform",
position=np.array([0.1, -0.25, 0.10]),
orientation=rotations.gf_rotation_to_np_array(Gf.Rotation(Gf.Vec3d(1, 0, 0), -90)),
usd_path=assets_root_path
+ "/Isaac/Props/YCB/Axis_Aligned_Physics/005_tomato_soup_can.usd",
)
prims.create_prim(
"/mustard_bottle",
"Xform",
position=np.array([0.0, 0.15, 0.12]),
orientation=rotations.gf_rotation_to_np_array(Gf.Rotation(Gf.Vec3d(1, 0, 0), -90)),
usd_path=assets_root_path
+ "/Isaac/Props/YCB/Axis_Aligned_Physics/006_mustard_bottle.usd",
)
simulation_app.update()
try:
ros_domain_id = int(os.environ["ROS_DOMAIN_ID"])
print("Using ROS_DOMAIN_ID: ", ros_domain_id)
except ValueError:
print("Invalid ROS_DOMAIN_ID integer value. Setting value to 0")
ros_domain_id = 0
except KeyError:
print("ROS_DOMAIN_ID environment variable is not set. Setting value to 0")
ros_domain_id = 0
# Creating a action graph with ROS component nodes
try:
og.Controller.edit(
{"graph_path": GRAPH_PATH, "evaluator_name": "execution"},
{
og.Controller.Keys.CREATE_NODES: [
("OnImpulseEvent", "omni.graph.action.OnImpulseEvent"),
("ReadSimTime", "omni.isaac.core_nodes.IsaacReadSimulationTime"),
("Context", "omni.isaac.ros2_bridge.ROS2Context"),
("PublishJointState", "omni.isaac.ros2_bridge.ROS2PublishJointState"),
(
"SubscribeJointState",
"omni.isaac.ros2_bridge.ROS2SubscribeJointState",
),
(
"ArticulationController",
"omni.isaac.core_nodes.IsaacArticulationController",
),
("PublishClock", "omni.isaac.ros2_bridge.ROS2PublishClock"),
("OnTick", "omni.graph.action.OnTick"),
("createViewport", "omni.isaac.core_nodes.IsaacCreateViewport"),
(
"getRenderProduct",
"omni.isaac.core_nodes.IsaacGetViewportRenderProduct",
),
("setCamera", "omni.isaac.core_nodes.IsaacSetCameraOnRenderProduct"),
("cameraHelperRgb", "omni.isaac.ros2_bridge.ROS2CameraHelper"),
("cameraHelperInfo", "omni.isaac.ros2_bridge.ROS2CameraHelper"),
("cameraHelperDepth", "omni.isaac.ros2_bridge.ROS2CameraHelper"),
],
og.Controller.Keys.CONNECT: [
("OnImpulseEvent.outputs:execOut", "PublishJointState.inputs:execIn"),
("OnImpulseEvent.outputs:execOut", "SubscribeJointState.inputs:execIn"),
("OnImpulseEvent.outputs:execOut", "PublishClock.inputs:execIn"),
(
"OnImpulseEvent.outputs:execOut",
"ArticulationController.inputs:execIn",
),
("Context.outputs:context", "PublishJointState.inputs:context"),
("Context.outputs:context", "SubscribeJointState.inputs:context"),
("Context.outputs:context", "PublishClock.inputs:context"),
(
"ReadSimTime.outputs:simulationTime",
"PublishJointState.inputs:timeStamp",
),
("ReadSimTime.outputs:simulationTime", "PublishClock.inputs:timeStamp"),
(
"SubscribeJointState.outputs:jointNames",
"ArticulationController.inputs:jointNames",
),
(
"SubscribeJointState.outputs:positionCommand",
"ArticulationController.inputs:positionCommand",
),
(
"SubscribeJointState.outputs:velocityCommand",
"ArticulationController.inputs:velocityCommand",
),
(
"SubscribeJointState.outputs:effortCommand",
"ArticulationController.inputs:effortCommand",
),
("OnTick.outputs:tick", "createViewport.inputs:execIn"),
("createViewport.outputs:execOut", "getRenderProduct.inputs:execIn"),
("createViewport.outputs:viewport", "getRenderProduct.inputs:viewport"),
("getRenderProduct.outputs:execOut", "setCamera.inputs:execIn"),
(
"getRenderProduct.outputs:renderProductPath",
"setCamera.inputs:renderProductPath",
),
("setCamera.outputs:execOut", "cameraHelperRgb.inputs:execIn"),
("setCamera.outputs:execOut", "cameraHelperInfo.inputs:execIn"),
("setCamera.outputs:execOut", "cameraHelperDepth.inputs:execIn"),
("Context.outputs:context", "cameraHelperRgb.inputs:context"),
("Context.outputs:context", "cameraHelperInfo.inputs:context"),
("Context.outputs:context", "cameraHelperDepth.inputs:context"),
(
"getRenderProduct.outputs:renderProductPath",
"cameraHelperRgb.inputs:renderProductPath",
),
(
"getRenderProduct.outputs:renderProductPath",
"cameraHelperInfo.inputs:renderProductPath",
),
(
"getRenderProduct.outputs:renderProductPath",
"cameraHelperDepth.inputs:renderProductPath",
),
],
og.Controller.Keys.SET_VALUES: [
("Context.inputs:domain_id", ros_domain_id),
# Setting the /Franka target prim to Articulation Controller node
("ArticulationController.inputs:usePath", True),
("ArticulationController.inputs:robotPath", FRANKA_STAGE_PATH),
("PublishJointState.inputs:topicName", "isaac_joint_states"),
("SubscribeJointState.inputs:topicName", "isaac_joint_commands"),
("createViewport.inputs:name", REALSENSE_VIEWPORT_NAME),
("createViewport.inputs:viewportId", 1),
("cameraHelperRgb.inputs:frameId", "sim_camera"),
("cameraHelperRgb.inputs:topicName", "rgb"),
("cameraHelperRgb.inputs:type", "rgb"),
("cameraHelperInfo.inputs:frameId", "sim_camera"),
("cameraHelperInfo.inputs:topicName", "camera_info"),
("cameraHelperInfo.inputs:type", "camera_info"),
("cameraHelperDepth.inputs:frameId", "sim_camera"),
("cameraHelperDepth.inputs:topicName", "depth"),
("cameraHelperDepth.inputs:type", "depth"),
],
},
)
except Exception as e:
print(e)
# Setting the /Franka target prim to Publish JointState node
set_targets(
prim=stage.get_current_stage().GetPrimAtPath("/ActionGraph/PublishJointState"),
attribute="inputs:targetPrim",
target_prim_paths=[FRANKA_STAGE_PATH],
)
# Fix camera settings since the defaults in the realsense model are inaccurate
realsense_prim = camera_prim = UsdGeom.Camera(
stage.get_current_stage().GetPrimAtPath(CAMERA_PRIM_PATH)
)
realsense_prim.GetHorizontalApertureAttr().Set(20.955)
realsense_prim.GetVerticalApertureAttr().Set(15.7)
realsense_prim.GetFocalLengthAttr().Set(18.8)
realsense_prim.GetFocusDistanceAttr().Set(400)
set_targets(
prim=stage.get_current_stage().GetPrimAtPath(GRAPH_PATH + "/setCamera"),
attribute="inputs:cameraPrim",
target_prim_paths=[CAMERA_PRIM_PATH],
)
simulation_app.update()
# need to initialize physics getting any articulation..etc
simulation_context.initialize_physics()
simulation_context.play()
# Dock the second camera window
viewport = omni.ui.Workspace.get_window("Viewport")
rs_viewport = omni.ui.Workspace.get_window(REALSENSE_VIEWPORT_NAME)
rs_viewport.dock_in(viewport, omni.ui.DockPosition.RIGHT)
while simulation_app.is_running():
# Run with a fixed step size
simulation_context.step(render=True)
# Tick the Publish/Subscribe JointState, Publish TF and Publish Clock nodes each frame
og.Controller.set(
og.Controller.attribute("/ActionGraph/OnImpulseEvent.state:enableImpulse"), True
)
simulation_context.stop()
simulation_app.close()
| 13,417 | Python | 40.670807 | 117 | 0.656853 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_esdf_visualizer/setup.py | # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
from setuptools import find_packages, setup
package_name = 'isaac_ros_esdf_visualizer'
setup(
name=package_name,
version='3.0.0',
packages=find_packages(exclude=['test']),
data_files=[
('share/ament_index/resource_index/packages',
['resource/' + package_name]),
('share/' + package_name, ['package.xml']),
],
install_requires=['setuptools'],
zip_safe=True,
maintainer='Isaac ROS Maintainers',
maintainer_email='[email protected]',
description='Package for ESDF Voxel visualizer.',
license='Apache-2.0',
tests_require=['pytest'],
entry_points={
'console_scripts': [
'esdf_visualizer = isaac_ros_esdf_visualizer.esdf_visualizer:main'
],
},
)
| 1,485 | Python | 32.772727 | 78 | 0.694949 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_esdf_visualizer/test/test_flake8.py | # Copyright 2017 Open Source Robotics Foundation, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ament_flake8.main import main_with_errors
import pytest
@pytest.mark.flake8
@pytest.mark.linter
def test_flake8():
rc, errors = main_with_errors(argv=[])
assert rc == 0, \
'Found %d code style errors / warnings:\n' % len(errors) + \
'\n'.join(errors)
| 884 | Python | 33.03846 | 74 | 0.725113 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_esdf_visualizer/test/test_pep257.py | # Copyright 2015 Open Source Robotics Foundation, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ament_pep257.main import main
import pytest
@pytest.mark.linter
@pytest.mark.pep257
def test_pep257():
rc = main(argv=['.', 'test'])
assert rc == 0, 'Found code style errors / warnings'
| 803 | Python | 32.499999 | 74 | 0.743462 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_esdf_visualizer/test/test_copyright.py | # Copyright 2015 Open Source Robotics Foundation, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ament_copyright.main import main
import pytest
@pytest.mark.copyright
@pytest.mark.linter
def test_copyright():
rc = main(argv=['.', 'test'])
assert rc == 0, 'Found errors'
| 790 | Python | 31.958332 | 74 | 0.74557 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_esdf_visualizer/isaac_ros_esdf_visualizer/__init__.py | # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
| 715 | Python | 41.117645 | 74 | 0.770629 |
NVIDIA-ISAAC-ROS/isaac_ros_cumotion/isaac_ros_esdf_visualizer/isaac_ros_esdf_visualizer/esdf_visualizer.py | # SPDX-FileCopyrightText: NVIDIA CORPORATION & AFFILIATES
# Copyright (c) 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# SPDX-License-Identifier: Apache-2.0
from curobo.geom.sdf.world import CollisionCheckerType
from curobo.geom.sdf.world import WorldCollisionConfig
from curobo.geom.sdf.world_voxel import WorldVoxelCollision
from curobo.geom.types import Cuboid as CuCuboid
from curobo.geom.types import VoxelGrid as CuVoxelGrid
from curobo.geom.types import WorldConfig
from curobo.types.base import TensorDeviceType
from geometry_msgs.msg import Point
from geometry_msgs.msg import Vector3
import numpy as np
from nvblox_msgs.srv import EsdfAndGradients
import rclpy
from rclpy.callback_groups import MutuallyExclusiveCallbackGroup
from rclpy.node import Node
from std_msgs.msg import ColorRGBA
import torch
from visualization_msgs.msg import Marker
class ESDFVisualizer(Node):
def __init__(self):
super().__init__('esdf_visualizer')
self.declare_parameter('voxel_dims', [1.25, 1.8, 1.8])
self.declare_parameter('grid_position', [0.0, 0.0, 0.0])
self.declare_parameter('voxel_size', 0.05)
self.declare_parameter('publish_voxel_size', 0.025)
self.declare_parameter('max_publish_voxels', 50000)
self.declare_parameter('esdf_service_name', '/nvblox_node/get_esdf_and_gradient')
self.declare_parameter('robot_base_frame', 'base_link')
self.__esdf_future = None
# Voxel publisher
self.__voxel_pub = self.create_publisher(Marker, '/curobo/voxels', 10)
# ESDF service
esdf_service_name = (
self.get_parameter('esdf_service_name').get_parameter_value().string_value
)
esdf_service_cb_group = MutuallyExclusiveCallbackGroup()
self.__esdf_client = self.create_client(
EsdfAndGradients, esdf_service_name, callback_group=esdf_service_cb_group
)
while not self.__esdf_client.wait_for_service(timeout_sec=1.0):
self.get_logger().info(f'Service({esdf_service_name}) not available, waiting again...')
self.__esdf_req = EsdfAndGradients.Request()
# Timer for calling the ESDF service
timer_period = 0.01
timer_cb_group = MutuallyExclusiveCallbackGroup()
self.timer = self.create_timer(
timer_period, self.timer_callback, callback_group=timer_cb_group
)
self.__voxel_dims = (
self.get_parameter('voxel_dims').get_parameter_value().double_array_value
)
self.__grid_position = (
self.get_parameter('grid_position').get_parameter_value().double_array_value
)
self.__voxel_size = self.get_parameter('voxel_size').get_parameter_value().double_value
self.__publish_voxel_size = (
self.get_parameter('publish_voxel_size').get_parameter_value().double_value
)
self.__max_publish_voxels = (
self.get_parameter('max_publish_voxels').get_parameter_value().integer_value
)
# Init WorldVoxelCollision
world_cfg = WorldConfig.from_dict(
{
'voxel': {
'world_voxel': {
'dims': self.__voxel_dims,
'pose': [
self.__grid_position[0],
self.__grid_position[1],
self.__grid_position[2],
1,
0,
0,
0,
], # x, y, z, qw, qx, qy, qz
'voxel_size': self.__voxel_size,
'feature_dtype': torch.float32,
},
},
},
)
tensor_args = TensorDeviceType()
world_collision_config = WorldCollisionConfig.load_from_dict(
{
'checker_type': CollisionCheckerType.VOXEL,
'max_distance': 10.0,
'n_envs': 1,
},
world_cfg,
tensor_args,
)
self.__world_collision = WorldVoxelCollision(world_collision_config)
self.__robot_base_frame = (
self.get_parameter('robot_base_frame').get_parameter_value().string_value
)
self.__tensor_args = tensor_args
esdf_grid = CuVoxelGrid(
name='world_voxel',
dims=self.__voxel_dims,
pose=[
self.__grid_position[0],
self.__grid_position[1],
self.__grid_position[2],
1,
0,
0,
0,
],
voxel_size=self.__voxel_size,
feature_dtype=torch.float32,
)
self.__grid_shape, _, _ = esdf_grid.get_grid_shape()
def timer_callback(self):
if self.__esdf_future is None:
self.get_logger().info('Calling ESDF service')
# This is half of x,y and z dims
aabb_min = Point()
aabb_min.x = (-0.5 * self.__voxel_dims[0]) + self.__grid_position[0]
aabb_min.y = (-0.5 * self.__voxel_dims[1]) + self.__grid_position[1]
aabb_min.z = (-0.5 * self.__voxel_dims[2]) + self.__grid_position[2]
# This is a voxel size.
voxel_dims = Vector3()
voxel_dims.x = self.__voxel_dims[0]
voxel_dims.y = self.__voxel_dims[1]
voxel_dims.z = self.__voxel_dims[2]
self.__esdf_future = self.send_request(aabb_min, voxel_dims)
if self.__esdf_future is not None and self.__esdf_future.done():
response = self.__esdf_future.result()
self.fill_marker(response)
self.__esdf_future = None
def send_request(self, aabb_min_m, aabb_size_m):
self.__esdf_req.aabb_min_m = aabb_min_m
self.__esdf_req.aabb_size_m = aabb_size_m
self.get_logger().info(
f'ESDF req = {self.__esdf_req.aabb_min_m}, {self.__esdf_req.aabb_size_m}'
)
esdf_future = self.__esdf_client.call_async(self.__esdf_req)
return esdf_future
def get_esdf_voxel_grid(self, esdf_data):
esdf_array = esdf_data.esdf_and_gradients
array_shape = [
esdf_array.layout.dim[0].size,
esdf_array.layout.dim[1].size,
esdf_array.layout.dim[2].size,
]
array_data = np.array(esdf_array.data)
array_data = self.__tensor_args.to_device(array_data)
# Array data is reshaped to x y z channels
array_data = array_data.view(array_shape[0], array_shape[1], array_shape[2]).contiguous()
# Array is squeezed to 1 dimension
array_data = array_data.reshape(-1, 1)
# nvblox uses negative distance inside obstacles, cuRobo needs the opposite:
array_data = -1 * array_data
# nvblox assigns a value of -1000.0 for unobserved voxels, making
array_data[array_data >= 1000.0] = -1000.0
# nvblox distance are at origin of each voxel, cuRobo's esdf needs it to be at faces
array_data = array_data + 0.5 * self.__voxel_size
esdf_grid = CuVoxelGrid(
name='world_voxel',
dims=self.__voxel_dims,
pose=[
self.__grid_position[0],
self.__grid_position[1],
self.__grid_position[2],
1,
0.0,
0.0,
0,
], # x, y, z, qw, qx, qy, qz
voxel_size=self.__voxel_size,
feature_dtype=torch.float32,
feature_tensor=array_data,
)
return esdf_grid
def fill_marker(self, esdf_data):
esdf_grid = self.get_esdf_voxel_grid(esdf_data)
self.__world_collision.update_voxel_data(esdf_grid)
vox_size = self.__publish_voxel_size
voxels = self.__world_collision.get_esdf_in_bounding_box(
CuCuboid(
name='test',
pose=[0.0, 0.0, 0.0, 1, 0, 0, 0], # x, y, z, qw, qx, qy, qz
dims=self.__voxel_dims,
),
voxel_size=vox_size,
)
xyzr_tensor = voxels.xyzr_tensor.clone()
xyzr_tensor[..., 3] = voxels.feature_tensor
self.publish_voxels(xyzr_tensor)
def publish_voxels(self, voxels):
vox_size = 0.25 * self.__publish_voxel_size
# create marker:
marker = Marker()
marker.header.frame_id = self.__robot_base_frame
marker.id = 0
marker.type = 6 # cube list
marker.ns = 'curobo_world'
marker.action = 0
marker.pose.orientation.w = 1.0
marker.lifetime = rclpy.duration.Duration(seconds=1000.0).to_msg()
marker.frame_locked = False
marker.scale.x = vox_size
marker.scale.y = vox_size
marker.scale.z = vox_size
# get only voxels that are inside surfaces:
voxels = voxels[voxels[:, 3] >= 0.0]
vox = voxels.view(-1, 4).cpu().numpy()
marker.points = []
for i in range(min(len(vox), self.__max_publish_voxels)):
pt = Point()
pt.x = float(vox[i, 0])
pt.y = float(vox[i, 1])
pt.z = float(vox[i, 2])
color = ColorRGBA()
d = vox[i, 3]
rgba = [min(1.0, 1.0 - float(d)), 0.0, 0.0, 1.0]
color.r = rgba[0]
color.g = rgba[1]
color.b = rgba[2]
color.a = rgba[3]
marker.colors.append(color)
marker.points.append(pt)
# publish voxels:
marker.header.stamp = self.get_clock().now().to_msg()
self.__voxel_pub.publish(marker)
def main(args=None):
# Initialize the rclpy library
rclpy.init(args=args)
# Create the node
esdf_client = ESDFVisualizer()
# Spin the node so the callback function is called.
try:
esdf_client.get_logger().info('Starting ESDFVisualizer node')
rclpy.spin(esdf_client)
except KeyboardInterrupt:
esdf_client.get_logger().info('Destroying ESDFVisualizer node')
# Destroy the node explicitly
esdf_client.destroy_node()
# Shutdown the ROS client library for Python
rclpy.shutdown()
if __name__ == '__main__':
main()
| 10,873 | Python | 35.006622 | 99 | 0.566265 |
Tbarkin121/GuardDog/README.md | # GuardDog
Makeing a hypoallergenic robot quadruped pet from scratch
| 69 | Markdown | 22.333326 | 57 | 0.84058 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/setup.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Installation script for the 'isaacgymenvs' python package."""
from __future__ import absolute_import
from __future__ import print_function
from __future__ import division
from setuptools import setup, find_packages
import os
# Minimum dependencies required prior to installation
INSTALL_REQUIRES = [
"numpy==1.23.5",
"protobuf==3.20.2",
"omegaconf==2.3.0",
"hydra-core==1.3.2",
"urllib3==1.26.16",
"rl-games==1.6.1",
"moviepy==1.0.3"
]
# Installation operation
setup(
name="omniisaacgymenvs",
author="NVIDIA",
version="2023.1.1a",
description="RL environments for robot learning in NVIDIA Isaac Sim.",
keywords=["robotics", "rl"],
include_package_data=True,
install_requires=INSTALL_REQUIRES,
packages=find_packages("."),
classifiers=["Natural Language :: English", "Programming Language :: Python :: 3.7, 3.8"],
zip_safe=False,
)
# EOF
| 2,474 | Python | 36.499999 | 94 | 0.740905 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/README.md | # Omniverse Isaac Gym Reinforcement Learning Environments for Isaac Sim
## About this repository
This repository contains Reinforcement Learning examples that can be run with the latest release of [Isaac Sim](https://docs.omniverse.nvidia.com/app_isaacsim/app_isaacsim/overview.html). RL examples are trained using PPO from [rl_games](https://github.com/Denys88/rl_games) library and examples are built on top of Isaac Sim's `omni.isaac.core` and `omni.isaac.gym` frameworks.
Please see [release notes](docs/release_notes.md) for the latest updates.
<img src="https://user-images.githubusercontent.com/34286328/171454189-6afafbff-bb61-4aac-b518-24646007cb9f.gif" width="300" height="150"/> <img src="https://user-images.githubusercontent.com/34286328/184172037-cdad9ee8-f705-466f-bbde-3caa6c7dea37.gif" width="300" height="150"/>
<img src="https://user-images.githubusercontent.com/34286328/171454182-0be1b830-bceb-4cfd-93fb-e1eb8871ec68.gif" width="300" height="150"/> <img src="https://user-images.githubusercontent.com/34286328/171454193-e027885d-1510-4ef4-b838-06b37f70c1c7.gif" width="300" height="150"/>
<img src="https://user-images.githubusercontent.com/34286328/184174894-03767aa0-936c-4bfe-bbe9-a6865f539bb4.gif" width="300" height="150"/> <img src="https://user-images.githubusercontent.com/34286328/184168200-152567a8-3354-4947-9ae0-9443a56fee4c.gif" width="300" height="150"/>
<img src="https://user-images.githubusercontent.com/34286328/184176312-df7d2727-f043-46e3-b537-48a583d321b9.gif" width="300" height="150"/> <img src="https://user-images.githubusercontent.com/34286328/184178817-9c4b6b3c-c8a2-41fb-94be-cfc8ece51d5d.gif" width="300" height="150"/>
<img src="https://user-images.githubusercontent.com/34286328/171454160-8cb6739d-162a-4c84-922d-cda04382633f.gif" width="300" height="150"/> <img src="https://user-images.githubusercontent.com/34286328/171454176-ce08f6d0-3087-4ecc-9273-7d30d8f73f6d.gif" width="300" height="150"/>
<img src="https://user-images.githubusercontent.com/34286328/184170040-3f76f761-e748-452e-b8c8-3cc1c7c8cb98.gif" width="614" height="307"/>
## System Requirements
It is recommended to have at least 32GB RAM and a GPU with at least 12GB VRAM. For detailed system requirements, please visit the [Isaac Sim System Requirements](https://docs.omniverse.nvidia.com/isaacsim/latest/installation/requirements.html#system-requirements) page. Please refer to the [Troubleshooting](docs/troubleshoot.md#memory-consumption) page for a detailed breakdown of memory consumption.
## Installation
Follow the Isaac Sim [documentation](https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_workstation.html) to install the latest Isaac Sim release.
*Examples in this repository rely on features from the most recent Isaac Sim release. Please make sure to update any existing Isaac Sim build to the latest release version, 2023.1.1, to ensure examples work as expected.*
Once installed, this repository can be used as a python module, `omniisaacgymenvs`, with the python executable provided in Isaac Sim.
To install `omniisaacgymenvs`, first clone this repository:
```bash
git clone https://github.com/NVIDIA-Omniverse/OmniIsaacGymEnvs.git
```
Once cloned, locate the [python executable in Isaac Sim](https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_python.html). By default, this should be `python.sh`. We will refer to this path as `PYTHON_PATH`.
To set a `PYTHON_PATH` variable in the terminal that links to the python executable, we can run a command that resembles the following. Make sure to update the paths to your local path.
```
For Linux: alias PYTHON_PATH=~/.local/share/ov/pkg/isaac_sim-*/python.sh
For Windows: doskey PYTHON_PATH=C:\Users\user\AppData\Local\ov\pkg\isaac_sim-*\python.bat $*
For IsaacSim Docker: alias PYTHON_PATH=/isaac-sim/python.sh
```
Install `omniisaacgymenvs` as a python module for `PYTHON_PATH`:
```bash
PYTHON_PATH -m pip install -e .
```
The following error may appear during the initial installation. This error is harmless and can be ignored.
```
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
```
### Running the examples
*Note: All commands should be executed from `OmniIsaacGymEnvs/omniisaacgymenvs`.*
To train your first policy, run:
```bash
PYTHON_PATH scripts/rlgames_train.py task=Cartpole
```
An Isaac Sim app window should be launched. Once Isaac Sim initialization completes, the Cartpole scene will be constructed and simulation will start running automatically. The process will terminate once training finishes.
Note that by default, we show a Viewport window with rendering, which slows down training. You can choose to close the Viewport window during training for better performance. The Viewport window can be re-enabled by selecting `Window > Viewport` from the top menu bar.
To achieve maximum performance, launch training in `headless` mode as follows:
```bash
PYTHON_PATH scripts/rlgames_train.py task=Ant headless=True
```
#### A Note on the Startup Time of the Simulation
Some of the examples could take a few minutes to load because the startup time scales based on the number of environments. The startup time will continually
be optimized in future releases.
### Extension Workflow
The extension workflow provides a simple user interface for creating and launching RL tasks. To launch Isaac Sim for the extension workflow, run:
```bash
./<isaac_sim_root>/isaac-sim.gym.sh --ext-folder </parent/directory/to/OIGE>
```
Note: `isaac_sim_root` should be located in the same directory as `python.sh`.
The UI window can be activated from `Isaac Examples > RL Examples` by navigating the top menu bar.
For more details on the extension workflow, please refer to the [documentation](docs/framework/extension_workflow.md).
### Loading trained models // Checkpoints
Checkpoints are saved in the folder `runs/EXPERIMENT_NAME/nn` where `EXPERIMENT_NAME`
defaults to the task name, but can also be overridden via the `experiment` argument.
To load a trained checkpoint and continue training, use the `checkpoint` argument:
```bash
PYTHON_PATH scripts/rlgames_train.py task=Ant checkpoint=runs/Ant/nn/Ant.pth
```
To load a trained checkpoint and only perform inference (no training), pass `test=True`
as an argument, along with the checkpoint name. To avoid rendering overhead, you may
also want to run with fewer environments using `num_envs=64`:
```bash
PYTHON_PATH scripts/rlgames_train.py task=Ant checkpoint=runs/Ant/nn/Ant.pth test=True num_envs=64
```
Note that if there are special characters such as `[` or `=` in the checkpoint names,
you will need to escape them and put quotes around the string. For example,
`checkpoint="runs/Ant/nn/last_Antep\=501rew\[5981.31\].pth"`
We provide pre-trained checkpoints on the [Nucleus](https://docs.omniverse.nvidia.com/nucleus/latest/index.html) server under `Assets/Isaac/2023.1.1/Isaac/Samples/OmniIsaacGymEnvs/Checkpoints`. Run the following command
to launch inference with pre-trained checkpoint:
Localhost (To set up localhost, please refer to the [Isaac Sim installation guide](https://docs.omniverse.nvidia.com/isaacsim/latest/installation/install_workstation.html)):
```bash
PYTHON_PATH scripts/rlgames_train.py task=Ant checkpoint=omniverse://localhost/NVIDIA/Assets/Isaac/2023.1.1/Isaac/Samples/OmniIsaacGymEnvs/Checkpoints/ant.pth test=True num_envs=64
```
Production server:
```bash
PYTHON_PATH scripts/rlgames_train.py task=Ant checkpoint=http://omniverse-content-production.s3-us-west-2.amazonaws.com/Assets/Isaac/2023.1.1/Isaac/Samples/OmniIsaacGymEnvs/Checkpoints/ant.pth test=True num_envs=64
```
When running with a pre-trained checkpoint for the first time, we will automatically download the checkpoint file to `omniisaacgymenvs/checkpoints`. For subsequent runs, we will re-use the file that has already been downloaded, and will not overwrite existing checkpoints with the same name in the `checkpoints` folder.
## Runing from Docker
Latest Isaac Sim Docker image can be found on [NGC](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/isaac-sim). A utility script is provided at `docker/run_docker.sh` to help initialize this repository and launch the Isaac Sim docker container. The script can be run with:
```bash
./docker/run_docker.sh
```
Then, training can be launched from the container with:
```bash
/isaac-sim/python.sh scripts/rlgames_train.py headless=True task=Ant
```
To run the Isaac Sim docker with UI, use the following script:
```bash
./docker/run_docker_viewer.sh
```
Then, training can be launched from the container with:
```bash
/isaac-sim/python.sh scripts/rlgames_train.py task=Ant
```
To avoid re-installing OIGE each time a container is launched, we also provide a dockerfile that can be used to build an image with OIGE installed. To build the image, run:
```bash
docker build -t isaac-sim-oige -f docker/dockerfile .
```
Then, start a container with the built image:
```bash
./docker/run_dockerfile.sh
```
Then, training can be launched from the container with:
```bash
/isaac-sim/python.sh scripts/rlgames_train.py task=Ant headless=True
```
### Isaac Sim Automator
Cloud instances for AWS, Azure, or GCP can be setup using [IsaacSim Automator](https://github.com/NVIDIA-Omniverse/IsaacSim-Automator/tree/main#omniverse-isaac-gym).
## Livestream
OmniIsaacGymEnvs supports livestream through the [Omniverse Streaming Client](https://docs.omniverse.nvidia.com/app_streaming-client/app_streaming-client/overview.html). To enable this feature, add the commandline argument `enable_livestream=True`:
```bash
PYTHON_PATH scripts/rlgames_train.py task=Ant headless=True enable_livestream=True
```
Connect from the Omniverse Streaming Client once the SimulationApp has been created. Note that enabling livestream is equivalent to training with the viewer enabled, thus the speed of training/inferencing will decrease compared to running in headless mode.
## Training Scripts
All scripts provided in `omniisaacgymenvs/scripts` can be launched directly with `PYTHON_PATH`.
To test out a task without RL in the loop, run the random policy script with:
```bash
PYTHON_PATH scripts/random_policy.py task=Cartpole
```
This script will sample random actions from the action space and apply these actions to your task without running any RL policies. Simulation should start automatically after launching the script, and will run indefinitely until terminated.
To run a simple form of PPO from `rl_games`, use the single-threaded training script:
```bash
PYTHON_PATH scripts/rlgames_train.py task=Cartpole
```
This script creates an instance of the PPO runner in `rl_games` and automatically launches training and simulation. Once training completes (the total number of iterations have been reached), the script will exit. If running inference with `test=True checkpoint=<path/to/checkpoint>`, the script will run indefinitely until terminated. Note that this script will have limitations on interaction with the UI.
### Configuration and command line arguments
We use [Hydra](https://hydra.cc/docs/intro/) to manage the config.
Common arguments for the training scripts are:
* `task=TASK` - Selects which task to use. Any of `AllegroHand`, `Ant`, `Anymal`, `AnymalTerrain`, `BallBalance`, `Cartpole`, `CartpoleCamera`, `Crazyflie`, `FactoryTaskNutBoltPick`, `FactoryTaskNutBoltPlace`, `FactoryTaskNutBoltScrew`, `FrankaCabinet`, `FrankaDeformable`, `Humanoid`, `Ingenuity`, `Quadcopter`, `ShadowHand`, `ShadowHandOpenAI_FF`, `ShadowHandOpenAI_LSTM` (these correspond to the config for each environment in the folder `omniisaacgymenvs/cfg/task`)
* `train=TRAIN` - Selects which training config to use. Will automatically default to the correct config for the environment (ie. `<TASK>PPO`).
* `num_envs=NUM_ENVS` - Selects the number of environments to use (overriding the default number of environments set in the task config).
* `seed=SEED` - Sets a seed value for randomization, and overrides the default seed in the task config
* `pipeline=PIPELINE` - Which API pipeline to use. Defaults to `gpu`, can also set to `cpu`. When using the `gpu` pipeline, all data stays on the GPU. When using the `cpu` pipeline, simulation can run on either CPU or GPU, depending on the `sim_device` setting, but a copy of the data is always made on the CPU at every step.
* `sim_device=SIM_DEVICE` - Device used for physics simulation. Set to `gpu` (default) to use GPU and to `cpu` for CPU.
* `device_id=DEVICE_ID` - Device ID for GPU to use for simulation and task. Defaults to `0`. This parameter will only be used if simulation runs on GPU.
* `rl_device=RL_DEVICE` - Which device / ID to use for the RL algorithm. Defaults to `cuda:0`, and follows PyTorch-like device syntax.
* `multi_gpu=MULTI_GPU` - Whether to train using multiple GPUs. Defaults to `False`. Note that this option is only available with `rlgames_train.py`.
* `test=TEST`- If set to `True`, only runs inference on the policy and does not do any training.
* `checkpoint=CHECKPOINT_PATH` - Path to the checkpoint to load for training or testing.
* `headless=HEADLESS` - Whether to run in headless mode.
* `enable_livestream=ENABLE_LIVESTREAM` - Whether to enable Omniverse streaming.
* `experiment=EXPERIMENT` - Sets the name of the experiment.
* `max_iterations=MAX_ITERATIONS` - Sets how many iterations to run for. Reasonable defaults are provided for the provided environments.
* `warp=WARP` - If set to True, launch the task implemented with Warp backend (Note: not all tasks have a Warp implementation).
* `kit_app=KIT_APP` - Specifies the absolute path to the kit app file to be used.
Hydra also allows setting variables inside config files directly as command line arguments. As an example, to set the minibatch size for a rl_games training run, you can use `train.params.config.minibatch_size=64`. Similarly, variables in task configs can also be set. For example, `task.env.episodeLength=100`.
#### Hydra Notes
Default values for each of these are found in the `omniisaacgymenvs/cfg/config.yaml` file.
The way that the `task` and `train` portions of the config works are through the use of config groups.
You can learn more about how these work [here](https://hydra.cc/docs/tutorials/structured_config/config_groups/)
The actual configs for `task` are in `omniisaacgymenvs/cfg/task/<TASK>.yaml` and for `train` in `omniisaacgymenvs/cfg/train/<TASK>PPO.yaml`.
In some places in the config you will find other variables referenced (for example,
`num_actors: ${....task.env.numEnvs}`). Each `.` represents going one level up in the config hierarchy.
This is documented fully [here](https://omegaconf.readthedocs.io/en/latest/usage.html#variable-interpolation).
### Tensorboard
Tensorboard can be launched during training via the following command:
```bash
PYTHON_PATH -m tensorboard.main --logdir runs/EXPERIMENT_NAME/summaries
```
## WandB support
You can run (WandB)[https://wandb.ai/] with OmniIsaacGymEnvs by setting `wandb_activate=True` flag from the command line. You can set the group, name, entity, and project for the run by setting the `wandb_group`, `wandb_name`, `wandb_entity` and `wandb_project` arguments. Make sure you have WandB installed in the Isaac Sim Python executable with `PYTHON_PATH -m pip install wandb` before activating.
## Training with Multiple GPUs
To train with multiple GPUs, use the following command, where `--proc_per_node` represents the number of available GPUs:
```bash
PYTHON_PATH -m torch.distributed.run --nnodes=1 --nproc_per_node=2 scripts/rlgames_train.py headless=True task=Ant multi_gpu=True
```
## Multi-Node Training
To train across multiple nodes/machines, it is required to launch an individual process on each node.
For the master node, use the following command, where `--proc_per_node` represents the number of available GPUs, and `--nnodes` represents the number of nodes:
```bash
PYTHON_PATH -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=0 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=localhost:5555 scripts/rlgames_train.py headless=True task=Ant multi_gpu=True
```
Note that the port (`5555`) can be replaced with any other available port.
For non-master nodes, use the following command, replacing `--node_rank` with the index of each machine:
```bash
PYTHON_PATH -m torch.distributed.run --nproc_per_node=2 --nnodes=2 --node_rank=1 --rdzv_id=123 --rdzv_backend=c10d --rdzv_endpoint=ip_of_master_machine:5555 scripts/rlgames_train.py headless=True task=Ant multi_gpu=True
```
For more details on multi-node training with PyTorch, please visit [here](https://pytorch.org/tutorials/intermediate/ddp_series_multinode.html). As mentioned in the PyTorch documentation, "multinode training is bottlenecked by inter-node communication latencies". When this latency is high, it is possible multi-node training will perform worse than running on a single node instance.
## Tasks
Source code for tasks can be found in `omniisaacgymenvs/tasks`.
Each task follows the frameworks provided in `omni.isaac.core` and `omni.isaac.gym` in Isaac Sim.
Refer to [docs/framework/framework.md](docs/framework/framework.md) for how to create your own tasks.
Full details on each of the tasks available can be found in the [RL examples documentation](docs/examples/rl_examples.md).
## Demo
We provide an interactable demo based on the `AnymalTerrain` RL example. In this demo, you can click on any of
the ANYmals in the scene to go into third-person mode and manually control the robot with your keyboard as follows:
- `Up Arrow`: Forward linear velocity command
- `Down Arrow`: Backward linear velocity command
- `Left Arrow`: Leftward linear velocity command
- `Right Arrow`: Rightward linear velocity command
- `Z`: Counterclockwise yaw angular velocity command
- `X`: Clockwise yaw angular velocity command
- `C`: Toggles camera view between third-person and scene view while maintaining manual control
- `ESC`: Unselect a selected ANYmal and yields manual control
Launch this demo with the following command. Note that this demo limits the maximum number of ANYmals in the scene to 128.
```
PYTHON_PATH scripts/rlgames_demo.py task=AnymalTerrain num_envs=64 checkpoint=omniverse://localhost/NVIDIA/Assets/Isaac/2023.1.1/Isaac/Samples/OmniIsaacGymEnvs/Checkpoints/anymal_terrain.pth
```
<img src="https://user-images.githubusercontent.com/34286328/184688654-6e7899b2-5847-4184-8944-2a96b129b1ff.gif" width="600" height="300"/>
| 18,653 | Markdown | 55.871951 | 469 | 0.777408 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/config/extension.toml | [gym]
reloadable = true
[package]
version = "0.0.0"
category = "Simulation"
title = "Isaac Gym Envs"
description = "RL environments"
authors = ["Isaac Sim Team"]
repository = "https://gitlab-master.nvidia.com/carbon-gym/omniisaacgymenvs"
keywords = ["isaac"]
changelog = "docs/CHANGELOG.md"
readme = "docs/README.md"
icon = "data/icon.png"
writeTarget.kit = true
[dependencies]
"omni.isaac.gym" = {}
"omni.isaac.core" = {}
"omni.isaac.cloner" = {}
"omni.isaac.ml_archive" = {} # torch
[[python.module]]
name = "omniisaacgymenvs"
| 532 | TOML | 20.319999 | 75 | 0.693609 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/extension.py | # Copyright (c) 2018-2023, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
import asyncio
import inspect
import os
import traceback
import weakref
from abc import abstractmethod
import gym
import hydra
import omni.ext
import omni.timeline
import omni.ui as ui
import omni.usd
from hydra import compose, initialize
from omegaconf import OmegaConf
from omni.isaac.cloner import GridCloner
from omni.isaac.core.utils.extensions import disable_extension, enable_extension
from omni.isaac.core.utils.torch.maths import set_seed
from omni.isaac.core.utils.viewports import set_camera_view
from omni.isaac.core.world import World
import omniisaacgymenvs
from omniisaacgymenvs.envs.vec_env_rlgames_mt import VecEnvRLGamesMT
from omniisaacgymenvs.utils.config_utils.sim_config import SimConfig
from omniisaacgymenvs.utils.hydra_cfg.reformat import omegaconf_to_dict, print_dict
from omniisaacgymenvs.utils.rlgames.rlgames_train_mt import RLGTrainer, Trainer
from omniisaacgymenvs.utils.task_util import import_tasks, initialize_task
from omni.isaac.ui.callbacks import on_open_folder_clicked, on_open_IDE_clicked
from omni.isaac.ui.menu import make_menu_item_description
from omni.isaac.ui.ui_utils import (
btn_builder,
dropdown_builder,
get_style,
int_builder,
multi_btn_builder,
multi_cb_builder,
scrolling_frame_builder,
setup_ui_headers,
str_builder,
)
from omni.kit.menu.utils import MenuItemDescription, add_menu_items, remove_menu_items
from omni.kit.viewport.utility import get_active_viewport, get_viewport_from_window_name
from omni.kit.viewport.utility.camera_state import ViewportCameraState
from pxr import Gf
ext_instance = None
class RLExtension(omni.ext.IExt):
def on_startup(self, ext_id: str):
self._render_modes = ["Full render", "UI only", "None"]
self._env = None
self._task = None
self._ext_id = ext_id
ext_manager = omni.kit.app.get_app().get_extension_manager()
extension_path = ext_manager.get_extension_path(ext_id)
self._ext_path = os.path.dirname(extension_path) if os.path.isfile(extension_path) else extension_path
self._ext_file_path = os.path.abspath(__file__)
self._initialize_task_list()
self.start_extension(
"",
"",
"RL Examples",
"RL Examples",
"",
"A set of reinforcement learning examples.",
self._ext_file_path,
)
self._task_initialized = False
self._task_changed = False
self._is_training = False
self._render = True
self._resume = False
self._test = False
self._evaluate = False
self._checkpoint_path = ""
self._timeline = omni.timeline.get_timeline_interface()
self._viewport = get_active_viewport()
self._viewport.updates_enabled = True
global ext_instance
ext_instance = self
def _initialize_task_list(self):
self._task_map, _ = import_tasks()
self._task_list = list(self._task_map.keys())
self._task_list.sort()
self._task_list.remove("CartpoleCamera") # we cannot run camera-based training from extension workflow for now. it requires a specialized app file.
self._task_name = self._task_list[0]
self._parse_config(self._task_name)
self._update_task_file_paths(self._task_name)
def _update_task_file_paths(self, task):
self._task_file_path = os.path.abspath(inspect.getfile(self._task_map[task]))
self._task_cfg_file_path = os.path.join(os.path.dirname(self._ext_file_path), f"cfg/task/{task}.yaml")
self._train_cfg_file_path = os.path.join(os.path.dirname(self._ext_file_path), f"cfg/train/{task}PPO.yaml")
def _parse_config(self, task, num_envs=None, overrides=None):
hydra.core.global_hydra.GlobalHydra.instance().clear()
initialize(version_base=None, config_path="cfg")
overrides_list = [f"task={task}"]
if overrides is not None:
overrides_list += overrides
if num_envs is None:
self._cfg = compose(config_name="config", overrides=overrides_list)
else:
self._cfg = compose(config_name="config", overrides=overrides_list + [f"num_envs={num_envs}"])
self._cfg_dict = omegaconf_to_dict(self._cfg)
self._sim_config = SimConfig(self._cfg_dict)
def start_extension(
self,
menu_name: str,
submenu_name: str,
name: str,
title: str,
doc_link: str,
overview: str,
file_path: str,
number_of_extra_frames=1,
window_width=550,
keep_window_open=False,
):
window = ui.Workspace.get_window("Property")
if window:
window.visible = False
window = ui.Workspace.get_window("Render Settings")
if window:
window.visible = False
menu_items = [make_menu_item_description(self._ext_id, name, lambda a=weakref.proxy(self): a._menu_callback())]
if menu_name == "" or menu_name is None:
self._menu_items = menu_items
elif submenu_name == "" or submenu_name is None:
self._menu_items = [MenuItemDescription(name=menu_name, sub_menu=menu_items)]
else:
self._menu_items = [
MenuItemDescription(
name=menu_name, sub_menu=[MenuItemDescription(name=submenu_name, sub_menu=menu_items)]
)
]
add_menu_items(self._menu_items, "Isaac Examples")
self._task_dropdown = None
self._cbs = None
self._build_ui(
name=name,
title=title,
doc_link=doc_link,
overview=overview,
file_path=file_path,
number_of_extra_frames=number_of_extra_frames,
window_width=window_width,
keep_window_open=keep_window_open,
)
return
def _build_ui(
self, name, title, doc_link, overview, file_path, number_of_extra_frames, window_width, keep_window_open
):
self._window = omni.ui.Window(
name, width=window_width, height=0, visible=keep_window_open, dockPreference=ui.DockPreference.LEFT_BOTTOM
)
with self._window.frame:
self._main_stack = ui.VStack(spacing=5, height=0)
with self._main_stack:
setup_ui_headers(self._ext_id, file_path, title, doc_link, overview)
self._controls_frame = ui.CollapsableFrame(
title="World Controls",
width=ui.Fraction(1),
height=0,
collapsed=False,
style=get_style(),
horizontal_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_AS_NEEDED,
vertical_scrollbar_policy=ui.ScrollBarPolicy.SCROLLBAR_ALWAYS_ON,
)
with self._controls_frame:
with ui.VStack(style=get_style(), spacing=5, height=0):
with ui.HStack(style=get_style()):
with ui.VStack(style=get_style(), width=ui.Fraction(20)):
dict = {
"label": "Select Task",
"type": "dropdown",
"default_val": 0,
"items": self._task_list,
"tooltip": "Select a task",
"on_clicked_fn": self._on_task_select,
}
self._task_dropdown = dropdown_builder(**dict)
with ui.Frame(tooltip="Open Source Code"):
ui.Button(
name="IconButton",
width=20,
height=20,
clicked_fn=lambda: on_open_IDE_clicked(self._ext_path, self._task_file_path),
style=get_style()["IconButton.Image::OpenConfig"],
alignment=ui.Alignment.LEFT_CENTER,
tooltip="Open in IDE",
)
with ui.Frame(tooltip="Open Task Config"):
ui.Button(
name="IconButton",
width=20,
height=20,
clicked_fn=lambda: on_open_IDE_clicked(self._ext_path, self._task_cfg_file_path),
style=get_style()["IconButton.Image::OpenConfig"],
alignment=ui.Alignment.LEFT_CENTER,
tooltip="Open in IDE",
)
with ui.Frame(tooltip="Open Training Config"):
ui.Button(
name="IconButton",
width=20,
height=20,
clicked_fn=lambda: on_open_IDE_clicked(self._ext_path, self._train_cfg_file_path),
style=get_style()["IconButton.Image::OpenConfig"],
alignment=ui.Alignment.LEFT_CENTER,
tooltip="Open in IDE",
)
dict = {
"label": "Number of environments",
"tooltip": "Enter the number of environments to construct",
"min": 0,
"max": 8192,
"default_val": self._cfg.task.env.numEnvs,
}
self._num_envs_int = int_builder(**dict)
dict = {
"label": "Load Environment",
"type": "button",
"text": "Load",
"tooltip": "Load Environment and Task",
"on_clicked_fn": self._on_load_world,
}
self._load_env_button = btn_builder(**dict)
dict = {
"label": "Rendering Mode",
"type": "dropdown",
"default_val": 0,
"items": self._render_modes,
"tooltip": "Select a rendering mode",
"on_clicked_fn": self._on_render_mode_select,
}
self._render_dropdown = dropdown_builder(**dict)
dict = {
"label": "Configure Training",
"count": 3,
"text": ["Resume from Checkpoint", "Test", "Evaluate"],
"default_val": [False, False, False],
"tooltip": [
"",
"Resume training from checkpoint",
"Play a trained policy",
"Evaluate a policy during training",
],
"on_clicked_fn": [
self._on_resume_cb_update,
self._on_test_cb_update,
self._on_evaluate_cb_update,
],
}
self._cbs = multi_cb_builder(**dict)
dict = {
"label": "Load Checkpoint",
"tooltip": "Enter path to checkpoint file",
"on_clicked_fn": self._on_checkpoint_update,
}
self._checkpoint_str = str_builder(**dict)
dict = {
"label": "Train/Test",
"count": 2,
"text": ["Start", "Stop"],
"tooltip": [
"",
"Launch new training/inference run",
"Terminate current training/inference run",
],
"on_clicked_fn": [self._on_train, self._on_train_stop],
}
self._buttons = multi_btn_builder(**dict)
return
def create_task(self):
headless = self._cfg.headless
enable_viewport = "enable_cameras" in self._cfg.task.sim and self._cfg.task.sim.enable_cameras
self._env = VecEnvRLGamesMT(
headless=headless,
sim_device=self._cfg.device_id,
enable_livestream=self._cfg.enable_livestream,
enable_viewport=enable_viewport or self._cfg.enable_recording,
launch_simulation_app=False,
)
# parse experiment directory
module_path = os.path.abspath(os.path.join(os.path.dirname(omniisaacgymenvs.__file__)))
experiment_dir = os.path.join(module_path, "runs", self._cfg.train.params.config.name)
# use gym RecordVideo wrapper for viewport recording
if self._cfg.enable_recording:
if self._cfg.recording_dir == '':
videos_dir = os.path.join(experiment_dir, "videos")
else:
videos_dir = self._cfg.recording_dir
video_interval = lambda step: step % self._cfg.recording_interval == 0
video_length = self._cfg.recording_length
self._env.is_vector_env = True
if self._env.metadata is None:
self._env.metadata = {"render_modes": ["rgb_array"], "render_fps": self._cfg.recording_fps}
else:
self._env.metadata["render_modes"] = ["rgb_array"]
self._env.metadata["render_fps"] = self._cfg.recording_fps
self._env = gym.wrappers.RecordVideo(
self._env, video_folder=videos_dir, step_trigger=video_interval, video_length=video_length
)
self._task = initialize_task(self._cfg_dict, self._env, init_sim=False)
self._task_initialized = True
self._task.set_is_extension(True)
def _on_task_select(self, value):
if self._task_initialized and value != self._task_name:
self._task_changed = True
self._task_initialized = False
self._task_name = value
self._parse_config(self._task_name)
self._num_envs_int.set_value(self._cfg.task.env.numEnvs)
self._update_task_file_paths(self._task_name)
def _on_render_mode_select(self, value):
if value == self._render_modes[0]:
self._viewport.updates_enabled = True
window = ui.Workspace.get_window("Viewport")
window.visible = True
if self._env:
self._env.update_viewport = True
self._env.set_render_mode(0)
elif value == self._render_modes[1]:
self._viewport.updates_enabled = False
window = ui.Workspace.get_window("Viewport")
window.visible = False
if self._env:
self._env.update_viewport = False
self._env.set_render_mode(1)
elif value == self._render_modes[2]:
self._viewport.updates_enabled = False
window = ui.Workspace.get_window("Viewport")
window.visible = False
if self._env:
self._env.update_viewport = False
self._env.set_render_mode(2)
def _on_render_cb_update(self, value):
self._render = value
print("updates enabled", value)
self._viewport.updates_enabled = value
if self._env:
self._env.update_viewport = value
if value:
window = ui.Workspace.get_window("Viewport")
window.visible = True
else:
window = ui.Workspace.get_window("Viewport")
window.visible = False
def _on_single_env_cb_update(self, value):
visibility = "invisible" if value else "inherited"
stage = omni.usd.get_context().get_stage()
env_root = stage.GetPrimAtPath("/World/envs")
if env_root.IsValid():
for i, p in enumerate(env_root.GetChildren()):
p.GetAttribute("visibility").Set(visibility)
if value:
stage.GetPrimAtPath("/World/envs/env_0").GetAttribute("visibility").Set("inherited")
env_pos = self._task._env_pos[0].cpu().numpy().tolist()
camera_pos = [env_pos[0] + 10, env_pos[1] + 10, 3]
camera_target = [env_pos[0], env_pos[1], env_pos[2]]
else:
camera_pos = [10, 10, 3]
camera_target = [0, 0, 0]
camera_state = ViewportCameraState("/OmniverseKit_Persp", get_active_viewport())
camera_state.set_position_world(Gf.Vec3d(*camera_pos), True)
camera_state.set_target_world(Gf.Vec3d(*camera_target), True)
def _on_test_cb_update(self, value):
self._test = value
if value is True and self._checkpoint_path.strip() == "":
module_path = os.path.abspath(os.path.join(os.path.dirname(omniisaacgymenvs.__file__)))
self._checkpoint_str.set_value(os.path.join(module_path, f"runs/{self._task_name}/nn/{self._task_name}.pth"))
def _on_resume_cb_update(self, value):
self._resume = value
if value is True and self._checkpoint_path.strip() == "":
module_path = os.path.abspath(os.path.join(os.path.dirname(omniisaacgymenvs.__file__)))
self._checkpoint_str.set_value(os.path.join(module_path, f"runs/{self._task_name}/nn/{self._task_name}.pth"))
def _on_evaluate_cb_update(self, value):
self._evaluate = value
def _on_checkpoint_update(self, value):
self._checkpoint_path = value.get_value_as_string()
async def _on_load_world_async(self, use_existing_stage):
# initialize task if not initialized
if not self._task_initialized or not omni.usd.get_context().get_stage().GetPrimAtPath("/World/envs").IsValid():
self._parse_config(task=self._task_name, num_envs=self._num_envs_int.get_value_as_int())
self.create_task()
else:
# update config
self._parse_config(task=self._task_name, num_envs=self._num_envs_int.get_value_as_int())
self._task.update_config(self._sim_config)
# clear scene
# self._env.world.scene.clear()
self._env.world._sim_params = self._sim_config.get_physics_params()
await self._env.world.initialize_simulation_context_async()
set_camera_view(eye=[10, 10, 3], target=[0, 0, 0], camera_prim_path="/OmniverseKit_Persp")
if not use_existing_stage:
# clear scene
self._env.world.scene.clear()
# clear environments added to world
omni.usd.get_context().get_stage().RemovePrim("/World/collisions")
omni.usd.get_context().get_stage().RemovePrim("/World/envs")
# create scene
await self._env.world.reset_async_set_up_scene()
# update num_envs in envs
self._env.update_task_params()
else:
self._task.initialize_views(self._env.world.scene)
def _on_load_world(self):
# stop simulation before updating stage
self._timeline.stop()
asyncio.ensure_future(self._on_load_world_async(use_existing_stage=False))
def _on_train_stop(self):
if self._task_initialized:
asyncio.ensure_future(self._env.world.stop_async())
async def _on_train_async(self, overrides=None):
try:
# initialize task if not initialized
print("task initialized:", self._task_initialized)
if not self._task_initialized:
# if this is the first launch of the extension, we do not want to re-create stage if stage already exists
use_existing_stage = False
if omni.usd.get_context().get_stage().GetPrimAtPath("/World/envs").IsValid():
use_existing_stage = True
print(use_existing_stage)
await self._on_load_world_async(use_existing_stage)
# update config
self._parse_config(task=self._task_name, num_envs=self._num_envs_int.get_value_as_int(), overrides=overrides)
sim_config = SimConfig(self._cfg_dict)
self._task.update_config(sim_config)
cfg_dict = omegaconf_to_dict(self._cfg)
# sets seed. if seed is -1 will pick a random one
self._cfg.seed = set_seed(self._cfg.seed, torch_deterministic=self._cfg.torch_deterministic)
cfg_dict["seed"] = self._cfg.seed
self._checkpoint_path = self._checkpoint_str.get_value_as_string()
if self._resume or self._test:
self._cfg.checkpoint = self._checkpoint_path
self._cfg.test = self._test
self._cfg.evaluation = self._evaluate
cfg_dict["checkpoint"] = self._cfg.checkpoint
cfg_dict["test"] = self._cfg.test
cfg_dict["evaluation"] = self._cfg.evaluation
rlg_trainer = RLGTrainer(self._cfg, cfg_dict)
if not rlg_trainer._bad_checkpoint:
trainer = Trainer(rlg_trainer, self._env)
await self._env.world.reset_async_no_set_up_scene()
# this is needed to enable rendering for viewport recording
for _ in range(5):
await self._env.world.render_async()
self._env.set_render_mode(self._render_dropdown.get_item_value_model().as_int)
await self._env.run(trainer)
await omni.kit.app.get_app().next_update_async()
except Exception as e:
print(traceback.format_exc())
finally:
self._is_training = False
if self._task._dr_randomizer.randomize:
await self._task._dr_randomizer.rep.orchestrator.stop_async()
self._task._dr_randomizer.rep.orchestrator._orchestrator.shutdown()
def _on_train(self):
# stop simulation if still running
self._timeline.stop()
self._on_render_mode_select(self._render_modes[self._render_dropdown.get_item_value_model().as_int])
if not self._is_training:
self._is_training = True
asyncio.ensure_future(self._on_train_async())
return
def _menu_callback(self):
self._window.visible = not self._window.visible
return
def _on_window(self, status):
return
def on_shutdown(self):
self._extra_frames = []
if self._menu_items is not None:
self._sample_window_cleanup()
self.shutdown_cleanup()
global ext_instance
ext_instance = None
return
def shutdown_cleanup(self):
return
def _sample_window_cleanup(self):
remove_menu_items(self._menu_items, "Isaac Examples")
self._window = None
self._menu_items = None
self._buttons = None
self._load_env_button = None
self._task_dropdown = None
self._cbs = None
self._checkpoint_str = None
return
def get_instance():
return ext_instance
| 24,151 | Python | 43.234432 | 155 | 0.539646 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/__init__.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import traceback
try:
from .extension import RLExtension, get_instance
# import omniisaacgymenvs.tests
except Exception as e:
pass
# print(e)
# print(traceback.format_exc())
| 1,753 | Python | 46.405404 | 80 | 0.775242 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/envs/vec_env_rlgames_mt.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import torch
from omni.isaac.gym.vec_env import TaskStopException, VecEnvMT
from .vec_env_rlgames import VecEnvRLGames
# VecEnv Wrapper for RL training
class VecEnvRLGamesMT(VecEnvRLGames, VecEnvMT):
def _parse_data(self, data):
self._obs = data["obs"]
self._rew = data["rew"].to(self._task.rl_device)
self._states = torch.clamp(data["states"], -self._task.clip_obs, self._task.clip_obs).to(self._task.rl_device)
self._resets = data["reset"].to(self._task.rl_device)
self._extras = data["extras"]
def step(self, actions):
if self._stop:
raise TaskStopException()
if self._task.randomize_actions:
actions = self._task._dr_randomizer.apply_actions_randomization(
actions=actions, reset_buf=self._task.reset_buf
)
actions = torch.clamp(actions, -self._task.clip_actions, self._task.clip_actions).to(self._task.device)
self.send_actions(actions)
data = self.get_data()
if self._task.randomize_observations:
self._obs = self._task._dr_randomizer.apply_observations_randomization(
observations=self._obs.to(self._task.rl_device), reset_buf=self._task.reset_buf
)
self._obs = torch.clamp(self._obs, -self._task.clip_obs, self._task.clip_obs).to(self._task.rl_device)
obs_dict = {}
obs_dict["obs"] = self._obs
obs_dict["states"] = self._states
return obs_dict, self._rew, self._resets, self._extras
| 3,109 | Python | 42.194444 | 118 | 0.705693 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/envs/vec_env_rlgames.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from datetime import datetime
import numpy as np
import torch
from omni.isaac.gym.vec_env import VecEnvBase
# VecEnv Wrapper for RL training
class VecEnvRLGames(VecEnvBase):
def _process_data(self):
self._obs = torch.clamp(self._obs, -self._task.clip_obs, self._task.clip_obs).to(self._task.rl_device)
self._rew = self._rew.to(self._task.rl_device)
self._states = torch.clamp(self._states, -self._task.clip_obs, self._task.clip_obs).to(self._task.rl_device)
self._resets = self._resets.to(self._task.rl_device)
self._extras = self._extras
def set_task(self, task, backend="numpy", sim_params=None, init_sim=True, rendering_dt=1.0 / 60.0) -> None:
super().set_task(task, backend, sim_params, init_sim, rendering_dt)
self.num_states = self._task.num_states
self.state_space = self._task.state_space
def step(self, actions):
# only enable rendering when we are recording, or if the task already has it enabled
to_render = self._render
if self._record:
if not hasattr(self, "step_count"):
self.step_count = 0
if self.step_count % self._task.cfg["recording_interval"] == 0:
self.is_recording = True
self.record_length = 0
if self.is_recording:
self.record_length += 1
if self.record_length > self._task.cfg["recording_length"]:
self.is_recording = False
if self.is_recording:
to_render = True
else:
if (self._task.cfg["headless"] and not self._task.enable_cameras and not self._task.cfg["enable_livestream"]):
to_render = False
self.step_count += 1
if self._task.randomize_actions:
actions = self._task._dr_randomizer.apply_actions_randomization(
actions=actions, reset_buf=self._task.reset_buf
)
actions = torch.clamp(actions, -self._task.clip_actions, self._task.clip_actions).to(self._task.device)
self._task.pre_physics_step(actions)
if (self.sim_frame_count + self._task.control_frequency_inv) % self._task.rendering_interval == 0:
for _ in range(self._task.control_frequency_inv - 1):
self._world.step(render=False)
self.sim_frame_count += 1
self._world.step(render=to_render)
self.sim_frame_count += 1
else:
for _ in range(self._task.control_frequency_inv):
self._world.step(render=False)
self.sim_frame_count += 1
self._obs, self._rew, self._resets, self._extras = self._task.post_physics_step()
if self._task.randomize_observations:
self._obs = self._task._dr_randomizer.apply_observations_randomization(
observations=self._obs.to(device=self._task.rl_device), reset_buf=self._task.reset_buf
)
self._states = self._task.get_states()
self._process_data()
obs_dict = {"obs": self._obs, "states": self._states}
return obs_dict, self._rew, self._resets, self._extras
def reset(self, seed=None, options=None):
"""Resets the task and applies default zero actions to recompute observations and states."""
now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
print(f"[{now}] Running RL reset")
self._task.reset()
actions = torch.zeros((self.num_envs, self._task.num_actions), device=self._task.rl_device)
obs_dict, _, _, _ = self.step(actions)
return obs_dict
| 5,196 | Python | 43.801724 | 126 | 0.65127 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/allegro_hand.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.torch import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.allegro_hand import AllegroHand
from omniisaacgymenvs.robots.articulations.views.allegro_hand_view import AllegroHandView
from omniisaacgymenvs.tasks.shared.in_hand_manipulation import InHandManipulationTask
class AllegroHandTask(InHandManipulationTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
InHandManipulationTask.__init__(self, name=name, env=env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self.object_type = self._task_cfg["env"]["objectType"]
assert self.object_type in ["block"]
self.obs_type = self._task_cfg["env"]["observationType"]
if not (self.obs_type in ["full_no_vel", "full"]):
raise Exception("Unknown type of observations!\nobservationType should be one of: [full_no_vel, full]")
print("Obs type:", self.obs_type)
self.num_obs_dict = {
"full_no_vel": 50,
"full": 72,
}
self.object_scale = torch.tensor([1.0, 1.0, 1.0])
self._num_observations = self.num_obs_dict[self.obs_type]
self._num_actions = 16
self._num_states = 0
InHandManipulationTask.update_config(self)
def get_starting_positions(self):
self.hand_start_translation = torch.tensor([0.0, 0.0, 0.5], device=self.device)
self.hand_start_orientation = torch.tensor([0.257551, 0.283045, 0.683330, -0.621782], device=self.device)
self.pose_dy, self.pose_dz = -0.2, 0.06
def get_hand(self):
allegro_hand = AllegroHand(
prim_path=self.default_zero_env_path + "/allegro_hand",
name="allegro_hand",
translation=self.hand_start_translation,
orientation=self.hand_start_orientation,
)
self._sim_config.apply_articulation_settings(
"allegro_hand",
get_prim_at_path(allegro_hand.prim_path),
self._sim_config.parse_actor_config("allegro_hand"),
)
allegro_hand_prim = self._stage.GetPrimAtPath(allegro_hand.prim_path)
allegro_hand.set_allegro_hand_properties(stage=self._stage, allegro_hand_prim=allegro_hand_prim)
allegro_hand.set_motor_control_mode(
stage=self._stage, allegro_hand_path=self.default_zero_env_path + "/allegro_hand"
)
def get_hand_view(self, scene):
return AllegroHandView(prim_paths_expr="/World/envs/.*/allegro_hand", name="allegro_hand_view")
def get_observations(self):
self.get_object_goal_observations()
self.hand_dof_pos = self._hands.get_joint_positions(clone=False)
self.hand_dof_vel = self._hands.get_joint_velocities(clone=False)
if self.obs_type == "full_no_vel":
self.compute_full_observations(True)
elif self.obs_type == "full":
self.compute_full_observations()
else:
print("Unkown observations type!")
observations = {self._hands.name: {"obs_buf": self.obs_buf}}
return observations
def compute_full_observations(self, no_vel=False):
if no_vel:
self.obs_buf[:, 0 : self.num_hand_dofs] = unscale(
self.hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits
)
self.obs_buf[:, 16:19] = self.object_pos
self.obs_buf[:, 19:23] = self.object_rot
self.obs_buf[:, 23:26] = self.goal_pos
self.obs_buf[:, 26:30] = self.goal_rot
self.obs_buf[:, 30:34] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.obs_buf[:, 34:50] = self.actions
else:
self.obs_buf[:, 0 : self.num_hand_dofs] = unscale(
self.hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits
)
self.obs_buf[:, self.num_hand_dofs : 2 * self.num_hand_dofs] = self.vel_obs_scale * self.hand_dof_vel
self.obs_buf[:, 32:35] = self.object_pos
self.obs_buf[:, 35:39] = self.object_rot
self.obs_buf[:, 39:42] = self.object_linvel
self.obs_buf[:, 42:45] = self.vel_obs_scale * self.object_angvel
self.obs_buf[:, 45:48] = self.goal_pos
self.obs_buf[:, 48:52] = self.goal_rot
self.obs_buf[:, 52:56] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.obs_buf[:, 56:72] = self.actions
| 6,329 | Python | 42.655172 | 115 | 0.658872 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/guarddog.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.stage import get_current_stage
from omni.isaac.core.utils.torch.rotations import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.guarddog import Guarddog
from omniisaacgymenvs.robots.articulations.views.guarddog_view import GuarddogView
from omniisaacgymenvs.tasks.utils.usd_utils import set_drive
class GuarddogTask(RLTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self._max_episode_length = 500
self._num_observations = 48
self._num_actions = 12
RLTask.__init__(self, name, env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
# normalization
self.lin_vel_scale = self._task_cfg["env"]["learn"]["linearVelocityScale"]
self.ang_vel_scale = self._task_cfg["env"]["learn"]["angularVelocityScale"]
self.dof_pos_scale = self._task_cfg["env"]["learn"]["dofPositionScale"]
self.dof_vel_scale = self._task_cfg["env"]["learn"]["dofVelocityScale"]
self.action_scale = self._task_cfg["env"]["control"]["actionScale"]
# reward scales
self.rew_scales = {}
self.rew_scales["lin_vel_xy"] = self._task_cfg["env"]["learn"]["linearVelocityXYRewardScale"]
self.rew_scales["ang_vel_z"] = self._task_cfg["env"]["learn"]["angularVelocityZRewardScale"]
self.rew_scales["lin_vel_z"] = self._task_cfg["env"]["learn"]["linearVelocityZRewardScale"]
self.rew_scales["joint_acc"] = self._task_cfg["env"]["learn"]["jointAccRewardScale"]
self.rew_scales["action_rate"] = self._task_cfg["env"]["learn"]["actionRateRewardScale"]
self.rew_scales["cosmetic"] = self._task_cfg["env"]["learn"]["cosmeticRewardScale"]
# command ranges
self.command_x_range = self._task_cfg["env"]["randomCommandVelocityRanges"]["linear_x"]
self.command_y_range = self._task_cfg["env"]["randomCommandVelocityRanges"]["linear_y"]
self.command_yaw_range = self._task_cfg["env"]["randomCommandVelocityRanges"]["yaw"]
# base init state
pos = self._task_cfg["env"]["baseInitState"]["pos"]
rot = self._task_cfg["env"]["baseInitState"]["rot"]
v_lin = self._task_cfg["env"]["baseInitState"]["vLinear"]
v_ang = self._task_cfg["env"]["baseInitState"]["vAngular"]
state = pos + rot + v_lin + v_ang
self.base_init_state = state
# default joint positions
self.named_default_joint_angles = self._task_cfg["env"]["defaultJointAngles"]
# other
self.dt = 1 / 60
self.max_episode_length_s = self._task_cfg["env"]["learn"]["episodeLength_s"]
self.max_episode_length = int(self.max_episode_length_s / self.dt + 0.5)
self.Kp = self._task_cfg["env"]["control"]["stiffness"]
self.Kd = self._task_cfg["env"]["control"]["damping"]
for key in self.rew_scales.keys():
self.rew_scales[key] *= self.dt
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._guarddog_translation = torch.tensor([0.0, 0.0, 0.62])
self._env_spacing = self._task_cfg["env"]["envSpacing"]
def set_up_scene(self, scene) -> None:
self._stage = get_current_stage()
self.get_guarddog()
super().set_up_scene(scene)
self._guarddogs = GuarddogView(
prim_paths_expr="/World/envs/.*/Guarddog/Body",
name="guarddog_view",
track_contact_forces=True
)
scene.add(self._guarddogs)
return
def initialize_views(self, scene):
super().initialize_views(scene)
if scene.object_exists("guarddog_view"):
scene.remove_object("guarddog_view", registry_only=True)
self._guarddogs = GuarddogView(
prim_paths_expr="/World/envs/.*/Guarddog/Body",
name="guarddog_view",
track_contact_forces=True
)
scene.add(self._guarddogs)
def get_guarddog(self):
guarddog = Guarddog(
prim_path=self.default_zero_env_path + "/Guarddog", name="Guarddog", translation=self._guarddog_translation
)
self._sim_config.apply_articulation_settings(
"Guarddog", get_prim_at_path(guarddog.prim_path), self._sim_config.parse_actor_config("Guarddog")
)
# guarddog.set_guarddog_properties(self._stage, guarddog.prim)
# guarddog.prepare_contacts(self._stage, guarddog.prim)
# Configure joint properties
joint_paths = []
for quadrant in ["FL", "BL", "FR", "BL"]:
for component, abbrev in [("Hip", "J2"), ("Thigh", "J3")]:
joint_paths.append(f"{quadrant}_{component}/{quadrant}_{abbrev}")
joint_paths.append(f"Body/{quadrant}_J1")
for joint_path in joint_paths:
# print(f"{guarddog.prim_path}/{joint_path}")
set_drive(f"{guarddog.prim_path}/{joint_path}", "angular", "position", 0, 400, 40, 2)
def get_observations(self) -> dict:
torso_position, torso_rotation = self._guarddogs.get_world_poses(clone=False)
root_velocities = self._guarddogs.get_velocities(clone=False)
dof_pos = self._guarddogs.get_joint_positions(clone=False)
dof_vel = self._guarddogs.get_joint_velocities(clone=False)
velocity = root_velocities[:, 0:3]
ang_velocity = root_velocities[:, 3:6]
base_lin_vel = quat_rotate_inverse(torso_rotation, velocity) * self.lin_vel_scale
base_ang_vel = quat_rotate_inverse(torso_rotation, ang_velocity) * self.ang_vel_scale
projected_gravity = quat_rotate(torso_rotation, self.gravity_vec)
dof_pos_scaled = (dof_pos - self.default_dof_pos) * self.dof_pos_scale
commands_scaled = self.commands * torch.tensor(
[self.lin_vel_scale, self.lin_vel_scale, self.ang_vel_scale],
requires_grad=False,
device=self.commands.device,
)
obs = torch.cat(
(
base_lin_vel,
base_ang_vel,
projected_gravity,
commands_scaled,
dof_pos_scaled,
dof_vel * self.dof_vel_scale,
self.actions,
),
dim=-1,
)
self.obs_buf[:] = obs
# print(self.obs_buf)
# print(commands_scaled[0,...])
observations = {self._guarddogs.name: {"obs_buf": self.obs_buf}}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
indices = torch.arange(self._guarddogs.count, dtype=torch.int32, device=self._device)
self.actions[:] = actions.clone().to(self._device)
current_targets = self.current_targets + self.action_scale * self.actions * self.dt
self.current_targets[:] = tensor_clamp(
current_targets, self.guarddog_dof_lower_limits, self.guarddog_dof_upper_limits
)
self._guarddogs.set_joint_position_targets(self.current_targets, indices)
def reset_idx(self, env_ids):
num_resets = len(env_ids)
# randomize DOF velocities
velocities = torch_rand_float(-0.1, 0.1, (num_resets, self._guarddogs.num_dof), device=self._device)
dof_pos = self.default_dof_pos[env_ids]
dof_vel = velocities
self.current_targets[env_ids] = dof_pos[:]
root_vel = torch.zeros((num_resets, 6), device=self._device)
# apply resets
indices = env_ids.to(dtype=torch.int32)
self._guarddogs.set_joint_positions(dof_pos, indices)
self._guarddogs.set_joint_velocities(dof_vel, indices)
self._guarddogs.set_world_poses(
self.initial_root_pos[env_ids].clone(), self.initial_root_rot[env_ids].clone(), indices
)
self._guarddogs.set_velocities(root_vel, indices)
self.commands_x[env_ids] = torch_rand_float(
self.command_x_range[0], self.command_x_range[1], (num_resets, 1), device=self._device
).squeeze()
self.commands_y[env_ids] = torch_rand_float(
self.command_y_range[0], self.command_y_range[1], (num_resets, 1), device=self._device
).squeeze()
self.commands_yaw[env_ids] = torch_rand_float(
self.command_yaw_range[0], self.command_yaw_range[1], (num_resets, 1), device=self._device
).squeeze()
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
self.last_actions[env_ids] = 0.0
self.last_dof_vel[env_ids] = 0.0
def post_reset(self):
self.default_dof_pos = torch.zeros(
(self.num_envs, 12), dtype=torch.float, device=self.device, requires_grad=False
)
dof_names = self._guarddogs.dof_names
for i in range(self.num_actions):
name = dof_names[i]
angle = self.named_default_joint_angles[name]
self.default_dof_pos[:, i] = angle
self.initial_root_pos, self.initial_root_rot = self._guarddogs.get_world_poses()
self.current_targets = self.default_dof_pos.clone()
dof_limits = self._guarddogs.get_dof_limits()
self.guarddog_dof_lower_limits = dof_limits[0, :, 0].to(device=self._device)
self.guarddog_dof_upper_limits = dof_limits[0, :, 1].to(device=self._device)
self.commands = torch.zeros(self._num_envs, 3, dtype=torch.float, device=self._device, requires_grad=False)
self.commands_y = self.commands.view(self._num_envs, 3)[..., 1]
self.commands_x = self.commands.view(self._num_envs, 3)[..., 0]
self.commands_yaw = self.commands.view(self._num_envs, 3)[..., 2]
# initialize some data used later on
self.extras = {}
self.gravity_vec = torch.tensor([0.0, 0.0, -1.0], device=self._device).repeat((self._num_envs, 1))
self.actions = torch.zeros(
self._num_envs, self.num_actions, dtype=torch.float, device=self._device, requires_grad=False
)
self.last_dof_vel = torch.zeros(
(self._num_envs, 12), dtype=torch.float, device=self._device, requires_grad=False
)
self.last_actions = torch.zeros(
self._num_envs, self.num_actions, dtype=torch.float, device=self._device, requires_grad=False
)
self.time_out_buf = torch.zeros_like(self.reset_buf)
# randomize all envs
indices = torch.arange(self._guarddogs.count, dtype=torch.int64, device=self._device)
self.reset_idx(indices)
def calculate_metrics(self) -> None:
torso_position, torso_rotation = self._guarddogs.get_world_poses(clone=False)
root_velocities = self._guarddogs.get_velocities(clone=False)
dof_pos = self._guarddogs.get_joint_positions(clone=False)
dof_vel = self._guarddogs.get_joint_velocities(clone=False)
velocity = root_velocities[:, 0:3]
ang_velocity = root_velocities[:, 3:6]
base_lin_vel = quat_rotate_inverse(torso_rotation, velocity)
base_ang_vel = quat_rotate_inverse(torso_rotation, ang_velocity)
# velocity tracking reward
lin_vel_error = torch.sum(torch.square(self.commands[:, :2] - base_lin_vel[:, :2]), dim=1)
ang_vel_error = torch.square(self.commands[:, 2] - base_ang_vel[:, 2])
rew_lin_vel_xy = torch.exp(-lin_vel_error / 0.25) * self.rew_scales["lin_vel_xy"]
rew_ang_vel_z = torch.exp(-ang_vel_error / 0.25) * self.rew_scales["ang_vel_z"]
rew_lin_vel_z = torch.square(base_lin_vel[:, 2]) * self.rew_scales["lin_vel_z"]
rew_joint_acc = torch.sum(torch.square(self.last_dof_vel - dof_vel), dim=1) * self.rew_scales["joint_acc"]
rew_action_rate = (
torch.sum(torch.square(self.last_actions - self.actions), dim=1) * self.rew_scales["action_rate"]
)
rew_cosmetic = (
torch.sum(torch.abs(dof_pos[:, 0:4] - self.default_dof_pos[:, 0:4]), dim=1) * self.rew_scales["cosmetic"]
)
# total_reward = rew_lin_vel_xy + rew_ang_vel_z + rew_joint_acc + rew_action_rate + rew_cosmetic + rew_lin_vel_z
total_reward = rew_lin_vel_xy + rew_ang_vel_z
total_reward = torch.clip(total_reward, 0.0, None)
self.last_actions[:] = self.actions[:]
self.last_dof_vel[:] = dof_vel[:]
self.fallen_over = self._guarddogs.is_base_below_threshold(threshold=0.3, ground_heights=0.0)
total_reward[torch.nonzero(self.fallen_over)] = -1
self.rew_buf[:] = total_reward.detach()
def is_done(self) -> None:
# reset agents
time_out = self.progress_buf >= self.max_episode_length - 1
self.reset_buf[:] = time_out | self.fallen_over
# print(self._guarddogs._base.get_net_contact_forces(clone=False))
# print(self._guarddogs._knees.get_net_contact_forces(clone=False))
# knee_contact = (
# torch.norm(self._guarddogs._knees.get_net_contact_forces(clone=False).view(self._num_envs, 4, 3), dim=-1)
# > 1.0
# )
# self.has_fallen = (torch.norm(self._guarddogs._base.get_net_contact_forces(clone=False), dim=1) > 1.0) | (
# torch.sum(knee_contact, dim=-1) > 1.0
# )
# self.reset_buf[:] = time_out | self.fallen_over | knee_contact
| 15,294 | Python | 44.520833 | 120 | 0.627501 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/ball_balance.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.objects import DynamicSphere
from omni.isaac.core.prims import RigidPrim, RigidPrimView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.stage import get_current_stage
from omni.isaac.core.utils.torch.maths import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.balance_bot import BalanceBot
from pxr import PhysxSchema
class BallBalanceTask(RLTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self._num_observations = 12 + 12
self._num_actions = 3
self.anchored = False
RLTask.__init__(self, name, env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._dt = self._task_cfg["sim"]["dt"]
self._table_position = torch.tensor([0, 0, 0.56])
self._ball_position = torch.tensor([0.0, 0.0, 1.0])
self._ball_radius = 0.1
self._action_speed_scale = self._task_cfg["env"]["actionSpeedScale"]
self._max_episode_length = self._task_cfg["env"]["maxEpisodeLength"]
def set_up_scene(self, scene) -> None:
self.get_balance_table()
self.add_ball()
super().set_up_scene(scene, replicate_physics=False)
self.set_up_table_anchors()
self._balance_bots = ArticulationView(
prim_paths_expr="/World/envs/.*/BalanceBot/tray", name="balance_bot_view", reset_xform_properties=False
)
scene.add(self._balance_bots)
self._balls = RigidPrimView(
prim_paths_expr="/World/envs/.*/Ball/ball", name="ball_view", reset_xform_properties=False
)
scene.add(self._balls)
return
def initialize_views(self, scene):
super().initialize_views(scene)
if scene.object_exists("balance_bot_view"):
scene.remove_object("balance_bot_view", registry_only=True)
if scene.object_exists("ball_view"):
scene.remove_object("ball_view", registry_only=True)
self._balance_bots = ArticulationView(
prim_paths_expr="/World/envs/.*/BalanceBot/tray", name="balance_bot_view", reset_xform_properties=False
)
scene.add(self._balance_bots)
self._balls = RigidPrimView(
prim_paths_expr="/World/envs/.*/Ball/ball", name="ball_view", reset_xform_properties=False
)
scene.add(self._balls)
def get_balance_table(self):
balance_table = BalanceBot(
prim_path=self.default_zero_env_path + "/BalanceBot", name="BalanceBot", translation=self._table_position
)
self._sim_config.apply_articulation_settings(
"table", get_prim_at_path(balance_table.prim_path), self._sim_config.parse_actor_config("table")
)
def add_ball(self):
ball = DynamicSphere(
prim_path=self.default_zero_env_path + "/Ball/ball",
translation=self._ball_position,
name="ball_0",
radius=self._ball_radius,
color=torch.tensor([0.9, 0.6, 0.2]),
)
self._sim_config.apply_articulation_settings(
"ball", get_prim_at_path(ball.prim_path), self._sim_config.parse_actor_config("ball")
)
def set_up_table_anchors(self):
from pxr import Gf
height = 0.08
stage = get_current_stage()
for i in range(self._num_envs):
base_path = f"{self.default_base_env_path}/env_{i}/BalanceBot"
for j, leg_offset in enumerate([(0.4, 0, height), (-0.2, 0.34641, 0), (-0.2, -0.34641, 0)]):
# fix the legs to ground
leg_path = f"{base_path}/lower_leg{j}"
ground_joint_path = leg_path + "_ground"
env_pos = stage.GetPrimAtPath(f"{self.default_base_env_path}/env_{i}").GetAttribute("xformOp:translate").Get()
anchor_pos = env_pos + Gf.Vec3d(*leg_offset)
self.fix_to_ground(stage, ground_joint_path, leg_path, anchor_pos)
def fix_to_ground(self, stage, joint_path, prim_path, anchor_pos):
from pxr import UsdPhysics, Gf
# D6 fixed joint
d6FixedJoint = UsdPhysics.Joint.Define(stage, joint_path)
d6FixedJoint.CreateBody0Rel().SetTargets(["/World/defaultGroundPlane"])
d6FixedJoint.CreateBody1Rel().SetTargets([prim_path])
d6FixedJoint.CreateLocalPos0Attr().Set(anchor_pos)
d6FixedJoint.CreateLocalRot0Attr().Set(Gf.Quatf(1.0, Gf.Vec3f(0, 0, 0)))
d6FixedJoint.CreateLocalPos1Attr().Set(Gf.Vec3f(0, 0, 0.18))
d6FixedJoint.CreateLocalRot1Attr().Set(Gf.Quatf(1.0, Gf.Vec3f(0, 0, 0)))
# lock all DOF (lock - low is greater than high)
d6Prim = stage.GetPrimAtPath(joint_path)
limitAPI = UsdPhysics.LimitAPI.Apply(d6Prim, "transX")
limitAPI.CreateLowAttr(1.0)
limitAPI.CreateHighAttr(-1.0)
limitAPI = UsdPhysics.LimitAPI.Apply(d6Prim, "transY")
limitAPI.CreateLowAttr(1.0)
limitAPI.CreateHighAttr(-1.0)
limitAPI = UsdPhysics.LimitAPI.Apply(d6Prim, "transZ")
limitAPI.CreateLowAttr(1.0)
limitAPI.CreateHighAttr(-1.0)
def get_observations(self) -> dict:
ball_positions, ball_orientations = self._balls.get_world_poses(clone=False)
ball_positions = ball_positions[:, 0:3] - self._env_pos
ball_velocities = self._balls.get_velocities(clone=False)
ball_linvels = ball_velocities[:, 0:3]
ball_angvels = ball_velocities[:, 3:6]
dof_pos = self._balance_bots.get_joint_positions(clone=False)
dof_vel = self._balance_bots.get_joint_velocities(clone=False)
sensor_force_torques = self._balance_bots.get_measured_joint_forces(joint_indices=self._sensor_indices) # (num_envs, num_sensors, 6)
self.obs_buf[..., 0:3] = dof_pos[..., self.actuated_dof_indices]
self.obs_buf[..., 3:6] = dof_vel[..., self.actuated_dof_indices]
self.obs_buf[..., 6:9] = ball_positions
self.obs_buf[..., 9:12] = ball_linvels
self.obs_buf[..., 12:15] = sensor_force_torques[..., 0] / 20.0
self.obs_buf[..., 15:18] = sensor_force_torques[..., 3] / 20.0
self.obs_buf[..., 18:21] = sensor_force_torques[..., 4] / 20.0
self.obs_buf[..., 21:24] = sensor_force_torques[..., 5] / 20.0
self.ball_positions = ball_positions
self.ball_linvels = ball_linvels
observations = {"ball_balance": {"obs_buf": self.obs_buf}}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
# update position targets from actions
self.dof_position_targets[..., self.actuated_dof_indices] += (
self._dt * self._action_speed_scale * actions.to(self.device)
)
self.dof_position_targets[:] = tensor_clamp(
self.dof_position_targets, self.bbot_dof_lower_limits, self.bbot_dof_upper_limits
)
# reset position targets for reset envs
self.dof_position_targets[reset_env_ids] = 0
self._balance_bots.set_joint_position_targets(self.dof_position_targets) # .clone())
def reset_idx(self, env_ids):
num_resets = len(env_ids)
env_ids_32 = env_ids.type(torch.int32)
env_ids_64 = env_ids.type(torch.int64)
min_d = 0.001 # min horizontal dist from origin
max_d = 0.4 # max horizontal dist from origin
min_height = 1.0
max_height = 2.0
min_horizontal_speed = 0
max_horizontal_speed = 2
dists = torch_rand_float(min_d, max_d, (num_resets, 1), self._device)
dirs = torch_random_dir_2((num_resets, 1), self._device)
hpos = dists * dirs
speedscales = (dists - min_d) / (max_d - min_d)
hspeeds = torch_rand_float(min_horizontal_speed, max_horizontal_speed, (num_resets, 1), self._device)
hvels = -speedscales * hspeeds * dirs
vspeeds = -torch_rand_float(5.0, 5.0, (num_resets, 1), self._device).squeeze()
ball_pos = self.initial_ball_pos.clone()
ball_rot = self.initial_ball_rot.clone()
# position
ball_pos[env_ids_64, 0:2] += hpos[..., 0:2]
ball_pos[env_ids_64, 2] += torch_rand_float(min_height, max_height, (num_resets, 1), self._device).squeeze()
# rotation
ball_rot[env_ids_64, 0] = 1
ball_rot[env_ids_64, 1:] = 0
ball_velocities = self.initial_ball_velocities.clone()
# linear
ball_velocities[env_ids_64, 0:2] = hvels[..., 0:2]
ball_velocities[env_ids_64, 2] = vspeeds
# angular
ball_velocities[env_ids_64, 3:6] = 0
# reset root state for bbots and balls in selected envs
self._balls.set_world_poses(ball_pos[env_ids_64], ball_rot[env_ids_64], indices=env_ids_32)
self._balls.set_velocities(ball_velocities[env_ids_64], indices=env_ids_32)
# reset root pose and velocity
self._balance_bots.set_world_poses(
self.initial_bot_pos[env_ids_64].clone(), self.initial_bot_rot[env_ids_64].clone(), indices=env_ids_32
)
self._balance_bots.set_velocities(self.initial_bot_velocities[env_ids_64].clone(), indices=env_ids_32)
# reset DOF states for bbots in selected envs
self._balance_bots.set_joint_positions(self.initial_dof_positions[env_ids_64].clone(), indices=env_ids_32)
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def post_reset(self):
dof_limits = self._balance_bots.get_dof_limits()
self.bbot_dof_lower_limits, self.bbot_dof_upper_limits = torch.t(dof_limits[0].to(device=self._device))
self.initial_dof_positions = self._balance_bots.get_joint_positions()
self.initial_bot_pos, self.initial_bot_rot = self._balance_bots.get_world_poses()
# self.initial_bot_pos[..., 2] = 0.559 # tray_height
self.initial_bot_velocities = self._balance_bots.get_velocities()
self.initial_ball_pos, self.initial_ball_rot = self._balls.get_world_poses()
self.initial_ball_velocities = self._balls.get_velocities()
self.dof_position_targets = torch.zeros(
(self.num_envs, self._balance_bots.num_dof), dtype=torch.float32, device=self._device, requires_grad=False
)
actuated_joints = ["lower_leg0", "lower_leg1", "lower_leg2"]
self.actuated_dof_indices = torch.tensor(
[self._balance_bots._dof_indices[j] for j in actuated_joints], device=self._device, dtype=torch.long
)
force_links = ["upper_leg0", "upper_leg1", "upper_leg2"]
self._sensor_indices = torch.tensor(
[self._balance_bots._body_indices[j] for j in force_links], device=self._device, dtype=torch.long
)
def calculate_metrics(self) -> None:
ball_dist = torch.sqrt(
self.ball_positions[..., 0] * self.ball_positions[..., 0]
+ (self.ball_positions[..., 2] - 0.7) * (self.ball_positions[..., 2] - 0.7)
+ (self.ball_positions[..., 1]) * self.ball_positions[..., 1]
)
ball_speed = torch.sqrt(
self.ball_linvels[..., 0] * self.ball_linvels[..., 0]
+ self.ball_linvels[..., 1] * self.ball_linvels[..., 1]
+ self.ball_linvels[..., 2] * self.ball_linvels[..., 2]
)
pos_reward = 1.0 / (1.0 + ball_dist)
speed_reward = 1.0 / (1.0 + ball_speed)
self.rew_buf[:] = pos_reward * speed_reward
def is_done(self) -> None:
reset = torch.where(
self.progress_buf >= self._max_episode_length - 1, torch.ones_like(self.reset_buf), self.reset_buf
)
reset = torch.where(
self.ball_positions[..., 2] < self._ball_radius * 1.5, torch.ones_like(self.reset_buf), reset
)
self.reset_buf[:] = reset
| 13,952 | Python | 44.15534 | 140 | 0.630447 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/cartpole_camera.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from gym import spaces
import numpy as np
import torch
import omni.usd
from pxr import UsdGeom
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.stage import get_current_stage
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.tasks.cartpole import CartpoleTask
from omniisaacgymenvs.robots.articulations.cartpole import Cartpole
class CartpoleCameraTask(CartpoleTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self._max_episode_length = 500
self._num_observations = self.camera_width * self.camera_height * 3
self._num_actions = 1
# use multi-dimensional observation for camera RGB
self.observation_space = spaces.Box(
np.ones((self.camera_width, self.camera_height, 3), dtype=np.float32) * -np.Inf,
np.ones((self.camera_width, self.camera_height, 3), dtype=np.float32) * np.Inf)
RLTask.__init__(self, name, env)
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._cartpole_positions = torch.tensor([0.0, 0.0, 2.0])
self._reset_dist = self._task_cfg["env"]["resetDist"]
self._max_push_effort = self._task_cfg["env"]["maxEffort"]
self.camera_type = self._task_cfg["env"].get("cameraType", 'rgb')
self.camera_width = self._task_cfg["env"]["cameraWidth"]
self.camera_height = self._task_cfg["env"]["cameraHeight"]
self.camera_channels = 3
self._export_images = self._task_cfg["env"]["exportImages"]
def cleanup(self) -> None:
# initialize remaining buffers
RLTask.cleanup(self)
# override observation buffer for camera data
self.obs_buf = torch.zeros(
(self.num_envs, self.camera_width, self.camera_height, 3), device=self.device, dtype=torch.float)
def add_camera(self) -> None:
stage = get_current_stage()
camera_path = f"/World/envs/env_0/Camera"
camera_xform = stage.DefinePrim(f'{camera_path}_Xform', 'Xform')
# set up transforms for parent and camera prims
position = (-4.2, 0.0, 3.0)
rotation = (0, -6.1155, -180)
UsdGeom.Xformable(camera_xform).AddTranslateOp()
UsdGeom.Xformable(camera_xform).AddRotateXYZOp()
camera_xform.GetAttribute('xformOp:translate').Set(position)
camera_xform.GetAttribute('xformOp:rotateXYZ').Set(rotation)
camera = stage.DefinePrim(f'{camera_path}_Xform/Camera', 'Camera')
UsdGeom.Xformable(camera).AddRotateXYZOp()
camera.GetAttribute("xformOp:rotateXYZ").Set((90, 0, 90))
# set camera properties
camera.GetAttribute('focalLength').Set(24)
camera.GetAttribute('focusDistance').Set(400)
# hide other environments in the background
camera.GetAttribute("clippingRange").Set((0.01, 20.0))
def set_up_scene(self, scene) -> None:
self.get_cartpole()
self.add_camera()
RLTask.set_up_scene(self, scene)
# start replicator to capture image data
self.rep.orchestrator._orchestrator._is_started = True
# set up cameras
self.render_products = []
env_pos = self._env_pos.cpu()
camera_paths = [f"/World/envs/env_{i}/Camera_Xform/Camera" for i in range(self._num_envs)]
for i in range(self._num_envs):
render_product = self.rep.create.render_product(camera_paths[i], resolution=(self.camera_width, self.camera_height))
self.render_products.append(render_product)
# initialize pytorch writer for vectorized collection
self.pytorch_listener = self.PytorchListener()
self.pytorch_writer = self.rep.WriterRegistry.get("PytorchWriter")
self.pytorch_writer.initialize(listener=self.pytorch_listener, device="cuda")
self.pytorch_writer.attach(self.render_products)
self._cartpoles = ArticulationView(
prim_paths_expr="/World/envs/.*/Cartpole", name="cartpole_view", reset_xform_properties=False
)
scene.add(self._cartpoles)
return
def get_observations(self) -> dict:
dof_pos = self._cartpoles.get_joint_positions(clone=False)
dof_vel = self._cartpoles.get_joint_velocities(clone=False)
self.cart_pos = dof_pos[:, self._cart_dof_idx]
self.cart_vel = dof_vel[:, self._cart_dof_idx]
self.pole_pos = dof_pos[:, self._pole_dof_idx]
self.pole_vel = dof_vel[:, self._pole_dof_idx]
# retrieve RGB data from all render products
images = self.pytorch_listener.get_rgb_data()
if images is not None:
if self._export_images:
from torchvision.utils import save_image, make_grid
img = images/255
save_image(make_grid(img, nrows = 2), 'cartpole_export.png')
self.obs_buf = torch.swapaxes(images, 1, 3).clone().float()/255.0
else:
print("Image tensor is NONE!")
return self.obs_buf
| 6,899 | Python | 42.396226 | 128 | 0.674301 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/anymal_terrain.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.simulation_context import SimulationContext
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.stage import get_current_stage
from omni.isaac.core.utils.torch.rotations import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.anymal import Anymal
from omniisaacgymenvs.robots.articulations.views.anymal_view import AnymalView
from omniisaacgymenvs.tasks.utils.anymal_terrain_generator import *
from omniisaacgymenvs.utils.terrain_utils.terrain_utils import *
from pxr import UsdLux, UsdPhysics
class AnymalTerrainTask(RLTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.height_samples = None
self.custom_origins = False
self.init_done = False
self._env_spacing = 0.0
self._num_observations = 188
self._num_actions = 12
self.update_config(sim_config)
RLTask.__init__(self, name, env)
self.height_points = self.init_height_points()
self.measured_heights = None
# joint positions offsets
self.default_dof_pos = torch.zeros(
(self.num_envs, 12), dtype=torch.float, device=self.device, requires_grad=False
)
# reward episode sums
torch_zeros = lambda: torch.zeros(self.num_envs, dtype=torch.float, device=self.device, requires_grad=False)
self.episode_sums = {
"lin_vel_xy": torch_zeros(),
"lin_vel_z": torch_zeros(),
"ang_vel_z": torch_zeros(),
"ang_vel_xy": torch_zeros(),
"orient": torch_zeros(),
"torques": torch_zeros(),
"joint_acc": torch_zeros(),
"base_height": torch_zeros(),
"air_time": torch_zeros(),
"collision": torch_zeros(),
"stumble": torch_zeros(),
"action_rate": torch_zeros(),
"hip": torch_zeros(),
}
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
# normalization
self.lin_vel_scale = self._task_cfg["env"]["learn"]["linearVelocityScale"]
self.ang_vel_scale = self._task_cfg["env"]["learn"]["angularVelocityScale"]
self.dof_pos_scale = self._task_cfg["env"]["learn"]["dofPositionScale"]
self.dof_vel_scale = self._task_cfg["env"]["learn"]["dofVelocityScale"]
self.height_meas_scale = self._task_cfg["env"]["learn"]["heightMeasurementScale"]
self.action_scale = self._task_cfg["env"]["control"]["actionScale"]
# reward scales
self.rew_scales = {}
self.rew_scales["termination"] = self._task_cfg["env"]["learn"]["terminalReward"]
self.rew_scales["lin_vel_xy"] = self._task_cfg["env"]["learn"]["linearVelocityXYRewardScale"]
self.rew_scales["lin_vel_z"] = self._task_cfg["env"]["learn"]["linearVelocityZRewardScale"]
self.rew_scales["ang_vel_z"] = self._task_cfg["env"]["learn"]["angularVelocityZRewardScale"]
self.rew_scales["ang_vel_xy"] = self._task_cfg["env"]["learn"]["angularVelocityXYRewardScale"]
self.rew_scales["orient"] = self._task_cfg["env"]["learn"]["orientationRewardScale"]
self.rew_scales["torque"] = self._task_cfg["env"]["learn"]["torqueRewardScale"]
self.rew_scales["joint_acc"] = self._task_cfg["env"]["learn"]["jointAccRewardScale"]
self.rew_scales["base_height"] = self._task_cfg["env"]["learn"]["baseHeightRewardScale"]
self.rew_scales["action_rate"] = self._task_cfg["env"]["learn"]["actionRateRewardScale"]
self.rew_scales["hip"] = self._task_cfg["env"]["learn"]["hipRewardScale"]
self.rew_scales["fallen_over"] = self._task_cfg["env"]["learn"]["fallenOverRewardScale"]
# command ranges
self.command_x_range = self._task_cfg["env"]["randomCommandVelocityRanges"]["linear_x"]
self.command_y_range = self._task_cfg["env"]["randomCommandVelocityRanges"]["linear_y"]
self.command_yaw_range = self._task_cfg["env"]["randomCommandVelocityRanges"]["yaw"]
# base init state
pos = self._task_cfg["env"]["baseInitState"]["pos"]
rot = self._task_cfg["env"]["baseInitState"]["rot"]
v_lin = self._task_cfg["env"]["baseInitState"]["vLinear"]
v_ang = self._task_cfg["env"]["baseInitState"]["vAngular"]
self.base_init_state = pos + rot + v_lin + v_ang
# default joint positions
self.named_default_joint_angles = self._task_cfg["env"]["defaultJointAngles"]
# other
self.decimation = self._task_cfg["env"]["control"]["decimation"]
self.dt = self.decimation * self._task_cfg["sim"]["dt"]
self.max_episode_length_s = self._task_cfg["env"]["learn"]["episodeLength_s"]
self.max_episode_length = int(self.max_episode_length_s / self.dt + 0.5)
self.push_interval = int(self._task_cfg["env"]["learn"]["pushInterval_s"] / self.dt + 0.5)
self.Kp = self._task_cfg["env"]["control"]["stiffness"]
self.Kd = self._task_cfg["env"]["control"]["damping"]
self.curriculum = self._task_cfg["env"]["terrain"]["curriculum"]
self.base_threshold = 0.2
self.knee_threshold = 0.1
for key in self.rew_scales.keys():
self.rew_scales[key] *= self.dt
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._task_cfg["sim"]["default_physics_material"]["static_friction"] = self._task_cfg["env"]["terrain"][
"staticFriction"
]
self._task_cfg["sim"]["default_physics_material"]["dynamic_friction"] = self._task_cfg["env"]["terrain"][
"dynamicFriction"
]
self._task_cfg["sim"]["default_physics_material"]["restitution"] = self._task_cfg["env"]["terrain"][
"restitution"
]
self._task_cfg["sim"]["add_ground_plane"] = False
def _get_noise_scale_vec(self, cfg):
noise_vec = torch.zeros_like(self.obs_buf[0])
self.add_noise = self._task_cfg["env"]["learn"]["addNoise"]
noise_level = self._task_cfg["env"]["learn"]["noiseLevel"]
noise_vec[:3] = self._task_cfg["env"]["learn"]["linearVelocityNoise"] * noise_level * self.lin_vel_scale
noise_vec[3:6] = self._task_cfg["env"]["learn"]["angularVelocityNoise"] * noise_level * self.ang_vel_scale
noise_vec[6:9] = self._task_cfg["env"]["learn"]["gravityNoise"] * noise_level
noise_vec[9:12] = 0.0 # commands
noise_vec[12:24] = self._task_cfg["env"]["learn"]["dofPositionNoise"] * noise_level * self.dof_pos_scale
noise_vec[24:36] = self._task_cfg["env"]["learn"]["dofVelocityNoise"] * noise_level * self.dof_vel_scale
noise_vec[36:176] = (
self._task_cfg["env"]["learn"]["heightMeasurementNoise"] * noise_level * self.height_meas_scale
)
noise_vec[176:188] = 0.0 # previous actions
return noise_vec
def init_height_points(self):
# 1mx1.6m rectangle (without center line)
y = 0.1 * torch.tensor(
[-5, -4, -3, -2, -1, 1, 2, 3, 4, 5], device=self.device, requires_grad=False
) # 10-50cm on each side
x = 0.1 * torch.tensor(
[-8, -7, -6, -5, -4, -3, -2, 2, 3, 4, 5, 6, 7, 8], device=self.device, requires_grad=False
) # 20-80cm on each side
grid_x, grid_y = torch.meshgrid(x, y, indexing='ij')
self.num_height_points = grid_x.numel()
points = torch.zeros(self.num_envs, self.num_height_points, 3, device=self.device, requires_grad=False)
points[:, :, 0] = grid_x.flatten()
points[:, :, 1] = grid_y.flatten()
return points
def _create_trimesh(self, create_mesh=True):
self.terrain = Terrain(self._task_cfg["env"]["terrain"], num_robots=self.num_envs)
vertices = self.terrain.vertices
triangles = self.terrain.triangles
position = torch.tensor([-self.terrain.border_size, -self.terrain.border_size, 0.0])
if create_mesh:
add_terrain_to_stage(stage=self._stage, vertices=vertices, triangles=triangles, position=position)
self.height_samples = (
torch.tensor(self.terrain.heightsamples).view(self.terrain.tot_rows, self.terrain.tot_cols).to(self.device)
)
def set_up_scene(self, scene) -> None:
self._stage = get_current_stage()
self.get_terrain()
self.get_anymal()
super().set_up_scene(scene, collision_filter_global_paths=["/World/terrain"])
self._anymals = AnymalView(
prim_paths_expr="/World/envs/.*/anymal", name="anymal_view", track_contact_forces=True
)
scene.add(self._anymals)
scene.add(self._anymals._knees)
scene.add(self._anymals._base)
def initialize_views(self, scene):
# initialize terrain variables even if we do not need to re-create the terrain mesh
self.get_terrain(create_mesh=False)
super().initialize_views(scene)
if scene.object_exists("anymal_view"):
scene.remove_object("anymal_view", registry_only=True)
if scene.object_exists("knees_view"):
scene.remove_object("knees_view", registry_only=True)
if scene.object_exists("base_view"):
scene.remove_object("base_view", registry_only=True)
self._anymals = AnymalView(
prim_paths_expr="/World/envs/.*/anymal", name="anymal_view", track_contact_forces=True
)
scene.add(self._anymals)
scene.add(self._anymals._knees)
scene.add(self._anymals._base)
def get_terrain(self, create_mesh=True):
self.env_origins = torch.zeros((self.num_envs, 3), device=self.device, requires_grad=False)
if not self.curriculum:
self._task_cfg["env"]["terrain"]["maxInitMapLevel"] = self._task_cfg["env"]["terrain"]["numLevels"] - 1
self.terrain_levels = torch.randint(
0, self._task_cfg["env"]["terrain"]["maxInitMapLevel"] + 1, (self.num_envs,), device=self.device
)
self.terrain_types = torch.randint(
0, self._task_cfg["env"]["terrain"]["numTerrains"], (self.num_envs,), device=self.device
)
self._create_trimesh(create_mesh=create_mesh)
self.terrain_origins = torch.from_numpy(self.terrain.env_origins).to(self.device).to(torch.float)
def get_anymal(self):
anymal_translation = torch.tensor([0.0, 0.0, 0.66])
anymal_orientation = torch.tensor([1.0, 0.0, 0.0, 0.0])
anymal = Anymal(
prim_path=self.default_zero_env_path + "/anymal",
name="anymal",
translation=anymal_translation,
orientation=anymal_orientation,
)
self._sim_config.apply_articulation_settings(
"anymal", get_prim_at_path(anymal.prim_path), self._sim_config.parse_actor_config("anymal")
)
anymal.set_anymal_properties(self._stage, anymal.prim)
anymal.prepare_contacts(self._stage, anymal.prim)
self.dof_names = anymal.dof_names
for i in range(self.num_actions):
name = self.dof_names[i]
angle = self.named_default_joint_angles[name]
self.default_dof_pos[:, i] = angle
def post_reset(self):
self.base_init_state = torch.tensor(
self.base_init_state, dtype=torch.float, device=self.device, requires_grad=False
)
self.timeout_buf = torch.zeros(self.num_envs, device=self.device, dtype=torch.long)
# initialize some data used later on
self.up_axis_idx = 2
self.common_step_counter = 0
self.extras = {}
self.noise_scale_vec = self._get_noise_scale_vec(self._task_cfg)
self.commands = torch.zeros(
self.num_envs, 4, dtype=torch.float, device=self.device, requires_grad=False
) # x vel, y vel, yaw vel, heading
self.commands_scale = torch.tensor(
[self.lin_vel_scale, self.lin_vel_scale, self.ang_vel_scale],
device=self.device,
requires_grad=False,
)
self.gravity_vec = torch.tensor(
get_axis_params(-1.0, self.up_axis_idx), dtype=torch.float, device=self.device
).repeat((self.num_envs, 1))
self.forward_vec = torch.tensor([1.0, 0.0, 0.0], dtype=torch.float, device=self.device).repeat(
(self.num_envs, 1)
)
self.torques = torch.zeros(
self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False
)
self.actions = torch.zeros(
self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False
)
self.last_actions = torch.zeros(
self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False
)
self.feet_air_time = torch.zeros(self.num_envs, 4, dtype=torch.float, device=self.device, requires_grad=False)
self.last_dof_vel = torch.zeros((self.num_envs, 12), dtype=torch.float, device=self.device, requires_grad=False)
for i in range(self.num_envs):
self.env_origins[i] = self.terrain_origins[self.terrain_levels[i], self.terrain_types[i]]
self.num_dof = self._anymals.num_dof
self.dof_pos = torch.zeros((self.num_envs, self.num_dof), dtype=torch.float, device=self.device)
self.dof_vel = torch.zeros((self.num_envs, self.num_dof), dtype=torch.float, device=self.device)
self.base_pos = torch.zeros((self.num_envs, 3), dtype=torch.float, device=self.device)
self.base_quat = torch.zeros((self.num_envs, 4), dtype=torch.float, device=self.device)
self.base_velocities = torch.zeros((self.num_envs, 6), dtype=torch.float, device=self.device)
self.knee_pos = torch.zeros((self.num_envs * 4, 3), dtype=torch.float, device=self.device)
self.knee_quat = torch.zeros((self.num_envs * 4, 4), dtype=torch.float, device=self.device)
indices = torch.arange(self._num_envs, dtype=torch.int64, device=self._device)
self.reset_idx(indices)
self.init_done = True
def reset_idx(self, env_ids):
indices = env_ids.to(dtype=torch.int32)
positions_offset = torch_rand_float(0.5, 1.5, (len(env_ids), self.num_dof), device=self.device)
velocities = torch_rand_float(-0.1, 0.1, (len(env_ids), self.num_dof), device=self.device)
self.dof_pos[env_ids] = self.default_dof_pos[env_ids] * positions_offset
self.dof_vel[env_ids] = velocities
self.update_terrain_level(env_ids)
self.base_pos[env_ids] = self.base_init_state[0:3]
self.base_pos[env_ids, 0:3] += self.env_origins[env_ids]
self.base_pos[env_ids, 0:2] += torch_rand_float(-0.5, 0.5, (len(env_ids), 2), device=self.device)
self.base_quat[env_ids] = self.base_init_state[3:7]
self.base_velocities[env_ids] = self.base_init_state[7:]
self._anymals.set_world_poses(
positions=self.base_pos[env_ids].clone(), orientations=self.base_quat[env_ids].clone(), indices=indices
)
self._anymals.set_velocities(velocities=self.base_velocities[env_ids].clone(), indices=indices)
self._anymals.set_joint_positions(positions=self.dof_pos[env_ids].clone(), indices=indices)
self._anymals.set_joint_velocities(velocities=self.dof_vel[env_ids].clone(), indices=indices)
self.commands[env_ids, 0] = torch_rand_float(
self.command_x_range[0], self.command_x_range[1], (len(env_ids), 1), device=self.device
).squeeze()
self.commands[env_ids, 1] = torch_rand_float(
self.command_y_range[0], self.command_y_range[1], (len(env_ids), 1), device=self.device
).squeeze()
self.commands[env_ids, 3] = torch_rand_float(
self.command_yaw_range[0], self.command_yaw_range[1], (len(env_ids), 1), device=self.device
).squeeze()
self.commands[env_ids] *= (torch.norm(self.commands[env_ids, :2], dim=1) > 0.25).unsqueeze(
1
) # set small commands to zero
self.last_actions[env_ids] = 0.0
self.last_dof_vel[env_ids] = 0.0
self.feet_air_time[env_ids] = 0.0
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 1
# fill extras
self.extras["episode"] = {}
for key in self.episode_sums.keys():
self.extras["episode"]["rew_" + key] = (
torch.mean(self.episode_sums[key][env_ids]) / self.max_episode_length_s
)
self.episode_sums[key][env_ids] = 0.0
self.extras["episode"]["terrain_level"] = torch.mean(self.terrain_levels.float())
def update_terrain_level(self, env_ids):
if not self.init_done or not self.curriculum:
# do not change on initial reset
return
root_pos, _ = self._anymals.get_world_poses(clone=False)
distance = torch.norm(root_pos[env_ids, :2] - self.env_origins[env_ids, :2], dim=1)
self.terrain_levels[env_ids] -= 1 * (
distance < torch.norm(self.commands[env_ids, :2]) * self.max_episode_length_s * 0.25
)
self.terrain_levels[env_ids] += 1 * (distance > self.terrain.env_length / 2)
self.terrain_levels[env_ids] = torch.clip(self.terrain_levels[env_ids], 0) % self.terrain.env_rows
self.env_origins[env_ids] = self.terrain_origins[self.terrain_levels[env_ids], self.terrain_types[env_ids]]
def refresh_dof_state_tensors(self):
self.dof_pos = self._anymals.get_joint_positions(clone=False)
self.dof_vel = self._anymals.get_joint_velocities(clone=False)
def refresh_body_state_tensors(self):
self.base_pos, self.base_quat = self._anymals.get_world_poses(clone=False)
self.base_velocities = self._anymals.get_velocities(clone=False)
self.knee_pos, self.knee_quat = self._anymals._knees.get_world_poses(clone=False)
def pre_physics_step(self, actions):
if not self.world.is_playing():
return
self.actions = actions.clone().to(self.device)
for i in range(self.decimation):
if self.world.is_playing():
torques = torch.clip(
self.Kp * (self.action_scale * self.actions + self.default_dof_pos - self.dof_pos)
- self.Kd * self.dof_vel,
-80.0,
80.0,
)
self._anymals.set_joint_efforts(torques)
self.torques = torques
SimulationContext.step(self.world, render=False)
self.refresh_dof_state_tensors()
def post_physics_step(self):
self.progress_buf[:] += 1
if self.world.is_playing():
self.refresh_dof_state_tensors()
self.refresh_body_state_tensors()
self.common_step_counter += 1
if self.common_step_counter % self.push_interval == 0:
self.push_robots()
# prepare quantities
self.base_lin_vel = quat_rotate_inverse(self.base_quat, self.base_velocities[:, 0:3])
self.base_ang_vel = quat_rotate_inverse(self.base_quat, self.base_velocities[:, 3:6])
self.projected_gravity = quat_rotate_inverse(self.base_quat, self.gravity_vec)
forward = quat_apply(self.base_quat, self.forward_vec)
heading = torch.atan2(forward[:, 1], forward[:, 0])
self.commands[:, 2] = torch.clip(0.5 * wrap_to_pi(self.commands[:, 3] - heading), -1.0, 1.0)
self.check_termination()
self.get_states()
self.calculate_metrics()
env_ids = self.reset_buf.nonzero(as_tuple=False).flatten()
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.get_observations()
if self.add_noise:
self.obs_buf += (2 * torch.rand_like(self.obs_buf) - 1) * self.noise_scale_vec
self.last_actions[:] = self.actions[:]
self.last_dof_vel[:] = self.dof_vel[:]
return self.obs_buf, self.rew_buf, self.reset_buf, self.extras
def push_robots(self):
self.base_velocities[:, 0:2] = torch_rand_float(
-1.0, 1.0, (self.num_envs, 2), device=self.device
) # lin vel x/y
self._anymals.set_velocities(self.base_velocities)
def check_termination(self):
self.timeout_buf = torch.where(
self.progress_buf >= self.max_episode_length - 1,
torch.ones_like(self.timeout_buf),
torch.zeros_like(self.timeout_buf),
)
knee_contact = (
torch.norm(self._anymals._knees.get_net_contact_forces(clone=False).view(self._num_envs, 4, 3), dim=-1)
> 1.0
)
self.has_fallen = (torch.norm(self._anymals._base.get_net_contact_forces(clone=False), dim=1) > 1.0) | (
torch.sum(knee_contact, dim=-1) > 1.0
)
self.reset_buf = self.has_fallen.clone()
self.reset_buf = torch.where(self.timeout_buf.bool(), torch.ones_like(self.reset_buf), self.reset_buf)
def calculate_metrics(self):
# velocity tracking reward
lin_vel_error = torch.sum(torch.square(self.commands[:, :2] - self.base_lin_vel[:, :2]), dim=1)
ang_vel_error = torch.square(self.commands[:, 2] - self.base_ang_vel[:, 2])
rew_lin_vel_xy = torch.exp(-lin_vel_error / 0.25) * self.rew_scales["lin_vel_xy"]
rew_ang_vel_z = torch.exp(-ang_vel_error / 0.25) * self.rew_scales["ang_vel_z"]
# other base velocity penalties
rew_lin_vel_z = torch.square(self.base_lin_vel[:, 2]) * self.rew_scales["lin_vel_z"]
rew_ang_vel_xy = torch.sum(torch.square(self.base_ang_vel[:, :2]), dim=1) * self.rew_scales["ang_vel_xy"]
# orientation penalty
rew_orient = torch.sum(torch.square(self.projected_gravity[:, :2]), dim=1) * self.rew_scales["orient"]
# base height penalty
rew_base_height = torch.square(self.base_pos[:, 2] - 0.52) * self.rew_scales["base_height"]
# torque penalty
rew_torque = torch.sum(torch.square(self.torques), dim=1) * self.rew_scales["torque"]
# joint acc penalty
rew_joint_acc = torch.sum(torch.square(self.last_dof_vel - self.dof_vel), dim=1) * self.rew_scales["joint_acc"]
# fallen over penalty
rew_fallen_over = self.has_fallen * self.rew_scales["fallen_over"]
# action rate penalty
rew_action_rate = (
torch.sum(torch.square(self.last_actions - self.actions), dim=1) * self.rew_scales["action_rate"]
)
# cosmetic penalty for hip motion
rew_hip = (
torch.sum(torch.abs(self.dof_pos[:, 0:4] - self.default_dof_pos[:, 0:4]), dim=1) * self.rew_scales["hip"]
)
# total reward
self.rew_buf = (
rew_lin_vel_xy
+ rew_ang_vel_z
+ rew_lin_vel_z
+ rew_ang_vel_xy
+ rew_orient
+ rew_base_height
+ rew_torque
+ rew_joint_acc
+ rew_action_rate
+ rew_hip
+ rew_fallen_over
)
self.rew_buf = torch.clip(self.rew_buf, min=0.0, max=None)
# add termination reward
self.rew_buf += self.rew_scales["termination"] * self.reset_buf * ~self.timeout_buf
# log episode reward sums
self.episode_sums["lin_vel_xy"] += rew_lin_vel_xy
self.episode_sums["ang_vel_z"] += rew_ang_vel_z
self.episode_sums["lin_vel_z"] += rew_lin_vel_z
self.episode_sums["ang_vel_xy"] += rew_ang_vel_xy
self.episode_sums["orient"] += rew_orient
self.episode_sums["torques"] += rew_torque
self.episode_sums["joint_acc"] += rew_joint_acc
self.episode_sums["action_rate"] += rew_action_rate
self.episode_sums["base_height"] += rew_base_height
self.episode_sums["hip"] += rew_hip
def get_observations(self):
self.measured_heights = self.get_heights()
heights = (
torch.clip(self.base_pos[:, 2].unsqueeze(1) - 0.5 - self.measured_heights, -1, 1.0) * self.height_meas_scale
)
self.obs_buf = torch.cat(
(
self.base_lin_vel * self.lin_vel_scale,
self.base_ang_vel * self.ang_vel_scale,
self.projected_gravity,
self.commands[:, :3] * self.commands_scale,
self.dof_pos * self.dof_pos_scale,
self.dof_vel * self.dof_vel_scale,
heights,
self.actions,
),
dim=-1,
)
def get_ground_heights_below_knees(self):
points = self.knee_pos.reshape(self.num_envs, 4, 3)
points += self.terrain.border_size
points = (points / self.terrain.horizontal_scale).long()
px = points[:, :, 0].view(-1)
py = points[:, :, 1].view(-1)
px = torch.clip(px, 0, self.height_samples.shape[0] - 2)
py = torch.clip(py, 0, self.height_samples.shape[1] - 2)
heights1 = self.height_samples[px, py]
heights2 = self.height_samples[px + 1, py + 1]
heights = torch.min(heights1, heights2)
return heights.view(self.num_envs, -1) * self.terrain.vertical_scale
def get_ground_heights_below_base(self):
points = self.base_pos.reshape(self.num_envs, 1, 3)
points += self.terrain.border_size
points = (points / self.terrain.horizontal_scale).long()
px = points[:, :, 0].view(-1)
py = points[:, :, 1].view(-1)
px = torch.clip(px, 0, self.height_samples.shape[0] - 2)
py = torch.clip(py, 0, self.height_samples.shape[1] - 2)
heights1 = self.height_samples[px, py]
heights2 = self.height_samples[px + 1, py + 1]
heights = torch.min(heights1, heights2)
return heights.view(self.num_envs, -1) * self.terrain.vertical_scale
def get_heights(self, env_ids=None):
if env_ids:
points = quat_apply_yaw(
self.base_quat[env_ids].repeat(1, self.num_height_points), self.height_points[env_ids]
) + (self.base_pos[env_ids, 0:3]).unsqueeze(1)
else:
points = quat_apply_yaw(self.base_quat.repeat(1, self.num_height_points), self.height_points) + (
self.base_pos[:, 0:3]
).unsqueeze(1)
points += self.terrain.border_size
points = (points / self.terrain.horizontal_scale).long()
px = points[:, :, 0].view(-1)
py = points[:, :, 1].view(-1)
px = torch.clip(px, 0, self.height_samples.shape[0] - 2)
py = torch.clip(py, 0, self.height_samples.shape[1] - 2)
heights1 = self.height_samples[px, py]
heights2 = self.height_samples[px + 1, py + 1]
heights = torch.min(heights1, heights2)
return heights.view(self.num_envs, -1) * self.terrain.vertical_scale
@torch.jit.script
def quat_apply_yaw(quat, vec):
quat_yaw = quat.clone().view(-1, 4)
quat_yaw[:, 1:3] = 0.0
quat_yaw = normalize(quat_yaw)
return quat_apply(quat_yaw, vec)
@torch.jit.script
def wrap_to_pi(angles):
angles %= 2 * np.pi
angles -= 2 * np.pi * (angles > np.pi)
return angles
def get_axis_params(value, axis_idx, x_value=0.0, dtype=float, n_dims=3):
"""construct arguments to `Vec` according to axis index."""
zs = np.zeros((n_dims,))
assert axis_idx < n_dims, "the axis dim should be within the vector dimensions"
zs[axis_idx] = 1.0
params = np.where(zs == 1.0, value, zs)
params[0] = x_value
return list(params.astype(dtype))
| 29,313 | Python | 45.530159 | 120 | 0.609218 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/shadow_hand.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.torch import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.shadow_hand import ShadowHand
from omniisaacgymenvs.robots.articulations.views.shadow_hand_view import ShadowHandView
from omniisaacgymenvs.tasks.shared.in_hand_manipulation import InHandManipulationTask
class ShadowHandTask(InHandManipulationTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
InHandManipulationTask.__init__(self, name=name, env=env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self.object_type = self._task_cfg["env"]["objectType"]
assert self.object_type in ["block"]
self.obs_type = self._task_cfg["env"]["observationType"]
if not (self.obs_type in ["openai", "full_no_vel", "full", "full_state"]):
raise Exception(
"Unknown type of observations!\nobservationType should be one of: [openai, full_no_vel, full, full_state]"
)
print("Obs type:", self.obs_type)
self.num_obs_dict = {
"openai": 42,
"full_no_vel": 77,
"full": 157,
"full_state": 187,
}
self.asymmetric_obs = self._task_cfg["env"]["asymmetric_observations"]
self.use_vel_obs = False
self.fingertip_obs = True
self.fingertips = [
"robot0:ffdistal",
"robot0:mfdistal",
"robot0:rfdistal",
"robot0:lfdistal",
"robot0:thdistal",
]
self.num_fingertips = len(self.fingertips)
self.object_scale = torch.tensor([1.0, 1.0, 1.0])
self.force_torque_obs_scale = 10.0
num_states = 0
if self.asymmetric_obs:
num_states = 187
self._num_observations = self.num_obs_dict[self.obs_type]
self._num_actions = 20
self._num_states = num_states
InHandManipulationTask.update_config(self)
def get_starting_positions(self):
self.hand_start_translation = torch.tensor([0.0, 0.0, 0.5], device=self.device)
self.hand_start_orientation = torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device)
self.pose_dy, self.pose_dz = -0.39, 0.10
def get_hand(self):
shadow_hand = ShadowHand(
prim_path=self.default_zero_env_path + "/shadow_hand",
name="shadow_hand",
translation=self.hand_start_translation,
orientation=self.hand_start_orientation,
)
self._sim_config.apply_articulation_settings(
"shadow_hand",
get_prim_at_path(shadow_hand.prim_path),
self._sim_config.parse_actor_config("shadow_hand"),
)
shadow_hand.set_shadow_hand_properties(stage=self._stage, shadow_hand_prim=shadow_hand.prim)
shadow_hand.set_motor_control_mode(stage=self._stage, shadow_hand_path=shadow_hand.prim_path)
def get_hand_view(self, scene):
hand_view = ShadowHandView(prim_paths_expr="/World/envs/.*/shadow_hand", name="shadow_hand_view")
scene.add(hand_view._fingers)
return hand_view
def get_observations(self):
self.get_object_goal_observations()
self.fingertip_pos, self.fingertip_rot = self._hands._fingers.get_world_poses(clone=False)
self.fingertip_pos -= self._env_pos.repeat((1, self.num_fingertips)).reshape(
self.num_envs * self.num_fingertips, 3
)
self.fingertip_velocities = self._hands._fingers.get_velocities(clone=False)
self.hand_dof_pos = self._hands.get_joint_positions(clone=False)
self.hand_dof_vel = self._hands.get_joint_velocities(clone=False)
if self.obs_type == "full_state" or self.asymmetric_obs:
self.vec_sensor_tensor = self._hands.get_measured_joint_forces(
joint_indices=self._hands._sensor_indices
).view(self._num_envs, -1)
if self.obs_type == "openai":
self.compute_fingertip_observations(True)
elif self.obs_type == "full_no_vel":
self.compute_full_observations(True)
elif self.obs_type == "full":
self.compute_full_observations()
elif self.obs_type == "full_state":
self.compute_full_state(False)
else:
print("Unkown observations type!")
if self.asymmetric_obs:
self.compute_full_state(True)
observations = {self._hands.name: {"obs_buf": self.obs_buf}}
return observations
def compute_fingertip_observations(self, no_vel=False):
if no_vel:
# Per https://arxiv.org/pdf/1808.00177.pdf Table 2
# Fingertip positions
# Object Position, but not orientation
# Relative target orientation
# 3*self.num_fingertips = 15
self.obs_buf[:, 0:15] = self.fingertip_pos.reshape(self.num_envs, 15)
self.obs_buf[:, 15:18] = self.object_pos
self.obs_buf[:, 18:22] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.obs_buf[:, 22:42] = self.actions
else:
# 13*self.num_fingertips = 65
self.obs_buf[:, 0:65] = self.fingertip_state.reshape(self.num_envs, 65)
self.obs_buf[:, 0:15] = self.fingertip_pos.reshape(self.num_envs, 3 * self.num_fingertips)
self.obs_buf[:, 15:35] = self.fingertip_rot.reshape(self.num_envs, 4 * self.num_fingertips)
self.obs_buf[:, 35:65] = self.fingertip_velocities.reshape(self.num_envs, 6 * self.num_fingertips)
self.obs_buf[:, 65:68] = self.object_pos
self.obs_buf[:, 68:72] = self.object_rot
self.obs_buf[:, 72:75] = self.object_linvel
self.obs_buf[:, 75:78] = self.vel_obs_scale * self.object_angvel
self.obs_buf[:, 78:81] = self.goal_pos
self.obs_buf[:, 81:85] = self.goal_rot
self.obs_buf[:, 85:89] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.obs_buf[:, 89:109] = self.actions
def compute_full_observations(self, no_vel=False):
if no_vel:
self.obs_buf[:, 0 : self.num_hand_dofs] = unscale(
self.hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits
)
self.obs_buf[:, 24:37] = self.object_pos
self.obs_buf[:, 27:31] = self.object_rot
self.obs_buf[:, 31:34] = self.goal_pos
self.obs_buf[:, 34:38] = self.goal_rot
self.obs_buf[:, 38:42] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
self.obs_buf[:, 42:57] = self.fingertip_pos.reshape(self.num_envs, 3 * self.num_fingertips)
self.obs_buf[:, 57:77] = self.actions
else:
self.obs_buf[:, 0 : self.num_hand_dofs] = unscale(
self.hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits
)
self.obs_buf[:, self.num_hand_dofs : 2 * self.num_hand_dofs] = self.vel_obs_scale * self.hand_dof_vel
self.obs_buf[:, 48:51] = self.object_pos
self.obs_buf[:, 51:55] = self.object_rot
self.obs_buf[:, 55:58] = self.object_linvel
self.obs_buf[:, 58:61] = self.vel_obs_scale * self.object_angvel
self.obs_buf[:, 61:64] = self.goal_pos
self.obs_buf[:, 64:68] = self.goal_rot
self.obs_buf[:, 68:72] = quat_mul(self.object_rot, quat_conjugate(self.goal_rot))
# (7+6)*self.num_fingertips = 65
self.obs_buf[:, 72:87] = self.fingertip_pos.reshape(self.num_envs, 3 * self.num_fingertips)
self.obs_buf[:, 87:107] = self.fingertip_rot.reshape(self.num_envs, 4 * self.num_fingertips)
self.obs_buf[:, 107:137] = self.fingertip_velocities.reshape(self.num_envs, 6 * self.num_fingertips)
self.obs_buf[:, 137:157] = self.actions
def compute_full_state(self, asymm_obs=False):
if asymm_obs:
self.states_buf[:, 0 : self.num_hand_dofs] = unscale(
self.hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits
)
self.states_buf[:, self.num_hand_dofs : 2 * self.num_hand_dofs] = self.vel_obs_scale * self.hand_dof_vel
# self.states_buf[:, 2*self.num_hand_dofs:3*self.num_hand_dofs] = self.force_torque_obs_scale * self.dof_force_tensor
obj_obs_start = 2 * self.num_hand_dofs # 48
self.states_buf[:, obj_obs_start : obj_obs_start + 3] = self.object_pos
self.states_buf[:, obj_obs_start + 3 : obj_obs_start + 7] = self.object_rot
self.states_buf[:, obj_obs_start + 7 : obj_obs_start + 10] = self.object_linvel
self.states_buf[:, obj_obs_start + 10 : obj_obs_start + 13] = self.vel_obs_scale * self.object_angvel
goal_obs_start = obj_obs_start + 13 # 61
self.states_buf[:, goal_obs_start : goal_obs_start + 3] = self.goal_pos
self.states_buf[:, goal_obs_start + 3 : goal_obs_start + 7] = self.goal_rot
self.states_buf[:, goal_obs_start + 7 : goal_obs_start + 11] = quat_mul(
self.object_rot, quat_conjugate(self.goal_rot)
)
# fingertip observations, state(pose and vel) + force-torque sensors
num_ft_states = 13 * self.num_fingertips # 65
num_ft_force_torques = 6 * self.num_fingertips # 30
fingertip_obs_start = goal_obs_start + 11 # 72
self.states_buf[
:, fingertip_obs_start : fingertip_obs_start + 3 * self.num_fingertips
] = self.fingertip_pos.reshape(self.num_envs, 3 * self.num_fingertips)
self.states_buf[
:, fingertip_obs_start + 3 * self.num_fingertips : fingertip_obs_start + 7 * self.num_fingertips
] = self.fingertip_rot.reshape(self.num_envs, 4 * self.num_fingertips)
self.states_buf[
:, fingertip_obs_start + 7 * self.num_fingertips : fingertip_obs_start + 13 * self.num_fingertips
] = self.fingertip_velocities.reshape(self.num_envs, 6 * self.num_fingertips)
self.states_buf[
:, fingertip_obs_start + num_ft_states : fingertip_obs_start + num_ft_states + num_ft_force_torques
] = (self.force_torque_obs_scale * self.vec_sensor_tensor)
# obs_end = 72 + 65 + 30 = 167
# obs_total = obs_end + num_actions = 187
obs_end = fingertip_obs_start + num_ft_states + num_ft_force_torques
self.states_buf[:, obs_end : obs_end + self.num_actions] = self.actions
else:
self.obs_buf[:, 0 : self.num_hand_dofs] = unscale(
self.hand_dof_pos, self.hand_dof_lower_limits, self.hand_dof_upper_limits
)
self.obs_buf[:, self.num_hand_dofs : 2 * self.num_hand_dofs] = self.vel_obs_scale * self.hand_dof_vel
self.obs_buf[:, 2 * self.num_hand_dofs : 3 * self.num_hand_dofs] = (
self.force_torque_obs_scale * self.dof_force_tensor
)
obj_obs_start = 3 * self.num_hand_dofs # 48
self.obs_buf[:, obj_obs_start : obj_obs_start + 3] = self.object_pos
self.obs_buf[:, obj_obs_start + 3 : obj_obs_start + 7] = self.object_rot
self.obs_buf[:, obj_obs_start + 7 : obj_obs_start + 10] = self.object_linvel
self.obs_buf[:, obj_obs_start + 10 : obj_obs_start + 13] = self.vel_obs_scale * self.object_angvel
goal_obs_start = obj_obs_start + 13 # 61
self.obs_buf[:, goal_obs_start : goal_obs_start + 3] = self.goal_pos
self.obs_buf[:, goal_obs_start + 3 : goal_obs_start + 7] = self.goal_rot
self.obs_buf[:, goal_obs_start + 7 : goal_obs_start + 11] = quat_mul(
self.object_rot, quat_conjugate(self.goal_rot)
)
# fingertip observations, state(pose and vel) + force-torque sensors
num_ft_states = 13 * self.num_fingertips # 65
num_ft_force_torques = 6 * self.num_fingertips # 30
fingertip_obs_start = goal_obs_start + 11 # 72
self.obs_buf[
:, fingertip_obs_start : fingertip_obs_start + 3 * self.num_fingertips
] = self.fingertip_pos.reshape(self.num_envs, 3 * self.num_fingertips)
self.obs_buf[
:, fingertip_obs_start + 3 * self.num_fingertips : fingertip_obs_start + 7 * self.num_fingertips
] = self.fingertip_rot.reshape(self.num_envs, 4 * self.num_fingertips)
self.obs_buf[
:, fingertip_obs_start + 7 * self.num_fingertips : fingertip_obs_start + 13 * self.num_fingertips
] = self.fingertip_velocities.reshape(self.num_envs, 6 * self.num_fingertips)
self.obs_buf[
:, fingertip_obs_start + num_ft_states : fingertip_obs_start + num_ft_states + num_ft_force_torques
] = (self.force_torque_obs_scale * self.vec_sensor_tensor)
# obs_end = 96 + 65 + 30 = 167
# obs_total = obs_end + num_actions = 187
obs_end = fingertip_obs_start + num_ft_states + num_ft_force_torques
self.obs_buf[:, obs_end : obs_end + self.num_actions] = self.actions
| 15,107 | Python | 48.211726 | 129 | 0.609188 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/franka_cabinet.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
import math
import numpy as np
import torch
from omni.isaac.cloner import Cloner
from omni.isaac.core.objects import DynamicCuboid
from omni.isaac.core.prims import RigidPrim, RigidPrimView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.stage import get_current_stage
from omni.isaac.core.utils.torch.rotations import *
from omni.isaac.core.utils.torch.transformations import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.cabinet import Cabinet
from omniisaacgymenvs.robots.articulations.franka import Franka
from omniisaacgymenvs.robots.articulations.views.cabinet_view import CabinetView
from omniisaacgymenvs.robots.articulations.views.franka_view import FrankaView
from pxr import Usd, UsdGeom
class FrankaCabinetTask(RLTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self.distX_offset = 0.04
self.dt = 1 / 60.0
self._num_observations = 23
self._num_actions = 9
RLTask.__init__(self, name, env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._max_episode_length = self._task_cfg["env"]["episodeLength"]
self.action_scale = self._task_cfg["env"]["actionScale"]
self.start_position_noise = self._task_cfg["env"]["startPositionNoise"]
self.start_rotation_noise = self._task_cfg["env"]["startRotationNoise"]
self.num_props = self._task_cfg["env"]["numProps"]
self.dof_vel_scale = self._task_cfg["env"]["dofVelocityScale"]
self.dist_reward_scale = self._task_cfg["env"]["distRewardScale"]
self.rot_reward_scale = self._task_cfg["env"]["rotRewardScale"]
self.around_handle_reward_scale = self._task_cfg["env"]["aroundHandleRewardScale"]
self.open_reward_scale = self._task_cfg["env"]["openRewardScale"]
self.finger_dist_reward_scale = self._task_cfg["env"]["fingerDistRewardScale"]
self.action_penalty_scale = self._task_cfg["env"]["actionPenaltyScale"]
self.finger_close_reward_scale = self._task_cfg["env"]["fingerCloseRewardScale"]
def set_up_scene(self, scene) -> None:
self.get_franka()
self.get_cabinet()
if self.num_props > 0:
self.get_props()
super().set_up_scene(scene, filter_collisions=False)
self._frankas = FrankaView(prim_paths_expr="/World/envs/.*/franka", name="franka_view")
self._cabinets = CabinetView(prim_paths_expr="/World/envs/.*/cabinet", name="cabinet_view")
scene.add(self._frankas)
scene.add(self._frankas._hands)
scene.add(self._frankas._lfingers)
scene.add(self._frankas._rfingers)
scene.add(self._cabinets)
scene.add(self._cabinets._drawers)
if self.num_props > 0:
self._props = RigidPrimView(
prim_paths_expr="/World/envs/.*/prop/.*", name="prop_view", reset_xform_properties=False
)
scene.add(self._props)
self.init_data()
return
def initialize_views(self, scene):
super().initialize_views(scene)
if scene.object_exists("franka_view"):
scene.remove_object("franka_view", registry_only=True)
if scene.object_exists("hands_view"):
scene.remove_object("hands_view", registry_only=True)
if scene.object_exists("lfingers_view"):
scene.remove_object("lfingers_view", registry_only=True)
if scene.object_exists("rfingers_view"):
scene.remove_object("rfingers_view", registry_only=True)
if scene.object_exists("cabinet_view"):
scene.remove_object("cabinet_view", registry_only=True)
if scene.object_exists("drawers_view"):
scene.remove_object("drawers_view", registry_only=True)
if scene.object_exists("prop_view"):
scene.remove_object("prop_view", registry_only=True)
self._frankas = FrankaView(prim_paths_expr="/World/envs/.*/franka", name="franka_view")
self._cabinets = CabinetView(prim_paths_expr="/World/envs/.*/cabinet", name="cabinet_view")
scene.add(self._frankas)
scene.add(self._frankas._hands)
scene.add(self._frankas._lfingers)
scene.add(self._frankas._rfingers)
scene.add(self._cabinets)
scene.add(self._cabinets._drawers)
if self.num_props > 0:
self._props = RigidPrimView(
prim_paths_expr="/World/envs/.*/prop/.*", name="prop_view", reset_xform_properties=False
)
scene.add(self._props)
self.init_data()
def get_franka(self):
franka = Franka(prim_path=self.default_zero_env_path + "/franka", name="franka")
self._sim_config.apply_articulation_settings(
"franka", get_prim_at_path(franka.prim_path), self._sim_config.parse_actor_config("franka")
)
def get_cabinet(self):
cabinet = Cabinet(self.default_zero_env_path + "/cabinet", name="cabinet")
self._sim_config.apply_articulation_settings(
"cabinet", get_prim_at_path(cabinet.prim_path), self._sim_config.parse_actor_config("cabinet")
)
def get_props(self):
prop_cloner = Cloner()
drawer_pos = torch.tensor([0.0515, 0.0, 0.7172])
prop_color = torch.tensor([0.2, 0.4, 0.6])
props_per_row = int(math.ceil(math.sqrt(self.num_props)))
prop_size = 0.08
prop_spacing = 0.09
xmin = -0.5 * prop_spacing * (props_per_row - 1)
zmin = -0.5 * prop_spacing * (props_per_row - 1)
prop_count = 0
prop_pos = []
for j in range(props_per_row):
prop_up = zmin + j * prop_spacing
for k in range(props_per_row):
if prop_count >= self.num_props:
break
propx = xmin + k * prop_spacing
prop_pos.append([propx, prop_up, 0.0])
prop_count += 1
prop = DynamicCuboid(
prim_path=self.default_zero_env_path + "/prop/prop_0",
name="prop",
color=prop_color,
size=prop_size,
density=100.0,
)
self._sim_config.apply_articulation_settings(
"prop", get_prim_at_path(prop.prim_path), self._sim_config.parse_actor_config("prop")
)
prop_paths = [f"{self.default_zero_env_path}/prop/prop_{j}" for j in range(self.num_props)]
prop_cloner.clone(
source_prim_path=self.default_zero_env_path + "/prop/prop_0",
prim_paths=prop_paths,
positions=np.array(prop_pos) + drawer_pos.numpy(),
replicate_physics=False,
)
def init_data(self) -> None:
def get_env_local_pose(env_pos, xformable, device):
"""Compute pose in env-local coordinates"""
world_transform = xformable.ComputeLocalToWorldTransform(0)
world_pos = world_transform.ExtractTranslation()
world_quat = world_transform.ExtractRotationQuat()
px = world_pos[0] - env_pos[0]
py = world_pos[1] - env_pos[1]
pz = world_pos[2] - env_pos[2]
qx = world_quat.imaginary[0]
qy = world_quat.imaginary[1]
qz = world_quat.imaginary[2]
qw = world_quat.real
return torch.tensor([px, py, pz, qw, qx, qy, qz], device=device, dtype=torch.float)
stage = get_current_stage()
hand_pose = get_env_local_pose(
self._env_pos[0],
UsdGeom.Xformable(stage.GetPrimAtPath("/World/envs/env_0/franka/panda_link7")),
self._device,
)
lfinger_pose = get_env_local_pose(
self._env_pos[0],
UsdGeom.Xformable(stage.GetPrimAtPath("/World/envs/env_0/franka/panda_leftfinger")),
self._device,
)
rfinger_pose = get_env_local_pose(
self._env_pos[0],
UsdGeom.Xformable(stage.GetPrimAtPath("/World/envs/env_0/franka/panda_rightfinger")),
self._device,
)
finger_pose = torch.zeros(7, device=self._device)
finger_pose[0:3] = (lfinger_pose[0:3] + rfinger_pose[0:3]) / 2.0
finger_pose[3:7] = lfinger_pose[3:7]
hand_pose_inv_rot, hand_pose_inv_pos = tf_inverse(hand_pose[3:7], hand_pose[0:3])
grasp_pose_axis = 1
franka_local_grasp_pose_rot, franka_local_pose_pos = tf_combine(
hand_pose_inv_rot, hand_pose_inv_pos, finger_pose[3:7], finger_pose[0:3]
)
franka_local_pose_pos += torch.tensor([0, 0.04, 0], device=self._device)
self.franka_local_grasp_pos = franka_local_pose_pos.repeat((self._num_envs, 1))
self.franka_local_grasp_rot = franka_local_grasp_pose_rot.repeat((self._num_envs, 1))
drawer_local_grasp_pose = torch.tensor([0.3, 0.01, 0.0, 1.0, 0.0, 0.0, 0.0], device=self._device)
self.drawer_local_grasp_pos = drawer_local_grasp_pose[0:3].repeat((self._num_envs, 1))
self.drawer_local_grasp_rot = drawer_local_grasp_pose[3:7].repeat((self._num_envs, 1))
self.gripper_forward_axis = torch.tensor([0, 0, 1], device=self._device, dtype=torch.float).repeat(
(self._num_envs, 1)
)
self.drawer_inward_axis = torch.tensor([-1, 0, 0], device=self._device, dtype=torch.float).repeat(
(self._num_envs, 1)
)
self.gripper_up_axis = torch.tensor([0, 1, 0], device=self._device, dtype=torch.float).repeat(
(self._num_envs, 1)
)
self.drawer_up_axis = torch.tensor([0, 0, 1], device=self._device, dtype=torch.float).repeat(
(self._num_envs, 1)
)
self.franka_default_dof_pos = torch.tensor(
[1.157, -1.066, -0.155, -2.239, -1.841, 1.003, 0.469, 0.035, 0.035], device=self._device
)
self.actions = torch.zeros((self._num_envs, self.num_actions), device=self._device)
def get_observations(self) -> dict:
hand_pos, hand_rot = self._frankas._hands.get_world_poses(clone=False)
drawer_pos, drawer_rot = self._cabinets._drawers.get_world_poses(clone=False)
franka_dof_pos = self._frankas.get_joint_positions(clone=False)
franka_dof_vel = self._frankas.get_joint_velocities(clone=False)
self.cabinet_dof_pos = self._cabinets.get_joint_positions(clone=False)
self.cabinet_dof_vel = self._cabinets.get_joint_velocities(clone=False)
self.franka_dof_pos = franka_dof_pos
(
self.franka_grasp_rot,
self.franka_grasp_pos,
self.drawer_grasp_rot,
self.drawer_grasp_pos,
) = self.compute_grasp_transforms(
hand_rot,
hand_pos,
self.franka_local_grasp_rot,
self.franka_local_grasp_pos,
drawer_rot,
drawer_pos,
self.drawer_local_grasp_rot,
self.drawer_local_grasp_pos,
)
self.franka_lfinger_pos, self.franka_lfinger_rot = self._frankas._lfingers.get_world_poses(clone=False)
self.franka_rfinger_pos, self.franka_rfinger_rot = self._frankas._lfingers.get_world_poses(clone=False)
dof_pos_scaled = (
2.0
* (franka_dof_pos - self.franka_dof_lower_limits)
/ (self.franka_dof_upper_limits - self.franka_dof_lower_limits)
- 1.0
)
to_target = self.drawer_grasp_pos - self.franka_grasp_pos
self.obs_buf = torch.cat(
(
dof_pos_scaled,
franka_dof_vel * self.dof_vel_scale,
to_target,
self.cabinet_dof_pos[:, 3].unsqueeze(-1),
self.cabinet_dof_vel[:, 3].unsqueeze(-1),
),
dim=-1,
)
observations = {self._frankas.name: {"obs_buf": self.obs_buf}}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
self.actions = actions.clone().to(self._device)
targets = self.franka_dof_targets + self.franka_dof_speed_scales * self.dt * self.actions * self.action_scale
self.franka_dof_targets[:] = tensor_clamp(targets, self.franka_dof_lower_limits, self.franka_dof_upper_limits)
env_ids_int32 = torch.arange(self._frankas.count, dtype=torch.int32, device=self._device)
self._frankas.set_joint_position_targets(self.franka_dof_targets, indices=env_ids_int32)
def reset_idx(self, env_ids):
indices = env_ids.to(dtype=torch.int32)
num_indices = len(indices)
# reset franka
pos = tensor_clamp(
self.franka_default_dof_pos.unsqueeze(0)
+ 0.25 * (torch.rand((len(env_ids), self.num_franka_dofs), device=self._device) - 0.5),
self.franka_dof_lower_limits,
self.franka_dof_upper_limits,
)
dof_pos = torch.zeros((num_indices, self._frankas.num_dof), device=self._device)
dof_vel = torch.zeros((num_indices, self._frankas.num_dof), device=self._device)
dof_pos[:, :] = pos
self.franka_dof_targets[env_ids, :] = pos
self.franka_dof_pos[env_ids, :] = pos
# reset cabinet
self._cabinets.set_joint_positions(
torch.zeros_like(self._cabinets.get_joint_positions(clone=False)[env_ids]), indices=indices
)
self._cabinets.set_joint_velocities(
torch.zeros_like(self._cabinets.get_joint_velocities(clone=False)[env_ids]), indices=indices
)
# reset props
if self.num_props > 0:
self._props.set_world_poses(
self.default_prop_pos[self.prop_indices[env_ids].flatten()],
self.default_prop_rot[self.prop_indices[env_ids].flatten()],
self.prop_indices[env_ids].flatten().to(torch.int32),
)
self._frankas.set_joint_position_targets(self.franka_dof_targets[env_ids], indices=indices)
self._frankas.set_joint_positions(dof_pos, indices=indices)
self._frankas.set_joint_velocities(dof_vel, indices=indices)
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def post_reset(self):
self.num_franka_dofs = self._frankas.num_dof
self.franka_dof_pos = torch.zeros((self.num_envs, self.num_franka_dofs), device=self._device)
dof_limits = self._frankas.get_dof_limits()
self.franka_dof_lower_limits = dof_limits[0, :, 0].to(device=self._device)
self.franka_dof_upper_limits = dof_limits[0, :, 1].to(device=self._device)
self.franka_dof_speed_scales = torch.ones_like(self.franka_dof_lower_limits)
self.franka_dof_speed_scales[self._frankas.gripper_indices] = 0.1
self.franka_dof_targets = torch.zeros(
(self._num_envs, self.num_franka_dofs), dtype=torch.float, device=self._device
)
if self.num_props > 0:
self.default_prop_pos, self.default_prop_rot = self._props.get_world_poses()
self.prop_indices = torch.arange(self._num_envs * self.num_props, device=self._device).view(
self._num_envs, self.num_props
)
# randomize all envs
indices = torch.arange(self._num_envs, dtype=torch.int64, device=self._device)
self.reset_idx(indices)
def calculate_metrics(self) -> None:
self.rew_buf[:] = self.compute_franka_reward(
self.reset_buf,
self.progress_buf,
self.actions,
self.cabinet_dof_pos,
self.franka_grasp_pos,
self.drawer_grasp_pos,
self.franka_grasp_rot,
self.drawer_grasp_rot,
self.franka_lfinger_pos,
self.franka_rfinger_pos,
self.gripper_forward_axis,
self.drawer_inward_axis,
self.gripper_up_axis,
self.drawer_up_axis,
self._num_envs,
self.dist_reward_scale,
self.rot_reward_scale,
self.around_handle_reward_scale,
self.open_reward_scale,
self.finger_dist_reward_scale,
self.action_penalty_scale,
self.distX_offset,
self._max_episode_length,
self.franka_dof_pos,
self.finger_close_reward_scale,
)
def is_done(self) -> None:
# reset if drawer is open or max length reached
self.reset_buf = torch.where(self.cabinet_dof_pos[:, 3] > 0.39, torch.ones_like(self.reset_buf), self.reset_buf)
self.reset_buf = torch.where(
self.progress_buf >= self._max_episode_length - 1, torch.ones_like(self.reset_buf), self.reset_buf
)
def compute_grasp_transforms(
self,
hand_rot,
hand_pos,
franka_local_grasp_rot,
franka_local_grasp_pos,
drawer_rot,
drawer_pos,
drawer_local_grasp_rot,
drawer_local_grasp_pos,
):
global_franka_rot, global_franka_pos = tf_combine(
hand_rot, hand_pos, franka_local_grasp_rot, franka_local_grasp_pos
)
global_drawer_rot, global_drawer_pos = tf_combine(
drawer_rot, drawer_pos, drawer_local_grasp_rot, drawer_local_grasp_pos
)
return global_franka_rot, global_franka_pos, global_drawer_rot, global_drawer_pos
def compute_franka_reward(
self,
reset_buf,
progress_buf,
actions,
cabinet_dof_pos,
franka_grasp_pos,
drawer_grasp_pos,
franka_grasp_rot,
drawer_grasp_rot,
franka_lfinger_pos,
franka_rfinger_pos,
gripper_forward_axis,
drawer_inward_axis,
gripper_up_axis,
drawer_up_axis,
num_envs,
dist_reward_scale,
rot_reward_scale,
around_handle_reward_scale,
open_reward_scale,
finger_dist_reward_scale,
action_penalty_scale,
distX_offset,
max_episode_length,
joint_positions,
finger_close_reward_scale,
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, int, float, float, float, float, float, float, float, float, Tensor) -> Tuple[Tensor, Tensor]
# distance from hand to the drawer
d = torch.norm(franka_grasp_pos - drawer_grasp_pos, p=2, dim=-1)
dist_reward = 1.0 / (1.0 + d**2)
dist_reward *= dist_reward
dist_reward = torch.where(d <= 0.02, dist_reward * 2, dist_reward)
axis1 = tf_vector(franka_grasp_rot, gripper_forward_axis)
axis2 = tf_vector(drawer_grasp_rot, drawer_inward_axis)
axis3 = tf_vector(franka_grasp_rot, gripper_up_axis)
axis4 = tf_vector(drawer_grasp_rot, drawer_up_axis)
dot1 = (
torch.bmm(axis1.view(num_envs, 1, 3), axis2.view(num_envs, 3, 1)).squeeze(-1).squeeze(-1)
) # alignment of forward axis for gripper
dot2 = (
torch.bmm(axis3.view(num_envs, 1, 3), axis4.view(num_envs, 3, 1)).squeeze(-1).squeeze(-1)
) # alignment of up axis for gripper
# reward for matching the orientation of the hand to the drawer (fingers wrapped)
rot_reward = 0.5 * (torch.sign(dot1) * dot1**2 + torch.sign(dot2) * dot2**2)
# bonus if left finger is above the drawer handle and right below
around_handle_reward = torch.zeros_like(rot_reward)
around_handle_reward = torch.where(
franka_lfinger_pos[:, 2] > drawer_grasp_pos[:, 2],
torch.where(
franka_rfinger_pos[:, 2] < drawer_grasp_pos[:, 2], around_handle_reward + 0.5, around_handle_reward
),
around_handle_reward,
)
# reward for distance of each finger from the drawer
finger_dist_reward = torch.zeros_like(rot_reward)
lfinger_dist = torch.abs(franka_lfinger_pos[:, 2] - drawer_grasp_pos[:, 2])
rfinger_dist = torch.abs(franka_rfinger_pos[:, 2] - drawer_grasp_pos[:, 2])
finger_dist_reward = torch.where(
franka_lfinger_pos[:, 2] > drawer_grasp_pos[:, 2],
torch.where(
franka_rfinger_pos[:, 2] < drawer_grasp_pos[:, 2],
(0.04 - lfinger_dist) + (0.04 - rfinger_dist),
finger_dist_reward,
),
finger_dist_reward,
)
finger_close_reward = torch.zeros_like(rot_reward)
finger_close_reward = torch.where(
d <= 0.03, (0.04 - joint_positions[:, 7]) + (0.04 - joint_positions[:, 8]), finger_close_reward
)
# regularization on the actions (summed for each environment)
action_penalty = torch.sum(actions**2, dim=-1)
# how far the cabinet has been opened out
open_reward = cabinet_dof_pos[:, 3] * around_handle_reward + cabinet_dof_pos[:, 3] # drawer_top_joint
rewards = (
dist_reward_scale * dist_reward
+ rot_reward_scale * rot_reward
+ around_handle_reward_scale * around_handle_reward
+ open_reward_scale * open_reward
+ finger_dist_reward_scale * finger_dist_reward
- action_penalty_scale * action_penalty
+ finger_close_reward * finger_close_reward_scale
)
# bonus for opening drawer properly
rewards = torch.where(cabinet_dof_pos[:, 3] > 0.01, rewards + 0.5, rewards)
rewards = torch.where(cabinet_dof_pos[:, 3] > 0.2, rewards + around_handle_reward, rewards)
rewards = torch.where(cabinet_dof_pos[:, 3] > 0.39, rewards + (2.0 * around_handle_reward), rewards)
# # prevent bad style in opening drawer
# rewards = torch.where(franka_lfinger_pos[:, 0] < drawer_grasp_pos[:, 0] - distX_offset,
# torch.ones_like(rewards) * -1, rewards)
# rewards = torch.where(franka_rfinger_pos[:, 0] < drawer_grasp_pos[:, 0] - distX_offset,
# torch.ones_like(rewards) * -1, rewards)
return rewards
| 22,933 | Python | 41.313653 | 222 | 0.599922 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/crazyflie.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import numpy as np
import torch
from omni.isaac.core.objects import DynamicSphere
from omni.isaac.core.prims import RigidPrimView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.torch.rotations import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.crazyflie import Crazyflie
from omniisaacgymenvs.robots.articulations.views.crazyflie_view import CrazyflieView
EPS = 1e-6 # small constant to avoid divisions by 0 and log(0)
class CrazyflieTask(RLTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self._num_observations = 18
self._num_actions = 4
self._crazyflie_position = torch.tensor([0, 0, 1.0])
self._ball_position = torch.tensor([0, 0, 1.0])
RLTask.__init__(self, name=name, env=env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._max_episode_length = self._task_cfg["env"]["maxEpisodeLength"]
self.dt = self._task_cfg["sim"]["dt"]
# parameters for the crazyflie
self.arm_length = 0.05
# parameters for the controller
self.motor_damp_time_up = 0.15
self.motor_damp_time_down = 0.15
# I use the multiplier 4, since 4*T ~ time for a step response to finish, where
# T is a time constant of the first-order filter
self.motor_tau_up = 4 * self.dt / (self.motor_damp_time_up + EPS)
self.motor_tau_down = 4 * self.dt / (self.motor_damp_time_down + EPS)
# thrust max
self.mass = 0.028
self.thrust_to_weight = 1.9
self.motor_assymetry = np.array([1.0, 1.0, 1.0, 1.0])
# re-normalizing to sum-up to 4
self.motor_assymetry = self.motor_assymetry * 4.0 / np.sum(self.motor_assymetry)
self.grav_z = -1.0 * self._task_cfg["sim"]["gravity"][2]
def set_up_scene(self, scene) -> None:
self.get_crazyflie()
self.get_target()
RLTask.set_up_scene(self, scene)
self._copters = CrazyflieView(prim_paths_expr="/World/envs/.*/Crazyflie", name="crazyflie_view")
self._balls = RigidPrimView(prim_paths_expr="/World/envs/.*/ball", name="ball_view")
scene.add(self._copters)
scene.add(self._balls)
for i in range(4):
scene.add(self._copters.physics_rotors[i])
return
def initialize_views(self, scene):
super().initialize_views(scene)
if scene.object_exists("crazyflie_view"):
scene.remove_object("crazyflie_view", registry_only=True)
if scene.object_exists("ball_view"):
scene.remove_object("ball_view", registry_only=True)
for i in range(1, 5):
scene.remove_object(f"m{i}_prop_view", registry_only=True)
self._copters = CrazyflieView(prim_paths_expr="/World/envs/.*/Crazyflie", name="crazyflie_view")
self._balls = RigidPrimView(prim_paths_expr="/World/envs/.*/ball", name="ball_view")
scene.add(self._copters)
scene.add(self._balls)
for i in range(4):
scene.add(self._copters.physics_rotors[i])
def get_crazyflie(self):
copter = Crazyflie(
prim_path=self.default_zero_env_path + "/Crazyflie", name="crazyflie", translation=self._crazyflie_position
)
self._sim_config.apply_articulation_settings(
"crazyflie", get_prim_at_path(copter.prim_path), self._sim_config.parse_actor_config("crazyflie")
)
def get_target(self):
radius = 0.2
color = torch.tensor([1, 0, 0])
ball = DynamicSphere(
prim_path=self.default_zero_env_path + "/ball",
translation=self._ball_position,
name="target_0",
radius=radius,
color=color,
)
self._sim_config.apply_articulation_settings(
"ball", get_prim_at_path(ball.prim_path), self._sim_config.parse_actor_config("ball")
)
ball.set_collision_enabled(False)
def get_observations(self) -> dict:
self.root_pos, self.root_rot = self._copters.get_world_poses(clone=False)
self.root_velocities = self._copters.get_velocities(clone=False)
root_positions = self.root_pos - self._env_pos
root_quats = self.root_rot
rot_x = quat_axis(root_quats, 0)
rot_y = quat_axis(root_quats, 1)
rot_z = quat_axis(root_quats, 2)
root_linvels = self.root_velocities[:, :3]
root_angvels = self.root_velocities[:, 3:]
self.obs_buf[..., 0:3] = self.target_positions - root_positions
self.obs_buf[..., 3:6] = rot_x
self.obs_buf[..., 6:9] = rot_y
self.obs_buf[..., 9:12] = rot_z
self.obs_buf[..., 12:15] = root_linvels
self.obs_buf[..., 15:18] = root_angvels
observations = {self._copters.name: {"obs_buf": self.obs_buf}}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
set_target_ids = (self.progress_buf % 500 == 0).nonzero(as_tuple=False).squeeze(-1)
if len(set_target_ids) > 0:
self.set_targets(set_target_ids)
actions = actions.clone().to(self._device)
self.actions = actions
# clamp to [-1.0, 1.0]
thrust_cmds = torch.clamp(actions, min=-1.0, max=1.0)
# scale to [0.0, 1.0]
thrust_cmds = (thrust_cmds + 1.0) / 2.0
# filtering the thruster and adding noise
motor_tau = self.motor_tau_up * torch.ones((self._num_envs, 4), dtype=torch.float32, device=self._device)
motor_tau[thrust_cmds < self.thrust_cmds_damp] = self.motor_tau_down
motor_tau[motor_tau > 1.0] = 1.0
# Since NN commands thrusts we need to convert to rot vel and back
thrust_rot = thrust_cmds**0.5
self.thrust_rot_damp = motor_tau * (thrust_rot - self.thrust_rot_damp) + self.thrust_rot_damp
self.thrust_cmds_damp = self.thrust_rot_damp**2
## Adding noise
thrust_noise = 0.01 * torch.randn(4, dtype=torch.float32, device=self._device)
thrust_noise = thrust_cmds * thrust_noise
self.thrust_cmds_damp = torch.clamp(self.thrust_cmds_damp + thrust_noise, min=0.0, max=1.0)
thrusts = self.thrust_max * self.thrust_cmds_damp
# thrusts given rotation
root_quats = self.root_rot
rot_x = quat_axis(root_quats, 0)
rot_y = quat_axis(root_quats, 1)
rot_z = quat_axis(root_quats, 2)
rot_matrix = torch.cat((rot_x, rot_y, rot_z), 1).reshape(-1, 3, 3)
force_x = torch.zeros(self._num_envs, 4, dtype=torch.float32, device=self._device)
force_y = torch.zeros(self._num_envs, 4, dtype=torch.float32, device=self._device)
force_xy = torch.cat((force_x, force_y), 1).reshape(-1, 4, 2)
thrusts = thrusts.reshape(-1, 4, 1)
thrusts = torch.cat((force_xy, thrusts), 2)
thrusts_0 = thrusts[:, 0]
thrusts_0 = thrusts_0[:, :, None]
thrusts_1 = thrusts[:, 1]
thrusts_1 = thrusts_1[:, :, None]
thrusts_2 = thrusts[:, 2]
thrusts_2 = thrusts_2[:, :, None]
thrusts_3 = thrusts[:, 3]
thrusts_3 = thrusts_3[:, :, None]
mod_thrusts_0 = torch.matmul(rot_matrix, thrusts_0)
mod_thrusts_1 = torch.matmul(rot_matrix, thrusts_1)
mod_thrusts_2 = torch.matmul(rot_matrix, thrusts_2)
mod_thrusts_3 = torch.matmul(rot_matrix, thrusts_3)
self.thrusts[:, 0] = torch.squeeze(mod_thrusts_0)
self.thrusts[:, 1] = torch.squeeze(mod_thrusts_1)
self.thrusts[:, 2] = torch.squeeze(mod_thrusts_2)
self.thrusts[:, 3] = torch.squeeze(mod_thrusts_3)
# clear actions for reset envs
self.thrusts[reset_env_ids] = 0
# spin spinning rotors
prop_rot = self.thrust_cmds_damp * self.prop_max_rot
self.dof_vel[:, 0] = prop_rot[:, 0]
self.dof_vel[:, 1] = -1.0 * prop_rot[:, 1]
self.dof_vel[:, 2] = prop_rot[:, 2]
self.dof_vel[:, 3] = -1.0 * prop_rot[:, 3]
self._copters.set_joint_velocities(self.dof_vel)
# apply actions
for i in range(4):
self._copters.physics_rotors[i].apply_forces(self.thrusts[:, i], indices=self.all_indices)
def post_reset(self):
thrust_max = self.grav_z * self.mass * self.thrust_to_weight * self.motor_assymetry / 4.0
self.thrusts = torch.zeros((self._num_envs, 4, 3), dtype=torch.float32, device=self._device)
self.thrust_cmds_damp = torch.zeros((self._num_envs, 4), dtype=torch.float32, device=self._device)
self.thrust_rot_damp = torch.zeros((self._num_envs, 4), dtype=torch.float32, device=self._device)
self.thrust_max = torch.tensor(thrust_max, device=self._device, dtype=torch.float32)
self.motor_linearity = 1.0
self.prop_max_rot = 433.3
self.target_positions = torch.zeros((self._num_envs, 3), device=self._device, dtype=torch.float32)
self.target_positions[:, 2] = 1
self.actions = torch.zeros((self._num_envs, 4), device=self._device, dtype=torch.float32)
self.all_indices = torch.arange(self._num_envs, dtype=torch.int32, device=self._device)
# Extra info
self.extras = {}
torch_zeros = lambda: torch.zeros(self.num_envs, dtype=torch.float, device=self.device, requires_grad=False)
self.episode_sums = {
"rew_pos": torch_zeros(),
"rew_orient": torch_zeros(),
"rew_effort": torch_zeros(),
"rew_spin": torch_zeros(),
"raw_dist": torch_zeros(),
"raw_orient": torch_zeros(),
"raw_effort": torch_zeros(),
"raw_spin": torch_zeros(),
}
self.root_pos, self.root_rot = self._copters.get_world_poses()
self.root_velocities = self._copters.get_velocities()
self.dof_pos = self._copters.get_joint_positions()
self.dof_vel = self._copters.get_joint_velocities()
self.initial_ball_pos, self.initial_ball_rot = self._balls.get_world_poses(clone=False)
self.initial_root_pos, self.initial_root_rot = self.root_pos.clone(), self.root_rot.clone()
# control parameters
self.thrusts = torch.zeros((self._num_envs, 4, 3), dtype=torch.float32, device=self._device)
self.thrust_cmds_damp = torch.zeros((self._num_envs, 4), dtype=torch.float32, device=self._device)
self.thrust_rot_damp = torch.zeros((self._num_envs, 4), dtype=torch.float32, device=self._device)
self.set_targets(self.all_indices)
def set_targets(self, env_ids):
num_sets = len(env_ids)
envs_long = env_ids.long()
# set target position randomly with x, y in (0, 0) and z in (2)
self.target_positions[envs_long, 0:2] = torch.zeros((num_sets, 2), device=self._device)
self.target_positions[envs_long, 2] = torch.ones(num_sets, device=self._device) * 2.0
# shift the target up so it visually aligns better
ball_pos = self.target_positions[envs_long] + self._env_pos[envs_long]
ball_pos[:, 2] += 0.0
self._balls.set_world_poses(ball_pos[:, 0:3], self.initial_ball_rot[envs_long].clone(), indices=env_ids)
def reset_idx(self, env_ids):
num_resets = len(env_ids)
self.dof_pos[env_ids, :] = torch_rand_float(-0.0, 0.0, (num_resets, self._copters.num_dof), device=self._device)
self.dof_vel[env_ids, :] = 0
root_pos = self.initial_root_pos.clone()
root_pos[env_ids, 0] += torch_rand_float(-0.0, 0.0, (num_resets, 1), device=self._device).view(-1)
root_pos[env_ids, 1] += torch_rand_float(-0.0, 0.0, (num_resets, 1), device=self._device).view(-1)
root_pos[env_ids, 2] += torch_rand_float(-0.0, 0.0, (num_resets, 1), device=self._device).view(-1)
root_velocities = self.root_velocities.clone()
root_velocities[env_ids] = 0
# apply resets
self._copters.set_joint_positions(self.dof_pos[env_ids], indices=env_ids)
self._copters.set_joint_velocities(self.dof_vel[env_ids], indices=env_ids)
self._copters.set_world_poses(root_pos[env_ids], self.initial_root_rot[env_ids].clone(), indices=env_ids)
self._copters.set_velocities(root_velocities[env_ids], indices=env_ids)
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
self.thrust_cmds_damp[env_ids] = 0
self.thrust_rot_damp[env_ids] = 0
# fill extras
self.extras["episode"] = {}
for key in self.episode_sums.keys():
self.extras["episode"][key] = torch.mean(self.episode_sums[key][env_ids]) / self._max_episode_length
self.episode_sums[key][env_ids] = 0.0
def calculate_metrics(self) -> None:
root_positions = self.root_pos - self._env_pos
root_quats = self.root_rot
root_angvels = self.root_velocities[:, 3:]
# pos reward
target_dist = torch.sqrt(torch.square(self.target_positions - root_positions).sum(-1))
pos_reward = 1.0 / (1.0 + target_dist)
self.target_dist = target_dist
self.root_positions = root_positions
# orient reward
ups = quat_axis(root_quats, 2)
self.orient_z = ups[..., 2]
up_reward = torch.clamp(ups[..., 2], min=0.0, max=1.0)
# effort reward
effort = torch.square(self.actions).sum(-1)
effort_reward = 0.05 * torch.exp(-0.5 * effort)
# spin reward
spin = torch.square(root_angvels).sum(-1)
spin_reward = 0.01 * torch.exp(-1.0 * spin)
# combined reward
self.rew_buf[:] = pos_reward + pos_reward * (up_reward + spin_reward) - effort_reward
# log episode reward sums
self.episode_sums["rew_pos"] += pos_reward
self.episode_sums["rew_orient"] += up_reward
self.episode_sums["rew_effort"] += effort_reward
self.episode_sums["rew_spin"] += spin_reward
# log raw info
self.episode_sums["raw_dist"] += target_dist
self.episode_sums["raw_orient"] += ups[..., 2]
self.episode_sums["raw_effort"] += effort
self.episode_sums["raw_spin"] += spin
def is_done(self) -> None:
# resets due to misbehavior
ones = torch.ones_like(self.reset_buf)
die = torch.zeros_like(self.reset_buf)
die = torch.where(self.target_dist > 5.0, ones, die)
# z >= 0.5 & z <= 5.0 & up > 0
die = torch.where(self.root_positions[..., 2] < 0.5, ones, die)
die = torch.where(self.root_positions[..., 2] > 5.0, ones, die)
die = torch.where(self.orient_z < 0.0, ones, die)
# resets due to episode length
self.reset_buf[:] = torch.where(self.progress_buf >= self._max_episode_length - 1, ones, die)
| 16,824 | Python | 41.487374 | 120 | 0.619413 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/humanoid.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.torch.maths import tensor_clamp, torch_rand_float, unscale
from omni.isaac.core.utils.torch.rotations import compute_heading_and_up, compute_rot, quat_conjugate
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.humanoid import Humanoid
from omniisaacgymenvs.tasks.shared.locomotion import LocomotionTask
from pxr import PhysxSchema
class HumanoidLocomotionTask(LocomotionTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self._num_observations = 87
self._num_actions = 21
self._humanoid_positions = torch.tensor([0, 0, 1.34])
LocomotionTask.__init__(self, name=name, env=env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
LocomotionTask.update_config(self)
def set_up_scene(self, scene) -> None:
self.get_humanoid()
RLTask.set_up_scene(self, scene)
self._humanoids = ArticulationView(
prim_paths_expr="/World/envs/.*/Humanoid/torso", name="humanoid_view", reset_xform_properties=False
)
scene.add(self._humanoids)
return
def initialize_views(self, scene):
RLTask.initialize_views(self, scene)
if scene.object_exists("humanoid_view"):
scene.remove_object("humanoid_view", registry_only=True)
self._humanoids = ArticulationView(
prim_paths_expr="/World/envs/.*/Humanoid/torso", name="humanoid_view", reset_xform_properties=False
)
scene.add(self._humanoids)
def get_humanoid(self):
humanoid = Humanoid(
prim_path=self.default_zero_env_path + "/Humanoid", name="Humanoid", translation=self._humanoid_positions
)
self._sim_config.apply_articulation_settings(
"Humanoid", get_prim_at_path(humanoid.prim_path), self._sim_config.parse_actor_config("Humanoid")
)
def get_robot(self):
return self._humanoids
def post_reset(self):
self.joint_gears = torch.tensor(
[
67.5000, # lower_waist
67.5000, # lower_waist
67.5000, # right_upper_arm
67.5000, # right_upper_arm
67.5000, # left_upper_arm
67.5000, # left_upper_arm
67.5000, # pelvis
45.0000, # right_lower_arm
45.0000, # left_lower_arm
45.0000, # right_thigh: x
135.0000, # right_thigh: y
45.0000, # right_thigh: z
45.0000, # left_thigh: x
135.0000, # left_thigh: y
45.0000, # left_thigh: z
90.0000, # right_knee
90.0000, # left_knee
22.5, # right_foot
22.5, # right_foot
22.5, # left_foot
22.5, # left_foot
],
device=self._device,
)
self.max_motor_effort = torch.max(self.joint_gears)
self.motor_effort_ratio = self.joint_gears / self.max_motor_effort
dof_limits = self._humanoids.get_dof_limits()
self.dof_limits_lower = dof_limits[0, :, 0].to(self._device)
self.dof_limits_upper = dof_limits[0, :, 1].to(self._device)
force_links = ["left_foot", "right_foot"]
self._sensor_indices = torch.tensor(
[self._humanoids._body_indices[j] for j in force_links], device=self._device, dtype=torch.long
)
LocomotionTask.post_reset(self)
def get_dof_at_limit_cost(self):
return get_dof_at_limit_cost(self.obs_buf, self.motor_effort_ratio, self.joints_at_limit_cost_scale)
@torch.jit.script
def get_dof_at_limit_cost(obs_buf, motor_effort_ratio, joints_at_limit_cost_scale):
# type: (Tensor, Tensor, float) -> Tensor
scaled_cost = joints_at_limit_cost_scale * (torch.abs(obs_buf[:, 12:33]) - 0.98) / 0.02
dof_at_limit_cost = torch.sum(
(torch.abs(obs_buf[:, 12:33]) > 0.98) * scaled_cost * motor_effort_ratio.unsqueeze(0), dim=-1
)
return dof_at_limit_cost
| 5,980 | Python | 41.119718 | 117 | 0.651003 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/franka_deformable.py | # Copyright (c) 2021, NVIDIA CORPORATION. All rights reserved.
#
# NVIDIA CORPORATION and its licensors retain all intellectual property
# and proprietary rights in and to this software, related documentation
# and any modifications thereto. Any use, reproduction, disclosure or
# distribution of this software and related documentation without an express
# license agreement from NVIDIA CORPORATION is strictly prohibited.
#
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.franka import Franka
from omniisaacgymenvs.robots.articulations.views.franka_view import FrankaView
from omni.isaac.core.prims import RigidPrim, RigidPrimView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.stage import get_current_stage, add_reference_to_stage
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.torch.transformations import *
from omni.isaac.core.utils.torch.rotations import *
import omni.isaac.core.utils.deformable_mesh_utils as deformableMeshUtils
from omni.isaac.core.materials.deformable_material import DeformableMaterial
from omni.isaac.core.prims.soft.deformable_prim import DeformablePrim
from omni.isaac.core.prims.soft.deformable_prim_view import DeformablePrimView
from omni.physx.scripts import deformableUtils, physicsUtils
import numpy as np
import torch
import math
from pxr import Usd, UsdGeom, Gf, UsdPhysics, PhysxSchema
class FrankaDeformableTask(RLTask):
def __init__(
self,
name,
sim_config,
env,
offset=None
) -> None:
self.update_config(sim_config)
self.dt = 1/60.
self._num_observations = 39
self._num_actions = 9
RLTask.__init__(self, name, env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._max_episode_length = self._task_cfg["env"]["episodeLength"]
self.dof_vel_scale = self._task_cfg["env"]["dofVelocityScale"]
self.action_scale = self._task_cfg["env"]["actionScale"]
def set_up_scene(self, scene) -> None:
self.stage = get_current_stage()
self.assets_root_path = get_assets_root_path()
if self.assets_root_path is None:
carb.log_error("Could not find Isaac Sim assets folder")
self.get_franka()
self.get_beaker()
self.get_deformable_tube()
super().set_up_scene(scene=scene, replicate_physics=False)
self._frankas = FrankaView(prim_paths_expr="/World/envs/.*/franka", name="franka_view")
self.deformableView = DeformablePrimView(
prim_paths_expr="/World/envs/.*/deformableTube/tube/mesh", name="deformabletube_view"
)
scene.add(self.deformableView)
scene.add(self._frankas)
scene.add(self._frankas._hands)
scene.add(self._frankas._lfingers)
scene.add(self._frankas._rfingers)
return
def initialize_views(self, scene):
super().initialize_views(scene)
if scene.object_exists("franka_view"):
scene.remove_object("franka_view", registry_only=True)
if scene.object_exists("hands_view"):
scene.remove_object("hands_view", registry_only=True)
if scene.object_exists("lfingers_view"):
scene.remove_object("lfingers_view", registry_only=True)
if scene.object_exists("rfingers_view"):
scene.remove_object("rfingers_view", registry_only=True)
if scene.object_exists("deformabletube_view"):
scene.remove_object("deformabletube_view", registry_only=True)
self._frankas = FrankaView(
prim_paths_expr="/World/envs/.*/franka", name="franka_view"
)
self.deformableView = DeformablePrimView(
prim_paths_expr="/World/envs/.*/deformableTube/tube/mesh", name="deformabletube_view"
)
scene.add(self._frankas)
scene.add(self._frankas._hands)
scene.add(self._frankas._lfingers)
scene.add(self._frankas._rfingers)
scene.add(self.deformableView)
def get_franka(self):
franka = Franka(
prim_path=self.default_zero_env_path + "/franka",
name="franka",
orientation=torch.tensor([1.0, 0.0, 0.0, 0.0]),
translation=torch.tensor([0.0, 0.0, 0.0]),
)
self._sim_config.apply_articulation_settings(
"franka", get_prim_at_path(franka.prim_path), self._sim_config.parse_actor_config("franka")
)
franka.set_franka_properties(stage=self.stage, prim=franka.prim)
def get_beaker(self):
_usd_path = self.assets_root_path + "/Isaac/Props/Beaker/beaker_500ml.usd"
mesh_path = self.default_zero_env_path + "/beaker"
add_reference_to_stage(_usd_path, mesh_path)
beaker = RigidPrim(
prim_path=mesh_path+"/beaker",
name="beaker",
position=torch.tensor([0.5, 0.2, 0.095]),
)
self._sim_config.apply_articulation_settings("beaker", beaker.prim, self._sim_config.parse_actor_config("beaker"))
def get_deformable_tube(self):
_usd_path = self.assets_root_path + "/Isaac/Props/DeformableTube/tube.usd"
mesh_path = self.default_zero_env_path + "/deformableTube/tube"
add_reference_to_stage(_usd_path, mesh_path)
skin_mesh = get_prim_at_path(mesh_path)
physicsUtils.setup_transform_as_scale_orient_translate(skin_mesh)
physicsUtils.set_or_add_translate_op(skin_mesh, (0.6, 0.0, 0.005))
physicsUtils.set_or_add_orient_op(skin_mesh, Gf.Rotation(Gf.Vec3d([0, 0, 1]), 90).GetQuat())
def get_observations(self) -> dict:
franka_dof_pos = self._frankas.get_joint_positions(clone=False)
franka_dof_vel = self._frankas.get_joint_velocities(clone=False)
self.franka_dof_pos = franka_dof_pos
dof_pos_scaled = (
2.0 * (franka_dof_pos - self.franka_dof_lower_limits)
/ (self.franka_dof_upper_limits - self.franka_dof_lower_limits)
- 1.0
)
self.lfinger_pos, _ = self._frankas._lfingers.get_world_poses(clone=False)
self.rfinger_pos, _ = self._frankas._rfingers.get_world_poses(clone=False)
self.gripper_site_pos = (self.lfinger_pos + self.rfinger_pos)/2 - self._env_pos
tube_positions = self.deformableView.get_simulation_mesh_nodal_positions(clone=False)
tube_velocities = self.deformableView.get_simulation_mesh_nodal_velocities(clone=False)
self.tube_front_positions = tube_positions[:, 200, :] - self._env_pos
self.tube_front_velocities = tube_velocities[:, 200, :]
self.tube_back_positions = tube_positions[:, -1, :] - self._env_pos
self.tube_back_velocities = tube_velocities[:, -1, :]
front_to_gripper = self.tube_front_positions - self.gripper_site_pos
to_front_goal = self.front_goal_pos - self.tube_front_positions
to_back_goal = self.back_goal_pos - self.tube_back_positions
self.obs_buf = torch.cat(
(
dof_pos_scaled,
franka_dof_vel * self.dof_vel_scale,
front_to_gripper,
to_front_goal,
to_back_goal,
self.tube_front_positions,
self.tube_front_velocities,
self.tube_back_positions,
self.tube_back_velocities,
),
dim=-1,
)
observations = {
self._frankas.name: {
"obs_buf": self.obs_buf
}
}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
self.actions = actions.clone().to(self._device)
targets = self.franka_dof_targets + self.franka_dof_speed_scales * self.dt * self.actions * self.action_scale
self.franka_dof_targets[:] = tensor_clamp(targets, self.franka_dof_lower_limits, self.franka_dof_upper_limits)
self.franka_dof_targets[:, -1] = self.franka_dof_targets[:, -2]
env_ids_int32 = torch.arange(self._frankas.count, dtype=torch.int32, device=self._device)
self._frankas.set_joint_position_targets(self.franka_dof_targets, indices=env_ids_int32)
def reset_idx(self, env_ids):
indices = env_ids.to(dtype=torch.int32)
num_indices = len(indices)
pos = self.franka_default_dof_pos
dof_pos = torch.zeros((num_indices, self._frankas.num_dof), device=self._device)
dof_vel = torch.zeros((num_indices, self._frankas.num_dof), device=self._device)
dof_pos[:, :] = pos
self.franka_dof_targets[env_ids, :] = pos
self.franka_dof_pos[env_ids, :] = pos
self._frankas.set_joint_position_targets(self.franka_dof_targets[env_ids], indices=indices)
self._frankas.set_joint_positions(dof_pos, indices=indices)
self._frankas.set_joint_velocities(dof_vel, indices=indices)
self.deformableView.set_simulation_mesh_nodal_positions(self.initial_tube_positions[env_ids], indices)
self.deformableView.set_simulation_mesh_nodal_velocities(self.initial_tube_velocities[env_ids], indices)
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def post_reset(self):
self.franka_default_dof_pos = torch.tensor(
[0.00, 0.63, 0.00, -2.15, 0.00, 2.76, 0.75, 0.02, 0.02], device=self._device
)
self.actions = torch.zeros((self._num_envs, self.num_actions), device=self._device)
self.front_goal_pos = torch.tensor([0.36, 0.0, 0.23], device=self._device).repeat((self._num_envs, 1))
self.back_goal_pos = torch.tensor([0.5, 0.2, 0.0], device=self._device).repeat((self._num_envs, 1))
self.goal_hand_rot = torch.tensor([0.0, 1.0, 0.0, 0.0], device=self._device).repeat((self.num_envs, 1))
self.lfinger_pos, _ = self._frankas._lfingers.get_world_poses(clone=False)
self.rfinger_pos, _ = self._frankas._rfingers.get_world_poses(clone=False)
self.gripper_site_pos = (self.lfinger_pos + self.rfinger_pos)/2 - self._env_pos
self.initial_tube_positions = self.deformableView.get_simulation_mesh_nodal_positions()
self.initial_tube_velocities = self.deformableView.get_simulation_mesh_nodal_velocities()
self.tube_front_positions = self.initial_tube_positions[:, 0, :] - self._env_pos
self.tube_front_velocities = self.initial_tube_velocities[:, 0, :]
self.tube_back_positions = self.initial_tube_positions[:, -1, :] - self._env_pos
self.tube_back_velocities = self.initial_tube_velocities[:, -1, :]
self.num_franka_dofs = self._frankas.num_dof
self.franka_dof_pos = torch.zeros((self.num_envs, self.num_franka_dofs), device=self._device)
dof_limits = self._frankas.get_dof_limits()
self.franka_dof_lower_limits = dof_limits[0, :, 0].to(device=self._device)
self.franka_dof_upper_limits = dof_limits[0, :, 1].to(device=self._device)
self.franka_dof_speed_scales = torch.ones_like(self.franka_dof_lower_limits)
self.franka_dof_speed_scales[self._frankas.gripper_indices] = 0.1
self.franka_dof_targets = torch.zeros(
(self._num_envs, self.num_franka_dofs), dtype=torch.float, device=self._device
)
# randomize all envs
indices = torch.arange(self._num_envs, dtype=torch.int64, device=self._device)
self.reset_idx(indices)
def calculate_metrics(self) -> None:
goal_distance_error = torch.norm(self.tube_back_positions[:, 0:2] - self.back_goal_pos[:, 0:2], p = 2, dim = -1)
goal_dist_reward = 1.0 / (5*goal_distance_error + .025)
current_z_level = self.tube_back_positions[:, 2:3]
z_lift_level = torch.where(
goal_distance_error < 0.07, torch.zeros_like(current_z_level), torch.ones_like(current_z_level)*0.18
)
front_lift_error = torch.norm(current_z_level - z_lift_level, p = 2, dim = -1)
front_lift_reward = 1.0 / (5*front_lift_error + .025)
rewards = goal_dist_reward + 4*front_lift_reward
self.rew_buf[:] = rewards
def is_done(self) -> None:
self.reset_buf = torch.where(self.progress_buf >= self._max_episode_length - 1, torch.ones_like(self.reset_buf), self.reset_buf)
self.reset_buf = torch.where(self.tube_front_positions[:, 0] < 0, torch.ones_like(self.reset_buf), self.reset_buf)
self.reset_buf = torch.where(self.tube_front_positions[:, 0] > 1.0, torch.ones_like(self.reset_buf), self.reset_buf)
self.reset_buf = torch.where(self.tube_front_positions[:, 1] < -1.0, torch.ones_like(self.reset_buf), self.reset_buf)
self.reset_buf = torch.where(self.tube_front_positions[:, 1] > 1.0, torch.ones_like(self.reset_buf), self.reset_buf)
| 13,316 | Python | 42.805921 | 136 | 0.641108 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/ant.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.torch.maths import tensor_clamp, torch_rand_float, unscale
from omni.isaac.core.utils.torch.rotations import compute_heading_and_up, compute_rot, quat_conjugate
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.ant import Ant
from omniisaacgymenvs.tasks.shared.locomotion import LocomotionTask
from pxr import PhysxSchema
class AntLocomotionTask(LocomotionTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
LocomotionTask.__init__(self, name=name, env=env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_observations = 60
self._num_actions = 8
self._ant_positions = torch.tensor([0, 0, 0.5])
LocomotionTask.update_config(self)
def set_up_scene(self, scene) -> None:
self.get_ant()
RLTask.set_up_scene(self, scene)
self._ants = ArticulationView(
prim_paths_expr="/World/envs/.*/Ant/torso", name="ant_view", reset_xform_properties=False
)
scene.add(self._ants)
return
def initialize_views(self, scene):
RLTask.initialize_views(self, scene)
if scene.object_exists("ant_view"):
scene.remove_object("ant_view", registry_only=True)
self._ants = ArticulationView(
prim_paths_expr="/World/envs/.*/Ant/torso", name="ant_view", reset_xform_properties=False
)
scene.add(self._ants)
def get_ant(self):
ant = Ant(prim_path=self.default_zero_env_path + "/Ant", name="Ant", translation=self._ant_positions)
self._sim_config.apply_articulation_settings(
"Ant", get_prim_at_path(ant.prim_path), self._sim_config.parse_actor_config("Ant")
)
def get_robot(self):
return self._ants
def post_reset(self):
self.joint_gears = torch.tensor([15, 15, 15, 15, 15, 15, 15, 15], dtype=torch.float32, device=self._device)
dof_limits = self._ants.get_dof_limits()
self.dof_limits_lower = dof_limits[0, :, 0].to(self._device)
self.dof_limits_upper = dof_limits[0, :, 1].to(self._device)
self.motor_effort_ratio = torch.ones_like(self.joint_gears, device=self._device)
force_links = ["front_left_foot", "front_right_foot", "left_back_foot", "right_back_foot"]
self._sensor_indices = torch.tensor(
[self._ants._body_indices[j] for j in force_links], device=self._device, dtype=torch.long
)
LocomotionTask.post_reset(self)
def get_dof_at_limit_cost(self):
return get_dof_at_limit_cost(self.obs_buf, self._ants.num_dof)
@torch.jit.script
def get_dof_at_limit_cost(obs_buf, num_dof):
# type: (Tensor, int) -> Tensor
return torch.sum(obs_buf[:, 12 : 12 + num_dof] > 0.99, dim=-1)
| 4,691 | Python | 41.654545 | 115 | 0.69708 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/cartpole.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.cartpole import Cartpole
class CartpoleTask(RLTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self._max_episode_length = 500
self._num_observations = 4
self._num_actions = 1
RLTask.__init__(self, name, env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._cartpole_positions = torch.tensor([0.0, 0.0, 2.0])
self._reset_dist = self._task_cfg["env"]["resetDist"]
self._max_push_effort = self._task_cfg["env"]["maxEffort"]
def set_up_scene(self, scene) -> None:
self.get_cartpole()
super().set_up_scene(scene)
self._cartpoles = ArticulationView(
prim_paths_expr="/World/envs/.*/Cartpole", name="cartpole_view", reset_xform_properties=False
)
scene.add(self._cartpoles)
return
def initialize_views(self, scene):
super().initialize_views(scene)
if scene.object_exists("cartpole_view"):
scene.remove_object("cartpole_view", registry_only=True)
self._cartpoles = ArticulationView(
prim_paths_expr="/World/envs/.*/Cartpole", name="cartpole_view", reset_xform_properties=False
)
scene.add(self._cartpoles)
def get_cartpole(self):
cartpole = Cartpole(
prim_path=self.default_zero_env_path + "/Cartpole", name="Cartpole", translation=self._cartpole_positions
)
# applies articulation settings from the task configuration yaml file
self._sim_config.apply_articulation_settings(
"Cartpole", get_prim_at_path(cartpole.prim_path), self._sim_config.parse_actor_config("Cartpole")
)
def get_observations(self) -> dict:
dof_pos = self._cartpoles.get_joint_positions(clone=False)
dof_vel = self._cartpoles.get_joint_velocities(clone=False)
self.cart_pos = dof_pos[:, self._cart_dof_idx]
self.cart_vel = dof_vel[:, self._cart_dof_idx]
self.pole_pos = dof_pos[:, self._pole_dof_idx]
self.pole_vel = dof_vel[:, self._pole_dof_idx]
self.obs_buf[:, 0] = self.cart_pos
self.obs_buf[:, 1] = self.cart_vel
self.obs_buf[:, 2] = self.pole_pos
self.obs_buf[:, 3] = self.pole_vel
observations = {self._cartpoles.name: {"obs_buf": self.obs_buf}}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
actions = actions.to(self._device)
forces = torch.zeros((self._cartpoles.count, self._cartpoles.num_dof), dtype=torch.float32, device=self._device)
forces[:, self._cart_dof_idx] = self._max_push_effort * actions[:, 0]
indices = torch.arange(self._cartpoles.count, dtype=torch.int32, device=self._device)
self._cartpoles.set_joint_efforts(forces, indices=indices)
def reset_idx(self, env_ids):
num_resets = len(env_ids)
# randomize DOF positions
dof_pos = torch.zeros((num_resets, self._cartpoles.num_dof), device=self._device)
dof_pos[:, self._cart_dof_idx] = 1.0 * (1.0 - 2.0 * torch.rand(num_resets, device=self._device))
dof_pos[:, self._pole_dof_idx] = 0.125 * math.pi * (1.0 - 2.0 * torch.rand(num_resets, device=self._device))
# randomize DOF velocities
dof_vel = torch.zeros((num_resets, self._cartpoles.num_dof), device=self._device)
dof_vel[:, self._cart_dof_idx] = 0.5 * (1.0 - 2.0 * torch.rand(num_resets, device=self._device))
dof_vel[:, self._pole_dof_idx] = 0.25 * math.pi * (1.0 - 2.0 * torch.rand(num_resets, device=self._device))
# apply resets
indices = env_ids.to(dtype=torch.int32)
self._cartpoles.set_joint_positions(dof_pos, indices=indices)
self._cartpoles.set_joint_velocities(dof_vel, indices=indices)
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def post_reset(self):
self._cart_dof_idx = self._cartpoles.get_dof_index("cartJoint")
self._pole_dof_idx = self._cartpoles.get_dof_index("poleJoint")
# randomize all envs
indices = torch.arange(self._cartpoles.count, dtype=torch.int64, device=self._device)
self.reset_idx(indices)
def calculate_metrics(self) -> None:
reward = 1.0 - self.pole_pos * self.pole_pos - 0.01 * torch.abs(self.cart_vel) - 0.005 * torch.abs(self.pole_vel)
reward = torch.where(torch.abs(self.cart_pos) > self._reset_dist, torch.ones_like(reward) * -2.0, reward)
reward = torch.where(torch.abs(self.pole_pos) > np.pi / 2, torch.ones_like(reward) * -2.0, reward)
self.rew_buf[:] = reward
def is_done(self) -> None:
resets = torch.where(torch.abs(self.cart_pos) > self._reset_dist, 1, 0)
resets = torch.where(torch.abs(self.pole_pos) > math.pi / 2, 1, resets)
resets = torch.where(self.progress_buf >= self._max_episode_length, 1, resets)
self.reset_buf[:] = resets
| 7,250 | Python | 42.945454 | 121 | 0.65931 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/quadcopter.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.objects import DynamicSphere
from omni.isaac.core.prims import RigidPrimView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.torch.rotations import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.quadcopter import Quadcopter
from omniisaacgymenvs.robots.articulations.views.quadcopter_view import QuadcopterView
class QuadcopterTask(RLTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self._num_observations = 21
self._num_actions = 12
self._copter_position = torch.tensor([0, 0, 1.0])
RLTask.__init__(self, name=name, env=env)
max_thrust = 2.0
self.thrust_lower_limits = -max_thrust * torch.ones(4, device=self._device, dtype=torch.float32)
self.thrust_upper_limits = max_thrust * torch.ones(4, device=self._device, dtype=torch.float32)
self.all_indices = torch.arange(self._num_envs, dtype=torch.int32, device=self._device)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._max_episode_length = self._task_cfg["env"]["maxEpisodeLength"]
self.dt = self._task_cfg["sim"]["dt"]
def set_up_scene(self, scene) -> None:
self.get_copter()
self.get_target()
RLTask.set_up_scene(self, scene)
self._copters = QuadcopterView(prim_paths_expr="/World/envs/.*/Quadcopter", name="quadcopter_view")
self._balls = RigidPrimView(
prim_paths_expr="/World/envs/.*/ball", name="targets_view", reset_xform_properties=False
)
self._balls._non_root_link = True # do not set states for kinematics
scene.add(self._copters)
scene.add(self._copters.rotors)
scene.add(self._balls)
return
def initialize_views(self, scene):
super().initialize_views(scene)
if scene.object_exists("quadcopter_view"):
scene.remove_object("quadcopter_view", registry_only=True)
if scene.object_exists("rotors_view"):
scene.remove_object("rotors_view", registry_only=True)
if scene.object_exists("targets_view"):
scene.remove_object("targets_view", registry_only=True)
self._copters = QuadcopterView(prim_paths_expr="/World/envs/.*/Quadcopter", name="quadcopter_view")
self._balls = RigidPrimView(
prim_paths_expr="/World/envs/.*/ball", name="targets_view", reset_xform_properties=False
)
scene.add(self._copters)
scene.add(self._copters.rotors)
scene.add(self._balls)
def get_copter(self):
copter = Quadcopter(
prim_path=self.default_zero_env_path + "/Quadcopter", name="quadcopter", translation=self._copter_position
)
self._sim_config.apply_articulation_settings(
"copter", get_prim_at_path(copter.prim_path), self._sim_config.parse_actor_config("copter")
)
def get_target(self):
radius = 0.05
color = torch.tensor([1, 0, 0])
ball = DynamicSphere(
prim_path=self.default_zero_env_path + "/ball",
name="target_0",
radius=radius,
color=color,
)
self._sim_config.apply_articulation_settings(
"ball", get_prim_at_path(ball.prim_path), self._sim_config.parse_actor_config("ball")
)
ball.set_collision_enabled(False)
def get_observations(self) -> dict:
self.root_pos, self.root_rot = self._copters.get_world_poses(clone=False)
self.root_velocities = self._copters.get_velocities(clone=False)
self.dof_pos = self._copters.get_joint_positions(clone=False)
root_positions = self.root_pos - self._env_pos
root_quats = self.root_rot
root_linvels = self.root_velocities[:, :3]
root_angvels = self.root_velocities[:, 3:]
self.obs_buf[..., 0:3] = (self.target_positions - root_positions) / 3
self.obs_buf[..., 3:7] = root_quats
self.obs_buf[..., 7:10] = root_linvels / 2
self.obs_buf[..., 10:13] = root_angvels / math.pi
self.obs_buf[..., 13:21] = self.dof_pos
observations = {self._copters.name: {"obs_buf": self.obs_buf}}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
actions = actions.clone().to(self._device)
dof_action_speed_scale = 8 * math.pi
self.dof_position_targets += self.dt * dof_action_speed_scale * actions[:, 0:8]
self.dof_position_targets[:] = tensor_clamp(
self.dof_position_targets, self.dof_lower_limits, self.dof_upper_limits
)
thrust_action_speed_scale = 100
self.thrusts += self.dt * thrust_action_speed_scale * actions[:, 8:12]
self.thrusts[:] = tensor_clamp(self.thrusts, self.thrust_lower_limits, self.thrust_upper_limits)
self.forces[:, 0, 2] = self.thrusts[:, 0]
self.forces[:, 1, 2] = self.thrusts[:, 1]
self.forces[:, 2, 2] = self.thrusts[:, 2]
self.forces[:, 3, 2] = self.thrusts[:, 3]
# clear actions for reset envs
self.thrusts[reset_env_ids] = 0.0
self.forces[reset_env_ids] = 0.0
self.dof_position_targets[reset_env_ids] = self.dof_pos[reset_env_ids]
# apply actions
self._copters.set_joint_position_targets(self.dof_position_targets)
self._copters.rotors.apply_forces(self.forces, is_global=False)
def post_reset(self):
# control tensors
self.dof_position_targets = torch.zeros(
(self._num_envs, self._copters.num_dof), dtype=torch.float32, device=self._device, requires_grad=False
)
self.thrusts = torch.zeros((self._num_envs, 4), dtype=torch.float32, device=self._device, requires_grad=False)
self.forces = torch.zeros(
(self._num_envs, self._copters.rotors.count // self._num_envs, 3),
dtype=torch.float32,
device=self._device,
requires_grad=False,
)
self.target_positions = torch.zeros((self._num_envs, 3), device=self._device)
self.target_positions[:, 2] = 1.0
self.root_pos, self.root_rot = self._copters.get_world_poses(clone=False)
self.root_velocities = self._copters.get_velocities(clone=False)
self.dof_pos = self._copters.get_joint_positions(clone=False)
self.dof_vel = self._copters.get_joint_velocities(clone=False)
self.initial_root_pos, self.initial_root_rot = self.root_pos.clone(), self.root_rot.clone()
dof_limits = self._copters.get_dof_limits()
self.dof_lower_limits = dof_limits[0][:, 0].to(device=self._device)
self.dof_upper_limits = dof_limits[0][:, 1].to(device=self._device)
def reset_idx(self, env_ids):
num_resets = len(env_ids)
self.dof_pos[env_ids, :] = torch_rand_float(-0.2, 0.2, (num_resets, self._copters.num_dof), device=self._device)
self.dof_vel[env_ids, :] = 0
root_pos = self.initial_root_pos.clone()
root_pos[env_ids, 0] += torch_rand_float(-1.5, 1.5, (num_resets, 1), device=self._device).view(-1)
root_pos[env_ids, 1] += torch_rand_float(-1.5, 1.5, (num_resets, 1), device=self._device).view(-1)
root_pos[env_ids, 2] += torch_rand_float(-0.2, 1.5, (num_resets, 1), device=self._device).view(-1)
root_velocities = self.root_velocities.clone()
root_velocities[env_ids] = 0
# apply resets
self._copters.set_joint_positions(self.dof_pos[env_ids], indices=env_ids)
self._copters.set_joint_velocities(self.dof_vel[env_ids], indices=env_ids)
self._copters.set_world_poses(root_pos[env_ids], self.initial_root_rot[env_ids].clone(), indices=env_ids)
self._copters.set_velocities(root_velocities[env_ids], indices=env_ids)
self._balls.set_world_poses(positions=self.target_positions[:, 0:3] + self._env_pos)
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def calculate_metrics(self) -> None:
root_positions = self.root_pos - self._env_pos
root_quats = self.root_rot
root_angvels = self.root_velocities[:, 3:]
# distance to target
target_dist = torch.sqrt(torch.square(self.target_positions - root_positions).sum(-1))
pos_reward = 1.0 / (1.0 + 3 * target_dist * target_dist) # 2
self.target_dist = target_dist
self.root_positions = root_positions
# uprightness
ups = quat_axis(root_quats, 2)
tiltage = torch.abs(1 - ups[..., 2])
up_reward = 1.0 / (1.0 + 10 * tiltage * tiltage)
# spinning
spinnage = torch.abs(root_angvels[..., 2])
spinnage_reward = 1.0 / (1.0 + 0.001 * spinnage * spinnage)
rew = pos_reward + pos_reward * (up_reward + spinnage_reward + spinnage * spinnage * (-1 / 400))
rew = torch.clip(rew, 0.0, None)
self.rew_buf[:] = rew
def is_done(self) -> None:
# resets due to misbehavior
ones = torch.ones_like(self.reset_buf)
die = torch.zeros_like(self.reset_buf)
die = torch.where(self.target_dist > 3.0, ones, die)
die = torch.where(self.root_positions[..., 2] < 0.3, ones, die)
# resets due to episode length
self.reset_buf[:] = torch.where(self.progress_buf >= self._max_episode_length - 1, ones, die)
| 11,492 | Python | 42.866412 | 120 | 0.640707 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/ingenuity.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from omniisaacgymenvs.robots.articulations.ingenuity import Ingenuity
from omniisaacgymenvs.robots.articulations.views.ingenuity_view import IngenuityView
from omni.isaac.core.utils.torch.rotations import *
from omni.isaac.core.objects import DynamicSphere
from omni.isaac.core.prims import RigidPrimView
from omni.isaac.core.utils.prims import get_prim_at_path
from omniisaacgymenvs.tasks.base.rl_task import RLTask
import numpy as np
import torch
import math
class IngenuityTask(RLTask):
def __init__(
self,
name,
sim_config,
env,
offset=None
) -> None:
self.update_config(sim_config)
self.thrust_limit = 2000
self.thrust_lateral_component = 0.2
self._num_observations = 13
self._num_actions = 6
self._ingenuity_position = torch.tensor([0, 0, 1.0])
self._ball_position = torch.tensor([0, 0, 1.0])
RLTask.__init__(self, name=name, env=env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._max_episode_length = self._task_cfg["env"]["maxEpisodeLength"]
self.dt = self._task_cfg["sim"]["dt"]
def set_up_scene(self, scene) -> None:
self.get_ingenuity()
self.get_target()
RLTask.set_up_scene(self, scene)
self._copters = IngenuityView(prim_paths_expr="/World/envs/.*/Ingenuity", name="ingenuity_view")
self._balls = RigidPrimView(prim_paths_expr="/World/envs/.*/ball", name="targets_view", reset_xform_properties=False)
self._balls._non_root_link = True # do not set states for kinematics
scene.add(self._copters)
scene.add(self._balls)
for i in range(2):
scene.add(self._copters.physics_rotors[i])
scene.add(self._copters.visual_rotors[i])
return
def initialize_views(self, scene):
super().initialize_views(scene)
if scene.object_exists("ingenuity_view"):
scene.remove_object("ingenuity_view", registry_only=True)
for i in range(2):
if scene.object_exists(f"physics_rotor_{i}_view"):
scene.remove_object(f"physics_rotor_{i}_view", registry_only=True)
if scene.object_exists(f"visual_rotor_{i}_view"):
scene.remove_object(f"visual_rotor_{i}_view", registry_only=True)
if scene.object_exists("targets_view"):
scene.remove_object("targets_view", registry_only=True)
self._copters = IngenuityView(prim_paths_expr="/World/envs/.*/Ingenuity", name="ingenuity_view")
self._balls = RigidPrimView(prim_paths_expr="/World/envs/.*/ball", name="targets_view", reset_xform_properties=False)
scene.add(self._copters)
scene.add(self._balls)
for i in range(2):
scene.add(self._copters.physics_rotors[i])
scene.add(self._copters.visual_rotors[i])
def get_ingenuity(self):
copter = Ingenuity(prim_path=self.default_zero_env_path + "/Ingenuity", name="ingenuity", translation=self._ingenuity_position)
self._sim_config.apply_articulation_settings("ingenuity", get_prim_at_path(copter.prim_path), self._sim_config.parse_actor_config("ingenuity"))
def get_target(self):
radius = 0.1
color = torch.tensor([1, 0, 0])
ball = DynamicSphere(
prim_path=self.default_zero_env_path + "/ball",
translation=self._ball_position,
name="target_0",
radius=radius,
color=color,
)
self._sim_config.apply_articulation_settings("ball", get_prim_at_path(ball.prim_path), self._sim_config.parse_actor_config("ball"))
ball.set_collision_enabled(False)
def get_observations(self) -> dict:
self.root_pos, self.root_rot = self._copters.get_world_poses(clone=False)
self.root_velocities = self._copters.get_velocities(clone=False)
root_positions = self.root_pos - self._env_pos
root_quats = self.root_rot
root_linvels = self.root_velocities[:, :3]
root_angvels = self.root_velocities[:, 3:]
self.obs_buf[..., 0:3] = (self.target_positions - root_positions) / 3
self.obs_buf[..., 3:7] = root_quats
self.obs_buf[..., 7:10] = root_linvels / 2
self.obs_buf[..., 10:13] = root_angvels / math.pi
observations = {
self._copters.name: {
"obs_buf": self.obs_buf
}
}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
set_target_ids = (self.progress_buf % 500 == 0).nonzero(as_tuple=False).squeeze(-1)
if len(set_target_ids) > 0:
self.set_targets(set_target_ids)
actions = actions.clone().to(self._device)
vertical_thrust_prop_0 = torch.clamp(actions[:, 2] * self.thrust_limit, -self.thrust_limit, self.thrust_limit)
vertical_thrust_prop_1 = torch.clamp(actions[:, 5] * self.thrust_limit, -self.thrust_limit, self.thrust_limit)
lateral_fraction_prop_0 = torch.clamp(
actions[:, 0:2] * self.thrust_lateral_component,
-self.thrust_lateral_component,
self.thrust_lateral_component,
)
lateral_fraction_prop_1 = torch.clamp(
actions[:, 3:5] * self.thrust_lateral_component,
-self.thrust_lateral_component,
self.thrust_lateral_component,
)
self.thrusts[:, 0, 2] = self.dt * vertical_thrust_prop_0
self.thrusts[:, 0, 0:2] = self.thrusts[:, 0, 2, None] * lateral_fraction_prop_0
self.thrusts[:, 1, 2] = self.dt * vertical_thrust_prop_1
self.thrusts[:, 1, 0:2] = self.thrusts[:, 1, 2, None] * lateral_fraction_prop_1
# clear actions for reset envs
self.thrusts[reset_env_ids] = 0
# spin spinning rotors
self.dof_vel[:, self.spinning_indices[0]] = 50
self.dof_vel[:, self.spinning_indices[1]] = -50
self._copters.set_joint_velocities(self.dof_vel)
# apply actions
for i in range(2):
self._copters.physics_rotors[i].apply_forces(self.thrusts[:, i], indices=self.all_indices)
def post_reset(self):
self.spinning_indices = torch.tensor([1, 3], device=self._device)
self.all_indices = torch.arange(self._num_envs, dtype=torch.int32, device=self._device)
self.target_positions = torch.zeros((self._num_envs, 3), device=self._device, dtype=torch.float32)
self.target_positions[:, 2] = 1
self.root_pos, self.root_rot = self._copters.get_world_poses()
self.root_velocities = self._copters.get_velocities()
self.dof_pos = self._copters.get_joint_positions()
self.dof_vel = self._copters.get_joint_velocities()
self.initial_ball_pos, self.initial_ball_rot = self._balls.get_world_poses()
self.initial_root_pos, self.initial_root_rot = self.root_pos.clone(), self.root_rot.clone()
# control tensors
self.thrusts = torch.zeros((self._num_envs, 2, 3), dtype=torch.float32, device=self._device)
def set_targets(self, env_ids):
num_sets = len(env_ids)
envs_long = env_ids.long()
# set target position randomly with x, y in (-1, 1) and z in (1, 2)
self.target_positions[envs_long, 0:2] = torch.rand((num_sets, 2), device=self._device) * 2 - 1
self.target_positions[envs_long, 2] = torch.rand(num_sets, device=self._device) + 1
# shift the target up so it visually aligns better
ball_pos = self.target_positions[envs_long] + self._env_pos[envs_long]
ball_pos[:, 2] += 0.4
self._balls.set_world_poses(ball_pos[:, 0:3], self.initial_ball_rot[envs_long].clone(), indices=env_ids)
def reset_idx(self, env_ids):
num_resets = len(env_ids)
self.dof_pos[env_ids, 1] = torch_rand_float(-0.2, 0.2, (num_resets, 1), device=self._device).squeeze()
self.dof_pos[env_ids, 3] = torch_rand_float(-0.2, 0.2, (num_resets, 1), device=self._device).squeeze()
self.dof_vel[env_ids, :] = 0
root_pos = self.initial_root_pos.clone()
root_pos[env_ids, 0] += torch_rand_float(-0.5, 0.5, (num_resets, 1), device=self._device).view(-1)
root_pos[env_ids, 1] += torch_rand_float(-0.5, 0.5, (num_resets, 1), device=self._device).view(-1)
root_pos[env_ids, 2] += torch_rand_float(-0.5, 0.5, (num_resets, 1), device=self._device).view(-1)
root_velocities = self.root_velocities.clone()
root_velocities[env_ids] = 0
# apply resets
self._copters.set_joint_positions(self.dof_pos[env_ids], indices=env_ids)
self._copters.set_joint_velocities(self.dof_vel[env_ids], indices=env_ids)
self._copters.set_world_poses(root_pos[env_ids], self.initial_root_rot[env_ids].clone(), indices=env_ids)
self._copters.set_velocities(root_velocities[env_ids], indices=env_ids)
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def calculate_metrics(self) -> None:
root_positions = self.root_pos - self._env_pos
root_quats = self.root_rot
root_angvels = self.root_velocities[:, 3:]
# distance to target
target_dist = torch.sqrt(torch.square(self.target_positions - root_positions).sum(-1))
pos_reward = 1.0 / (1.0 + 2.5 * target_dist * target_dist)
self.target_dist = target_dist
self.root_positions = root_positions
# uprightness
ups = quat_axis(root_quats, 2)
tiltage = torch.abs(1 - ups[..., 2])
up_reward = 1.0 / (1.0 + 30 * tiltage * tiltage)
# spinning
spinnage = torch.abs(root_angvels[..., 2])
spinnage_reward = 1.0 / (1.0 + 10 * spinnage * spinnage)
# combined reward
# uprightness and spinning only matter when close to the target
self.rew_buf[:] = pos_reward + pos_reward * (up_reward + spinnage_reward)
def is_done(self) -> None:
# resets due to misbehavior
ones = torch.ones_like(self.reset_buf)
die = torch.zeros_like(self.reset_buf)
die = torch.where(self.target_dist > 20.0, ones, die)
die = torch.where(self.root_positions[..., 2] < 0.5, ones, die)
# resets due to episode length
self.reset_buf[:] = torch.where(self.progress_buf >= self._max_episode_length - 1, ones, die)
| 12,385 | Python | 42.921986 | 151 | 0.635204 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/anymal.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.torch.rotations import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.anymal import Anymal
from omniisaacgymenvs.robots.articulations.views.anymal_view import AnymalView
from omniisaacgymenvs.tasks.utils.usd_utils import set_drive
class AnymalTask(RLTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
self.update_config(sim_config)
self._num_observations = 48
self._num_actions = 12
RLTask.__init__(self, name, env)
return
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
# normalization
self.lin_vel_scale = self._task_cfg["env"]["learn"]["linearVelocityScale"]
self.ang_vel_scale = self._task_cfg["env"]["learn"]["angularVelocityScale"]
self.dof_pos_scale = self._task_cfg["env"]["learn"]["dofPositionScale"]
self.dof_vel_scale = self._task_cfg["env"]["learn"]["dofVelocityScale"]
self.action_scale = self._task_cfg["env"]["control"]["actionScale"]
# reward scales
self.rew_scales = {}
self.rew_scales["lin_vel_xy"] = self._task_cfg["env"]["learn"]["linearVelocityXYRewardScale"]
self.rew_scales["ang_vel_z"] = self._task_cfg["env"]["learn"]["angularVelocityZRewardScale"]
self.rew_scales["lin_vel_z"] = self._task_cfg["env"]["learn"]["linearVelocityZRewardScale"]
self.rew_scales["joint_acc"] = self._task_cfg["env"]["learn"]["jointAccRewardScale"]
self.rew_scales["action_rate"] = self._task_cfg["env"]["learn"]["actionRateRewardScale"]
self.rew_scales["cosmetic"] = self._task_cfg["env"]["learn"]["cosmeticRewardScale"]
# command ranges
self.command_x_range = self._task_cfg["env"]["randomCommandVelocityRanges"]["linear_x"]
self.command_y_range = self._task_cfg["env"]["randomCommandVelocityRanges"]["linear_y"]
self.command_yaw_range = self._task_cfg["env"]["randomCommandVelocityRanges"]["yaw"]
# base init state
pos = self._task_cfg["env"]["baseInitState"]["pos"]
rot = self._task_cfg["env"]["baseInitState"]["rot"]
v_lin = self._task_cfg["env"]["baseInitState"]["vLinear"]
v_ang = self._task_cfg["env"]["baseInitState"]["vAngular"]
state = pos + rot + v_lin + v_ang
self.base_init_state = state
# default joint positions
self.named_default_joint_angles = self._task_cfg["env"]["defaultJointAngles"]
# other
self.dt = 1 / 60
self.max_episode_length_s = self._task_cfg["env"]["learn"]["episodeLength_s"]
self.max_episode_length = int(self.max_episode_length_s / self.dt + 0.5)
self.Kp = self._task_cfg["env"]["control"]["stiffness"]
self.Kd = self._task_cfg["env"]["control"]["damping"]
for key in self.rew_scales.keys():
self.rew_scales[key] *= self.dt
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._anymal_translation = torch.tensor([0.0, 0.0, 0.62])
self._env_spacing = self._task_cfg["env"]["envSpacing"]
def set_up_scene(self, scene) -> None:
self.get_anymal()
super().set_up_scene(scene)
self._anymals = AnymalView(prim_paths_expr="/World/envs/.*/anymal", name="anymalview")
scene.add(self._anymals)
scene.add(self._anymals._knees)
scene.add(self._anymals._base)
return
def initialize_views(self, scene):
super().initialize_views(scene)
if scene.object_exists("anymalview"):
scene.remove_object("anymalview", registry_only=True)
if scene.object_exists("knees_view"):
scene.remove_object("knees_view", registry_only=True)
if scene.object_exists("base_view"):
scene.remove_object("base_view", registry_only=True)
self._anymals = AnymalView(prim_paths_expr="/World/envs/.*/anymal", name="anymalview")
scene.add(self._anymals)
scene.add(self._anymals._knees)
scene.add(self._anymals._base)
def get_anymal(self):
anymal = Anymal(
prim_path=self.default_zero_env_path + "/anymal", name="Anymal", translation=self._anymal_translation
)
self._sim_config.apply_articulation_settings(
"Anymal", get_prim_at_path(anymal.prim_path), self._sim_config.parse_actor_config("Anymal")
)
# Configure joint properties
joint_paths = []
for quadrant in ["LF", "LH", "RF", "RH"]:
for component, abbrev in [("HIP", "H"), ("THIGH", "K")]:
joint_paths.append(f"{quadrant}_{component}/{quadrant}_{abbrev}FE")
joint_paths.append(f"base/{quadrant}_HAA")
for joint_path in joint_paths:
set_drive(f"{anymal.prim_path}/{joint_path}", "angular", "position", 0, 400, 40, 1000)
def get_observations(self) -> dict:
torso_position, torso_rotation = self._anymals.get_world_poses(clone=False)
root_velocities = self._anymals.get_velocities(clone=False)
dof_pos = self._anymals.get_joint_positions(clone=False)
dof_vel = self._anymals.get_joint_velocities(clone=False)
velocity = root_velocities[:, 0:3]
ang_velocity = root_velocities[:, 3:6]
base_lin_vel = quat_rotate_inverse(torso_rotation, velocity) * self.lin_vel_scale
base_ang_vel = quat_rotate_inverse(torso_rotation, ang_velocity) * self.ang_vel_scale
projected_gravity = quat_rotate(torso_rotation, self.gravity_vec)
dof_pos_scaled = (dof_pos - self.default_dof_pos) * self.dof_pos_scale
commands_scaled = self.commands * torch.tensor(
[self.lin_vel_scale, self.lin_vel_scale, self.ang_vel_scale],
requires_grad=False,
device=self.commands.device,
)
obs = torch.cat(
(
base_lin_vel,
base_ang_vel,
projected_gravity,
commands_scaled,
dof_pos_scaled,
dof_vel * self.dof_vel_scale,
self.actions,
),
dim=-1,
)
self.obs_buf[:] = obs
observations = {self._anymals.name: {"obs_buf": self.obs_buf}}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
indices = torch.arange(self._anymals.count, dtype=torch.int32, device=self._device)
self.actions[:] = actions.clone().to(self._device)
current_targets = self.current_targets + self.action_scale * self.actions * self.dt
self.current_targets[:] = tensor_clamp(
current_targets, self.anymal_dof_lower_limits, self.anymal_dof_upper_limits
)
self._anymals.set_joint_position_targets(self.current_targets, indices)
def reset_idx(self, env_ids):
num_resets = len(env_ids)
# randomize DOF velocities
velocities = torch_rand_float(-0.1, 0.1, (num_resets, self._anymals.num_dof), device=self._device)
dof_pos = self.default_dof_pos[env_ids]
dof_vel = velocities
self.current_targets[env_ids] = dof_pos[:]
root_vel = torch.zeros((num_resets, 6), device=self._device)
# apply resets
indices = env_ids.to(dtype=torch.int32)
self._anymals.set_joint_positions(dof_pos, indices)
self._anymals.set_joint_velocities(dof_vel, indices)
self._anymals.set_world_poses(
self.initial_root_pos[env_ids].clone(), self.initial_root_rot[env_ids].clone(), indices
)
self._anymals.set_velocities(root_vel, indices)
self.commands_x[env_ids] = torch_rand_float(
self.command_x_range[0], self.command_x_range[1], (num_resets, 1), device=self._device
).squeeze()
self.commands_y[env_ids] = torch_rand_float(
self.command_y_range[0], self.command_y_range[1], (num_resets, 1), device=self._device
).squeeze()
self.commands_yaw[env_ids] = torch_rand_float(
self.command_yaw_range[0], self.command_yaw_range[1], (num_resets, 1), device=self._device
).squeeze()
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
self.last_actions[env_ids] = 0.0
self.last_dof_vel[env_ids] = 0.0
def post_reset(self):
self.default_dof_pos = torch.zeros(
(self.num_envs, 12), dtype=torch.float, device=self.device, requires_grad=False
)
dof_names = self._anymals.dof_names
for i in range(self.num_actions):
name = dof_names[i]
angle = self.named_default_joint_angles[name]
self.default_dof_pos[:, i] = angle
self.initial_root_pos, self.initial_root_rot = self._anymals.get_world_poses()
self.current_targets = self.default_dof_pos.clone()
dof_limits = self._anymals.get_dof_limits()
self.anymal_dof_lower_limits = dof_limits[0, :, 0].to(device=self._device)
self.anymal_dof_upper_limits = dof_limits[0, :, 1].to(device=self._device)
self.commands = torch.zeros(self._num_envs, 3, dtype=torch.float, device=self._device, requires_grad=False)
self.commands_y = self.commands.view(self._num_envs, 3)[..., 1]
self.commands_x = self.commands.view(self._num_envs, 3)[..., 0]
self.commands_yaw = self.commands.view(self._num_envs, 3)[..., 2]
# initialize some data used later on
self.extras = {}
self.gravity_vec = torch.tensor([0.0, 0.0, -1.0], device=self._device).repeat((self._num_envs, 1))
self.actions = torch.zeros(
self._num_envs, self.num_actions, dtype=torch.float, device=self._device, requires_grad=False
)
self.last_dof_vel = torch.zeros(
(self._num_envs, 12), dtype=torch.float, device=self._device, requires_grad=False
)
self.last_actions = torch.zeros(
self._num_envs, self.num_actions, dtype=torch.float, device=self._device, requires_grad=False
)
self.time_out_buf = torch.zeros_like(self.reset_buf)
# randomize all envs
indices = torch.arange(self._anymals.count, dtype=torch.int64, device=self._device)
self.reset_idx(indices)
def calculate_metrics(self) -> None:
torso_position, torso_rotation = self._anymals.get_world_poses(clone=False)
root_velocities = self._anymals.get_velocities(clone=False)
dof_pos = self._anymals.get_joint_positions(clone=False)
dof_vel = self._anymals.get_joint_velocities(clone=False)
velocity = root_velocities[:, 0:3]
ang_velocity = root_velocities[:, 3:6]
base_lin_vel = quat_rotate_inverse(torso_rotation, velocity)
base_ang_vel = quat_rotate_inverse(torso_rotation, ang_velocity)
# velocity tracking reward
lin_vel_error = torch.sum(torch.square(self.commands[:, :2] - base_lin_vel[:, :2]), dim=1)
ang_vel_error = torch.square(self.commands[:, 2] - base_ang_vel[:, 2])
rew_lin_vel_xy = torch.exp(-lin_vel_error / 0.25) * self.rew_scales["lin_vel_xy"]
rew_ang_vel_z = torch.exp(-ang_vel_error / 0.25) * self.rew_scales["ang_vel_z"]
rew_lin_vel_z = torch.square(base_lin_vel[:, 2]) * self.rew_scales["lin_vel_z"]
rew_joint_acc = torch.sum(torch.square(self.last_dof_vel - dof_vel), dim=1) * self.rew_scales["joint_acc"]
rew_action_rate = (
torch.sum(torch.square(self.last_actions - self.actions), dim=1) * self.rew_scales["action_rate"]
)
rew_cosmetic = (
torch.sum(torch.abs(dof_pos[:, 0:4] - self.default_dof_pos[:, 0:4]), dim=1) * self.rew_scales["cosmetic"]
)
total_reward = rew_lin_vel_xy + rew_ang_vel_z + rew_joint_acc + rew_action_rate + rew_cosmetic + rew_lin_vel_z
total_reward = torch.clip(total_reward, 0.0, None)
self.last_actions[:] = self.actions[:]
self.last_dof_vel[:] = dof_vel[:]
self.fallen_over = self._anymals.is_base_below_threshold(threshold=0.51, ground_heights=0.0)
total_reward[torch.nonzero(self.fallen_over)] = -1
self.rew_buf[:] = total_reward.detach()
def is_done(self) -> None:
# reset agents
time_out = self.progress_buf >= self.max_episode_length - 1
self.reset_buf[:] = time_out | self.fallen_over
| 14,344 | Python | 44.539682 | 118 | 0.630996 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/warp/humanoid.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from omniisaacgymenvs.tasks.warp.shared.locomotion import LocomotionTask
from omniisaacgymenvs.robots.articulations.humanoid import Humanoid
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
from omniisaacgymenvs.tasks.base.rl_task import RLTaskWarp
import numpy as np
import torch
import warp as wp
import math
class HumanoidLocomotionTask(LocomotionTask):
def __init__(
self,
name,
sim_config,
env,
offset=None
) -> None:
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_observations = 87
self._num_actions = 21
self._humanoid_positions = torch.tensor([0, 0, 1.34])
LocomotionTask.__init__(self, name=name, env=env)
return
def set_up_scene(self, scene) -> None:
self.get_humanoid()
RLTaskWarp.set_up_scene(self, scene)
self._humanoids = ArticulationView(prim_paths_expr="/World/envs/.*/Humanoid/torso", name="humanoid_view", reset_xform_properties=False)
scene.add(self._humanoids)
return
def get_humanoid(self):
humanoid = Humanoid(prim_path=self.default_zero_env_path + "/Humanoid", name="Humanoid", translation=self._humanoid_positions)
self._sim_config.apply_articulation_settings("Humanoid", get_prim_at_path(humanoid.prim_path),
self._sim_config.parse_actor_config("Humanoid"))
def get_robot(self):
return self._humanoids
def post_reset(self):
self.joint_gears = wp.array(
[
67.5000, # lower_waist
67.5000, # lower_waist
67.5000, # right_upper_arm
67.5000, # right_upper_arm
67.5000, # left_upper_arm
67.5000, # left_upper_arm
67.5000, # pelvis
45.0000, # right_lower_arm
45.0000, # left_lower_arm
45.0000, # right_thigh: x
135.0000, # right_thigh: y
45.0000, # right_thigh: z
45.0000, # left_thigh: x
135.0000, # left_thigh: y
45.0000, # left_thigh: z
90.0000, # right_knee
90.0000, # left_knee
22.5, # right_foot
22.5, # right_foot
22.5, # left_foot
22.5, # left_foot
],
device=self._device,
dtype=wp.float32
)
self.max_motor_effort = 135.0
self.motor_effort_ratio = wp.zeros(self._humanoids._num_dof, dtype=wp.float32, device=self._device)
wp.launch(compute_effort_ratio, dim=self._humanoids._num_dof,
inputs=[self.motor_effort_ratio, self.joint_gears, self.max_motor_effort], device=self._device)
dof_limits = self._humanoids.get_dof_limits().to(self._device)
self.dof_limits_lower = wp.zeros(self._humanoids._num_dof, dtype=wp.float32, device=self._device)
self.dof_limits_upper = wp.zeros(self._humanoids._num_dof, dtype=wp.float32, device=self._device)
wp.launch(parse_dof_limits, dim=self._humanoids._num_dof,
inputs=[self.dof_limits_lower, self.dof_limits_upper, dof_limits], device=self._device)
self.dof_at_limit_cost = wp.zeros(self._num_envs, dtype=wp.float32, device=self._device)
force_links = ["left_foot", "right_foot"]
self._sensor_indices = wp.array([self._humanoids._body_indices[j] for j in force_links], device=self._device, dtype=wp.int32)
LocomotionTask.post_reset(self)
def get_dof_at_limit_cost(self):
wp.launch(get_dof_at_limit_cost, dim=(self._num_envs, self._humanoids._num_dof),
inputs=[self.dof_at_limit_cost, self.obs_buf, self.motor_effort_ratio, self.joints_at_limit_cost_scale], device=self._device)
return self.dof_at_limit_cost
@wp.kernel
def compute_effort_ratio(motor_effort_ratio: wp.array(dtype=wp.float32),
joint_gears: wp.array(dtype=wp.float32),
max_motor_effort: float):
tid = wp.tid()
motor_effort_ratio[tid] = joint_gears[tid] / max_motor_effort
@wp.kernel
def parse_dof_limits(dof_limits_lower: wp.array(dtype=wp.float32),
dof_limits_upper: wp.array(dtype=wp.float32),
dof_limits: wp.array(dtype=wp.float32, ndim=3)):
tid = wp.tid()
dof_limits_lower[tid] = dof_limits[0, tid, 0]
dof_limits_upper[tid] = dof_limits[0, tid, 1]
@wp.kernel
def get_dof_at_limit_cost(dof_at_limit_cost: wp.array(dtype=wp.float32),
obs_buf: wp.array(dtype=wp.float32, ndim=2),
motor_effort_ratio: wp.array(dtype=wp.float32),
joints_at_limit_cost_scale: float):
i, j = wp.tid()
dof_i = j + 12
scaled_cost = joints_at_limit_cost_scale * (wp.abs(obs_buf[i, dof_i]) - 0.98) / 0.02
cost = 0.0
if wp.abs(obs_buf[i, dof_i]) > 0.98:
cost = scaled_cost * motor_effort_ratio[j]
dof_at_limit_cost[i] = cost
| 6,707 | Python | 42.558441 | 143 | 0.640376 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/warp/ant.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from omniisaacgymenvs.robots.articulations.ant import Ant
from omniisaacgymenvs.tasks.warp.shared.locomotion import LocomotionTask
from omni.isaac.core.utils.torch.rotations import compute_heading_and_up, compute_rot, quat_conjugate
from omni.isaac.core.utils.torch.maths import torch_rand_float, tensor_clamp, unscale
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
from omniisaacgymenvs.tasks.base.rl_task import RLTaskWarp
import numpy as np
import torch
import warp as wp
class AntLocomotionTask(LocomotionTask):
def __init__(
self,
name,
sim_config,
env,
offset=None
) -> None:
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_observations = 60
self._num_actions = 8
self._ant_positions = wp.array([0, 0, 0.5], dtype=wp.float32, device="cpu")
LocomotionTask.__init__(self, name=name, env=env)
return
def set_up_scene(self, scene) -> None:
self.get_ant()
RLTaskWarp.set_up_scene(self, scene)
self._ants = ArticulationView(prim_paths_expr="/World/envs/.*/Ant/torso", name="ant_view", reset_xform_properties=False)
scene.add(self._ants)
return
def get_ant(self):
ant = Ant(prim_path=self.default_zero_env_path + "/Ant", name="Ant", translation=self._ant_positions)
self._sim_config.apply_articulation_settings("Ant", get_prim_at_path(ant.prim_path), self._sim_config.parse_actor_config("Ant"))
def get_robot(self):
return self._ants
def post_reset(self):
self.joint_gears = wp.array([15, 15, 15, 15, 15, 15, 15, 15], dtype=wp.float32, device=self._device)
dof_limits = self._ants.get_dof_limits().to(self._device)
self.dof_limits_lower = wp.zeros(self._ants._num_dof, dtype=wp.float32, device=self._device)
self.dof_limits_upper = wp.zeros(self._ants._num_dof, dtype=wp.float32, device=self._device)
wp.launch(parse_dof_limits, dim=self._ants._num_dof,
inputs=[self.dof_limits_lower, self.dof_limits_upper, dof_limits], device=self._device)
self.motor_effort_ratio = wp.array([1, 1, 1, 1, 1, 1, 1, 1], dtype=wp.float32, device=self._device)
self.dof_at_limit_cost = wp.zeros(self._num_envs, dtype=wp.float32, device=self._device)
force_links = ["front_left_foot", "front_right_foot", "left_back_foot", "right_back_foot"]
self._sensor_indices = wp.array([self._ants._body_indices[j] for j in force_links], device=self._device, dtype=wp.int32)
LocomotionTask.post_reset(self)
def get_dof_at_limit_cost(self):
wp.launch(get_dof_at_limit_cost, dim=(self._num_envs, self._ants._num_dof),
inputs=[self.dof_at_limit_cost, self.obs_buf, self.motor_effort_ratio], device=self._device)
return self.dof_at_limit_cost
@wp.kernel
def get_dof_at_limit_cost(dof_at_limit_cost: wp.array(dtype=wp.float32),
obs_buf: wp.array(dtype=wp.float32, ndim=2),
motor_effort_ratio: wp.array(dtype=wp.float32)):
i, j = wp.tid()
dof_i = j + 12
cost = 0.0
if wp.abs(obs_buf[i, dof_i]) > 0.99:
cost = 1.0
dof_at_limit_cost[i] = cost
@wp.kernel
def parse_dof_limits(dof_limits_lower: wp.array(dtype=wp.float32),
dof_limits_upper: wp.array(dtype=wp.float32),
dof_limits: wp.array(dtype=wp.float32, ndim=3)):
tid = wp.tid()
dof_limits_lower[tid] = dof_limits[0, tid, 0]
dof_limits_upper[tid] = dof_limits[0, tid, 1] | 5,242 | Python | 44.991228 | 136 | 0.685616 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/warp/cartpole.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from omniisaacgymenvs.robots.articulations.cartpole import Cartpole
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
import omni.isaac.core.utils.warp as warp_utils
from omniisaacgymenvs.tasks.base.rl_task import RLTaskWarp
import numpy as np
import torch
import warp as wp
import math
class CartpoleTask(RLTaskWarp):
def __init__(
self,
name,
sim_config,
env,
offset=None
) -> None:
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._cartpole_positions = wp.array([0.0, 0.0, 2.0], dtype=wp.float32)
self._reset_dist = self._task_cfg["env"]["resetDist"]
self._max_push_effort = self._task_cfg["env"]["maxEffort"]
self._max_episode_length = 500
self._num_observations = 4
self._num_actions = 1
RLTaskWarp.__init__(self, name, env)
return
def set_up_scene(self, scene) -> None:
self.get_cartpole()
super().set_up_scene(scene)
self._cartpoles = ArticulationView(prim_paths_expr="/World/envs/.*/Cartpole", name="cartpole_view", reset_xform_properties=False)
scene.add(self._cartpoles)
return
def get_cartpole(self):
cartpole = Cartpole(prim_path=self.default_zero_env_path + "/Cartpole", name="Cartpole", translation=self._cartpole_positions)
# applies articulation settings from the task configuration yaml file
self._sim_config.apply_articulation_settings("Cartpole", get_prim_at_path(cartpole.prim_path), self._sim_config.parse_actor_config("Cartpole"))
def get_observations(self) -> dict:
dof_pos = self._cartpoles.get_joint_positions(clone=False)
dof_vel = self._cartpoles.get_joint_velocities(clone=False)
wp.launch(get_observations, dim=self._num_envs,
inputs=[self.obs_buf, dof_pos, dof_vel, self._cart_dof_idx, self._pole_dof_idx], device=self._device)
observations = {
self._cartpoles.name: {
"obs_buf": self.obs_buf
}
}
return observations
def pre_physics_step(self, actions) -> None:
self.reset_idx()
actions_wp = wp.from_torch(actions)
forces = wp.zeros((self._cartpoles.count, self._cartpoles.num_dof), dtype=wp.float32, device=self._device)
wp.launch(compute_forces, dim=self._num_envs,
inputs=[forces, actions_wp, self._cart_dof_idx, self._max_push_effort], device=self._device)
self._cartpoles.set_joint_efforts(forces)
def reset_idx(self):
reset_env_ids = wp.to_torch(self.reset_buf).nonzero(as_tuple=False).squeeze(-1)
num_resets = len(reset_env_ids)
indices = wp.from_torch(reset_env_ids.to(dtype=torch.int32), dtype=wp.int32)
if num_resets > 0:
wp.launch(reset_idx, num_resets,
inputs=[self.dof_pos, self.dof_vel, indices, self.reset_buf, self.progress_buf, self._cart_dof_idx, self._pole_dof_idx, self._rand_seed],
device=self._device)
# apply resets
self._cartpoles.set_joint_positions(self.dof_pos[indices], indices=indices)
self._cartpoles.set_joint_velocities(self.dof_vel[indices], indices=indices)
def post_reset(self):
self._cart_dof_idx = self._cartpoles.get_dof_index("cartJoint")
self._pole_dof_idx = self._cartpoles.get_dof_index("poleJoint")
self.dof_pos = wp.zeros((self._num_envs, self._cartpoles.num_dof), device=self._device, dtype=wp.float32)
self.dof_vel = wp.zeros((self._num_envs, self._cartpoles.num_dof), device=self._device, dtype=wp.float32)
# randomize all envs
self.reset_idx()
def calculate_metrics(self) -> None:
wp.launch(calculate_metrics, dim=self._num_envs,
inputs=[self.obs_buf, self.rew_buf, self._reset_dist], device=self._device)
def is_done(self) -> None:
wp.launch(is_done, dim=self._num_envs,
inputs=[self.obs_buf, self.reset_buf, self.progress_buf, self._reset_dist, self._max_episode_length],
device=self._device)
@wp.kernel
def reset_idx(dof_pos: wp.array(dtype=wp.float32, ndim=2),
dof_vel: wp.array(dtype=wp.float32, ndim=2),
indices: wp.array(dtype=wp.int32),
reset_buf: wp.array(dtype=wp.int32),
progress_buf: wp.array(dtype=wp.int32),
cart_dof_idx: int,
pole_dof_idx: int,
rand_seed: int):
i = wp.tid()
idx = indices[i]
rand_state = wp.rand_init(rand_seed, i)
# randomize DOF positions
dof_pos[idx, cart_dof_idx] = 1.0 * (1.0 - 2.0 * wp.randf(rand_state))
dof_pos[idx, pole_dof_idx] = 0.125 * warp_utils.PI * (1.0 - 2.0 * wp.randf(rand_state))
# randomize DOF velocities
dof_vel[idx, cart_dof_idx] = 0.5 * (1.0 - 2.0 * wp.randf(rand_state))
dof_vel[idx, pole_dof_idx] = 0.25 * warp_utils.PI * (1.0 - 2.0 * wp.randf(rand_state))
# bookkeeping
progress_buf[idx] = 0
reset_buf[idx] = 0
@wp.kernel
def compute_forces(forces: wp.array(dtype=wp.float32, ndim=2),
actions: wp.array(dtype=wp.float32, ndim=2),
cart_dof_idx: int,
max_push_effort: float):
i = wp.tid()
forces[i, cart_dof_idx] = max_push_effort * actions[i, 0]
@wp.kernel
def get_observations(obs_buf: wp.array(dtype=wp.float32, ndim=2),
dof_pos: wp.indexedarray(dtype=wp.float32, ndim=2),
dof_vel: wp.indexedarray(dtype=wp.float32, ndim=2),
cart_dof_idx: int,
pole_dof_idx: int):
i = wp.tid()
obs_buf[i, 0] = dof_pos[i, cart_dof_idx]
obs_buf[i, 1] = dof_vel[i, cart_dof_idx]
obs_buf[i, 2] = dof_pos[i, pole_dof_idx]
obs_buf[i, 3] = dof_vel[i, pole_dof_idx]
@wp.kernel
def calculate_metrics(obs_buf: wp.array(dtype=wp.float32, ndim=2),
rew_buf: wp.array(dtype=wp.float32),
reset_dist: float):
i = wp.tid()
cart_pos = obs_buf[i, 0]
cart_vel = obs_buf[i, 1]
pole_angle = obs_buf[i, 2]
pole_vel = obs_buf[i, 3]
rew_buf[i] = 1.0 - pole_angle * pole_angle - 0.01 * wp.abs(cart_vel) - 0.005 * wp.abs(pole_vel)
if wp.abs(cart_pos) > reset_dist or wp.abs(pole_angle) > warp_utils.PI / 2.0:
rew_buf[i] = -2.0
@wp.kernel
def is_done(obs_buf: wp.array(dtype=wp.float32, ndim=2),
reset_buf: wp.array(dtype=wp.int32),
progress_buf: wp.array(dtype=wp.int32),
reset_dist: float,
max_episode_length: int):
i = wp.tid()
cart_pos = obs_buf[i, 0]
pole_pos = obs_buf[i, 2]
if wp.abs(cart_pos) > reset_dist or wp.abs(pole_pos) > warp_utils.PI / 2.0 or progress_buf[i] > max_episode_length:
reset_buf[i] = 1
else:
reset_buf[i] = 0
| 8,665 | Python | 38.390909 | 154 | 0.635661 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/warp/shared/locomotion.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from abc import abstractmethod
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
import omni.isaac.core.utils.warp as warp_utils
from omniisaacgymenvs.tasks.base.rl_task import RLTaskWarp
import numpy as np
import torch
import warp as wp
class LocomotionTask(RLTaskWarp):
def __init__(
self,
name,
env,
offset=None
) -> None:
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._max_episode_length = self._task_cfg["env"]["episodeLength"]
self.dof_vel_scale = self._task_cfg["env"]["dofVelocityScale"]
self.angular_velocity_scale = self._task_cfg["env"]["angularVelocityScale"]
self.contact_force_scale = self._task_cfg["env"]["contactForceScale"]
self.power_scale = self._task_cfg["env"]["powerScale"]
self.heading_weight = self._task_cfg["env"]["headingWeight"]
self.up_weight = self._task_cfg["env"]["upWeight"]
self.actions_cost_scale = self._task_cfg["env"]["actionsCost"]
self.energy_cost_scale = self._task_cfg["env"]["energyCost"]
self.joints_at_limit_cost_scale = self._task_cfg["env"]["jointsAtLimitCost"]
self.death_cost = self._task_cfg["env"]["deathCost"]
self.termination_height = self._task_cfg["env"]["terminationHeight"]
self.alive_reward_scale = self._task_cfg["env"]["alive_reward_scale"]
self._num_sensors = 2
RLTaskWarp.__init__(self, name, env)
return
@abstractmethod
def set_up_scene(self, scene) -> None:
pass
@abstractmethod
def get_robot(self):
pass
def get_observations(self) -> dict:
torso_position, torso_rotation = self._robots.get_world_poses(clone=False)
velocities = self._robots.get_velocities(clone=False)
dof_pos = self._robots.get_joint_positions(clone=False)
dof_vel = self._robots.get_joint_velocities(clone=False)
# force sensors attached to the feet
sensor_force_torques = self._robots.get_measured_joint_forces()
wp.launch(get_observations, dim=self._num_envs,
inputs=[self.obs_buf, torso_position, torso_rotation, self._env_pos, velocities, dof_pos, dof_vel,
self.prev_potentials, self.potentials, self.dt, self.target,
self.basis_vec0, self.basis_vec1, self.dof_limits_lower, self.dof_limits_upper, self.dof_vel_scale,
sensor_force_torques, self.contact_force_scale, self.actions, self.angular_velocity_scale,
self._robots._num_dof, self._num_sensors, self._sensor_indices], device=self._device
)
observations = {
self._robots.name: {
"obs_buf": self.obs_buf
}
}
return observations
def pre_physics_step(self, actions) -> None:
self.reset_idx()
actions_wp = wp.from_torch(actions)
self.actions = actions_wp
wp.launch(compute_forces, dim=(self._num_envs, self._robots._num_dof),
inputs=[self.forces, self.actions, self.joint_gears, self.power_scale], device=self._device)
# applies joint torques
self._robots.set_joint_efforts(self.forces)
def reset_idx(self):
reset_env_ids = wp.to_torch(self.reset_buf).nonzero(as_tuple=False).squeeze(-1)
num_resets = len(reset_env_ids)
indices = wp.from_torch(reset_env_ids.to(dtype=torch.int32), dtype=wp.int32)
if num_resets > 0:
wp.launch(reset_dofs, dim=(num_resets, self._robots._num_dof),
inputs=[self.dof_pos, self.dof_vel, self.initial_dof_pos, self.dof_limits_lower, self.dof_limits_upper, indices, self._rand_seed],
device=self._device)
wp.launch(reset_idx, dim=num_resets,
inputs=[self.root_pos, self.root_rot, self.initial_root_pos, self.initial_root_rot, self._env_pos,
self.target, self.prev_potentials, self.potentials, self.dt,
self.reset_buf, self.progress_buf, indices, self._rand_seed],
device=self._device)
# apply resets
self._robots.set_joint_positions(self.dof_pos[indices], indices=indices)
self._robots.set_joint_velocities(self.dof_vel[indices], indices=indices)
self._robots.set_world_poses(self.root_pos[indices], self.root_rot[indices], indices=indices)
self._robots.set_velocities(self.root_vel[indices], indices=indices)
def post_reset(self):
self._robots = self.get_robot()
self.initial_root_pos, self.initial_root_rot = self._robots.get_world_poses()
self.initial_dof_pos = self._robots.get_joint_positions()
# initialize some data used later on
self.basis_vec0 = wp.vec3(1, 0, 0)
self.basis_vec1 = wp.vec3(0, 0, 1)
self.target = wp.vec3(1000, 0, 0)
self.dt = 1.0 / 60.0
# initialize potentials
self.potentials = wp.zeros(self._num_envs, dtype=wp.float32, device=self._device)
self.prev_potentials = wp.zeros(self._num_envs, dtype=wp.float32, device=self._device)
wp.launch(init_potentials, dim=self._num_envs,
inputs=[self.potentials, self.prev_potentials, self.dt], device=self._device)
self.actions = wp.zeros((self.num_envs, self.num_actions), device=self._device, dtype=wp.float32)
self.forces = wp.zeros((self._num_envs, self._robots._num_dof), dtype=wp.float32, device=self._device)
self.dof_pos = wp.zeros((self.num_envs, self._robots._num_dof), device=self._device, dtype=wp.float32)
self.dof_vel = wp.zeros((self.num_envs, self._robots._num_dof), device=self._device, dtype=wp.float32)
self.root_pos = wp.zeros((self.num_envs, 3), device=self._device, dtype=wp.float32)
self.root_rot = wp.zeros((self.num_envs, 4), device=self._device, dtype=wp.float32)
self.root_vel = wp.zeros((self.num_envs, 6), device=self._device, dtype=wp.float32)
# randomize all env
self.reset_idx()
def calculate_metrics(self) -> None:
dof_at_limit_cost = self.get_dof_at_limit_cost()
wp.launch(calculate_metrics, dim=self._num_envs,
inputs=[self.rew_buf, self.obs_buf, self.actions, self.up_weight, self.heading_weight, self.potentials, self.prev_potentials,
self.actions_cost_scale, self.energy_cost_scale, self.termination_height,
self.death_cost, self._robots.num_dof, dof_at_limit_cost, self.alive_reward_scale, self.motor_effort_ratio],
device=self._device
)
def is_done(self) -> None:
wp.launch(is_done, dim=self._num_envs,
inputs=[self.obs_buf, self.termination_height, self.reset_buf, self.progress_buf, self._max_episode_length],
device=self._device
)
#####################################################################
###==========================warp kernels=========================###
#####################################################################
@wp.kernel
def init_potentials(potentials: wp.array(dtype=wp.float32),
prev_potentials: wp.array(dtype=wp.float32),
dt: float):
i = wp.tid()
potentials[i] = -1000.0 / dt
prev_potentials[i] = -1000.0 / dt
@wp.kernel
def reset_idx(root_pos: wp.array(dtype=wp.float32, ndim=2),
root_rot: wp.array(dtype=wp.float32, ndim=2),
initial_root_pos: wp.indexedarray(dtype=wp.float32, ndim=2),
initial_root_rot: wp.indexedarray(dtype=wp.float32, ndim=2),
env_pos: wp.array(dtype=wp.float32, ndim=2),
target: wp.vec3,
prev_potentials: wp.array(dtype=wp.float32),
potentials: wp.array(dtype=wp.float32),
dt: float,
reset_buf: wp.array(dtype=wp.int32),
progress_buf: wp.array(dtype=wp.int32),
indices: wp.array(dtype=wp.int32),
rand_seed: int):
i = wp.tid()
idx = indices[i]
# reset root states
for j in range(3):
root_pos[idx, j] = initial_root_pos[idx, j]
for j in range(4):
root_rot[idx, j] = initial_root_rot[idx, j]
# reset potentials
to_target = target - wp.vec3(initial_root_pos[idx, 0] - env_pos[idx, 0], initial_root_pos[idx, 1] - env_pos[idx, 1], target[2])
prev_potentials[idx] = -wp.length(to_target) / dt
potentials[idx] = -wp.length(to_target) / dt
temp = potentials[idx] - prev_potentials[idx]
# bookkeeping
reset_buf[idx] = 0
progress_buf[idx] = 0
@wp.kernel
def reset_dofs(dof_pos: wp.array(dtype=wp.float32, ndim=2),
dof_vel: wp.array(dtype=wp.float32, ndim=2),
initial_dof_pos: wp.indexedarray(dtype=wp.float32, ndim=2),
dof_limits_lower: wp.array(dtype=wp.float32),
dof_limits_upper: wp.array(dtype=wp.float32),
indices: wp.array(dtype=wp.int32),
rand_seed: int):
i, j = wp.tid()
idx = indices[i]
rand_state = wp.rand_init(rand_seed, i * j + j)
# randomize DOF positions and velocities
dof_pos[idx, j] = wp.clamp(wp.randf(rand_state, -0.2, 0.2) + initial_dof_pos[idx, j], dof_limits_lower[j], dof_limits_upper[j])
dof_vel[idx, j] = wp.randf(rand_state, -0.1, 0.1)
@wp.kernel
def compute_forces(forces: wp.array(dtype=wp.float32, ndim=2),
actions: wp.array(dtype=wp.float32, ndim=2),
joint_gears: wp.array(dtype=wp.float32),
power_scale: float):
i, j = wp.tid()
forces[i, j] = actions[i, j] * joint_gears[j] * power_scale
@wp.func
def get_euler_xyz(q: wp.quat):
qx = 0
qy = 1
qz = 2
qw = 3
# roll (x-axis rotation)
sinr_cosp = 2.0 * (q[qw] * q[qx] + q[qy] * q[qz])
cosr_cosp = q[qw] * q[qw] - q[qx] * q[qx] - q[qy] * q[qy] + q[qz] * q[qz]
roll = wp.atan2(sinr_cosp, cosr_cosp)
# pitch (y-axis rotation)
sinp = 2.0 * (q[qw] * q[qy] - q[qz] * q[qx])
if wp.abs(sinp) >= 1:
pitch = warp_utils.PI / 2.0 * (wp.abs(sinp)/sinp)
else:
pitch = wp.asin(sinp)
# yaw (z-axis rotation)
siny_cosp = 2.0 * (q[qw] * q[qz] + q[qx] * q[qy])
cosy_cosp = q[qw] * q[qw] + q[qx] * q[qx] - q[qy] * q[qy] - q[qz] * q[qz]
yaw = wp.atan2(siny_cosp, cosy_cosp)
rpy = wp.vec3(roll % (2.0 * warp_utils.PI), pitch % (2.0 * warp_utils.PI), yaw % (2.0 * warp_utils.PI))
return rpy
@wp.func
def compute_up_vec(torso_rotation: wp.quat, vec1: wp.vec3):
up_vec = wp.quat_rotate(torso_rotation, vec1)
return up_vec
@wp.func
def compute_heading_vec(torso_rotation: wp.quat, vec0: wp.vec3):
heading_vec = wp.quat_rotate(torso_rotation, vec0)
return heading_vec
@wp.func
def unscale(x:float, lower:float, upper:float):
return (2.0 * x - upper - lower) / (upper - lower)
@wp.func
def normalize_angle(x: float):
return wp.atan2(wp.sin(x), wp.cos(x))
@wp.kernel
def get_observations(
obs_buf: wp.array(dtype=wp.float32, ndim=2),
torso_pos: wp.indexedarray(dtype=wp.float32, ndim=2),
torso_rot: wp.indexedarray(dtype=wp.float32, ndim=2),
env_pos: wp.array(dtype=wp.float32, ndim=2),
velocity: wp.indexedarray(dtype=wp.float32, ndim=2),
dof_pos: wp.indexedarray(dtype=wp.float32, ndim=2),
dof_vel: wp.indexedarray(dtype=wp.float32, ndim=2),
prev_potentials: wp.array(dtype=wp.float32),
potentials: wp.array(dtype=wp.float32),
dt: float,
target: wp.vec3,
basis_vec0: wp.vec3,
basis_vec1: wp.vec3,
dof_limits_lower: wp.array(dtype=wp.float32),
dof_limits_upper: wp.array(dtype=wp.float32),
dof_vel_scale: float,
sensor_force_torques: wp.indexedarray(dtype=wp.float32, ndim=3),
contact_force_scale: float,
actions: wp.array(dtype=wp.float32, ndim=2),
angular_velocity_scale: float,
num_dofs: int,
num_sensors: int,
sensor_indices: wp.array(dtype=wp.int32)
):
i = wp.tid()
torso_position_x = torso_pos[i, 0] - env_pos[i, 0]
torso_position_y = torso_pos[i, 1] - env_pos[i, 1]
torso_position_z = torso_pos[i, 2] - env_pos[i, 2]
to_target = target - wp.vec3(torso_position_x, torso_position_y, target[2])
prev_potentials[i] = potentials[i]
potentials[i] = -wp.length(to_target) / dt
temp = potentials[i] - prev_potentials[i]
torso_quat = wp.quat(torso_rot[i, 1], torso_rot[i, 2], torso_rot[i, 3], torso_rot[i, 0])
up_vec = compute_up_vec(torso_quat, basis_vec1)
up_proj = up_vec[2]
heading_vec = compute_heading_vec(torso_quat, basis_vec0)
target_dir = wp.normalize(to_target)
heading_proj = wp.dot(heading_vec, target_dir)
lin_velocity = wp.vec3(velocity[i, 0], velocity[i, 1], velocity[i, 2])
ang_velocity = wp.vec3(velocity[i, 3], velocity[i, 4], velocity[i, 5])
rpy = get_euler_xyz(torso_quat)
vel_loc = wp.quat_rotate_inv(torso_quat, lin_velocity)
angvel_loc = wp.quat_rotate_inv(torso_quat, ang_velocity)
walk_target_angle = wp.atan2(target[2] - torso_position_z, target[0] - torso_position_x)
angle_to_target = walk_target_angle - rpy[2] # yaw
# obs_buf shapes: 1, 3, 3, 1, 1, 1, 1, 1, num_dofs, num_dofs, num_sensors * 6, num_dofs
obs_offset = 0
obs_buf[i, 0] = torso_position_z
obs_offset = obs_offset + 1
for j in range(3):
obs_buf[i, j+obs_offset] = vel_loc[j]
obs_offset = obs_offset + 3
for j in range(3):
obs_buf[i, j+obs_offset] = angvel_loc[j] * angular_velocity_scale
obs_offset = obs_offset + 3
obs_buf[i, obs_offset+0] = normalize_angle(rpy[2])
obs_buf[i, obs_offset+1] = normalize_angle(rpy[0])
obs_buf[i, obs_offset+2] = normalize_angle(angle_to_target)
obs_buf[i, obs_offset+3] = up_proj
obs_buf[i, obs_offset+4] = heading_proj
obs_offset = obs_offset + 5
for j in range(num_dofs):
obs_buf[i, obs_offset+j] = unscale(dof_pos[i, j], dof_limits_lower[j], dof_limits_upper[j])
obs_offset = obs_offset + num_dofs
for j in range(num_dofs):
obs_buf[i, obs_offset+j] = dof_vel[i, j] * dof_vel_scale
obs_offset = obs_offset + num_dofs
for j in range(num_sensors):
sensor_idx = sensor_indices[j]
for k in range(6):
obs_buf[i, obs_offset+j*6+k] = sensor_force_torques[i, sensor_idx, k] * contact_force_scale
obs_offset = obs_offset + (num_sensors * 6)
for j in range(num_dofs):
obs_buf[i, obs_offset+j] = actions[i, j]
@wp.kernel
def is_done(
obs_buf: wp.array(dtype=wp.float32, ndim=2),
termination_height: float,
reset_buf: wp.array(dtype=wp.int32),
progress_buf: wp.array(dtype=wp.int32),
max_episode_length: int
):
i = wp.tid()
if obs_buf[i, 0] < termination_height or progress_buf[i] >= max_episode_length - 1:
reset_buf[i] = 1
else:
reset_buf[i] = 0
@wp.kernel
def calculate_metrics(
rew_buf: wp.array(dtype=wp.float32),
obs_buf: wp.array(dtype=wp.float32, ndim=2),
actions: wp.array(dtype=wp.float32, ndim=2),
up_weight: float,
heading_weight: float,
potentials: wp.array(dtype=wp.float32),
prev_potentials: wp.array(dtype=wp.float32),
actions_cost_scale: float,
energy_cost_scale: float,
termination_height: float,
death_cost: float,
num_dof: int,
dof_at_limit_cost: wp.array(dtype=wp.float32),
alive_reward_scale: float,
motor_effort_ratio: wp.array(dtype=wp.float32)
):
i = wp.tid()
# heading reward
if obs_buf[i, 11] > 0.8:
heading_reward = heading_weight
else:
heading_reward = heading_weight * obs_buf[i, 11] / 0.8
# aligning up axis of robot and environment
up_reward = 0.0
if obs_buf[i, 10] > 0.93:
up_reward = up_weight
# energy penalty for movement
actions_cost = float(0.0)
electricity_cost = float(0.0)
for j in range(num_dof):
actions_cost = actions_cost + (actions[i, j] * actions[i, j])
electricity_cost = electricity_cost + (wp.abs(actions[i, j] * obs_buf[i, 12+num_dof+j]) * motor_effort_ratio[j])
# reward for duration of staying alive
progress_reward = potentials[i] - prev_potentials[i]
total_reward = (
progress_reward
+ alive_reward_scale
+ up_reward
+ heading_reward
- actions_cost_scale * actions_cost
- energy_cost_scale * electricity_cost
- dof_at_limit_cost[i]
)
# adjust reward for fallen agents
if obs_buf[i, 0] < termination_height:
total_reward = death_cost
rew_buf[i] = total_reward
| 18,233 | Python | 39.52 | 147 | 0.624198 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/base/rl_task.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import asyncio
from abc import abstractmethod
import numpy as np
import omni.isaac.core.utils.warp.tensor as wp_utils
import omni.kit
import omni.usd
import torch
import warp as wp
from gym import spaces
from omni.isaac.cloner import GridCloner
from omni.isaac.core.tasks import BaseTask
from omni.isaac.core.utils.prims import define_prim
from omni.isaac.core.utils.stage import get_current_stage
from omni.isaac.core.utils.types import ArticulationAction
from omni.isaac.gym.tasks.rl_task import RLTaskInterface
from omniisaacgymenvs.utils.domain_randomization.randomize import Randomizer
from pxr import Gf, UsdGeom, UsdLux
class RLTask(RLTaskInterface):
"""This class provides a PyTorch RL-specific interface for setting up RL tasks.
It includes utilities for setting up RL task related parameters,
cloning environments, and data collection for RL algorithms.
"""
def __init__(self, name, env, offset=None) -> None:
"""Initializes RL parameters, cloner object, and buffers.
Args:
name (str): name of the task.
env (VecEnvBase): an instance of the environment wrapper class to register task.
offset (Optional[np.ndarray], optional): offset applied to all assets of the task. Defaults to None.
"""
BaseTask.__init__(self, name=name, offset=offset)
self._rand_seed = self._cfg["seed"]
# optimization flags for pytorch JIT
torch._C._jit_set_nvfuser_enabled(False)
self.test = self._cfg["test"]
self._device = self._cfg["sim_device"]
# set up randomizer for DR
self._dr_randomizer = Randomizer(self._cfg, self._task_cfg)
if self._dr_randomizer.randomize:
import omni.replicator.isaac as dr
self.dr = dr
# set up replicator for camera data collection
self.enable_cameras = self._task_cfg["sim"].get("enable_cameras", False)
if self.enable_cameras:
from omni.replicator.isaac.scripts.writers.pytorch_writer import PytorchWriter
from omni.replicator.isaac.scripts.writers.pytorch_listener import PytorchListener
import omni.replicator.core as rep
self.rep = rep
self.PytorchWriter = PytorchWriter
self.PytorchListener = PytorchListener
print("Task Device:", self._device)
self.randomize_actions = False
self.randomize_observations = False
self.clip_obs = self._task_cfg["env"].get("clipObservations", np.Inf)
self.clip_actions = self._task_cfg["env"].get("clipActions", np.Inf)
self.rl_device = self._cfg.get("rl_device", "cuda:0")
self.control_frequency_inv = self._task_cfg["env"].get("controlFrequencyInv", 1)
self.rendering_interval = self._task_cfg.get("renderingInterval", 1)
# parse default viewport camera position and lookat target and resolution (width, height)
self.camera_position = [10, 10, 3]
self.camera_target = [0, 0, 0]
self.viewport_camera_width = 1280
self.viewport_camera_height = 720
if "viewport" in self._task_cfg:
self.camera_position = self._task_cfg["viewport"].get("camera_position", self.camera_position)
self.camera_target = self._task_cfg["viewport"].get("camera_target", self.camera_target)
self.viewport_camera_width = self._task_cfg["viewport"].get("viewport_camera_width", self.viewport_camera_width)
self.viewport_camera_height = self._task_cfg["viewport"].get("viewport_camera_height", self.viewport_camera_height)
print("RL device: ", self.rl_device)
self._env = env
self.is_extension = False
if not hasattr(self, "_num_agents"):
self._num_agents = 1 # used for multi-agent environments
if not hasattr(self, "_num_states"):
self._num_states = 0
# initialize data spaces (defaults to gym.Box)
if not hasattr(self, "action_space"):
self.action_space = spaces.Box(
np.ones(self.num_actions, dtype=np.float32) * -1.0, np.ones(self.num_actions, dtype=np.float32) * 1.0
)
if not hasattr(self, "observation_space"):
self.observation_space = spaces.Box(
np.ones(self.num_observations, dtype=np.float32) * -np.Inf,
np.ones(self.num_observations, dtype=np.float32) * np.Inf,
)
if not hasattr(self, "state_space"):
self.state_space = spaces.Box(
np.ones(self.num_states, dtype=np.float32) * -np.Inf,
np.ones(self.num_states, dtype=np.float32) * np.Inf,
)
self.cleanup()
def cleanup(self) -> None:
"""Prepares torch buffers for RL data collection."""
# prepare tensors
self.obs_buf = torch.zeros((self._num_envs, self.num_observations), device=self._device, dtype=torch.float)
self.states_buf = torch.zeros((self._num_envs, self.num_states), device=self._device, dtype=torch.float)
self.rew_buf = torch.zeros(self._num_envs, device=self._device, dtype=torch.float)
self.reset_buf = torch.ones(self._num_envs, device=self._device, dtype=torch.long)
self.progress_buf = torch.zeros(self._num_envs, device=self._device, dtype=torch.long)
self.extras = {}
def set_up_scene(
self, scene, replicate_physics=True, collision_filter_global_paths=[], filter_collisions=True, copy_from_source=False
) -> None:
"""Clones environments based on value provided in task config and applies collision filters to mask
collisions across environments.
Args:
scene (Scene): Scene to add objects to.
replicate_physics (bool): Clone physics using PhysX API for better performance.
collision_filter_global_paths (list): Prim paths of global objects that should not have collision masked.
filter_collisions (bool): Mask off collision between environments.
copy_from_source (bool): Copy from source prim when cloning instead of inheriting.
"""
super().set_up_scene(scene)
self._cloner = GridCloner(spacing=self._env_spacing)
self._cloner.define_base_env(self.default_base_env_path)
stage = omni.usd.get_context().get_stage()
UsdGeom.Xform.Define(stage, self.default_zero_env_path)
if self._task_cfg["sim"].get("add_ground_plane", True):
self._ground_plane_path = "/World/defaultGroundPlane"
collision_filter_global_paths.append(self._ground_plane_path)
scene.add_default_ground_plane(prim_path=self._ground_plane_path)
prim_paths = self._cloner.generate_paths("/World/envs/env", self._num_envs)
self._env_pos = self._cloner.clone(
source_prim_path="/World/envs/env_0", prim_paths=prim_paths, replicate_physics=replicate_physics, copy_from_source=copy_from_source
)
self._env_pos = torch.tensor(np.array(self._env_pos), device=self._device, dtype=torch.float)
if filter_collisions:
self._cloner.filter_collisions(
self._env.world.get_physics_context().prim_path,
"/World/collisions",
prim_paths,
collision_filter_global_paths,
)
if self._env.render_enabled:
self.set_initial_camera_params(camera_position=self.camera_position, camera_target=self.camera_target)
if self._task_cfg["sim"].get("add_distant_light", True):
self._create_distant_light()
# initialize capturer for viewport recording
# this has to be called after initializing replicator for DR
if self._cfg.get("enable_recording", False) and not self._dr_randomizer.randomize:
self._env.create_viewport_render_product(resolution=(self.viewport_camera_width, self.viewport_camera_height))
def set_initial_camera_params(self, camera_position, camera_target):
from omni.kit.viewport.utility import get_viewport_from_window_name
from omni.kit.viewport.utility.camera_state import ViewportCameraState
viewport_api_2 = get_viewport_from_window_name("Viewport")
viewport_api_2.set_active_camera("/OmniverseKit_Persp")
camera_state = ViewportCameraState("/OmniverseKit_Persp", viewport_api_2)
camera_state.set_position_world(Gf.Vec3d(camera_position[0], camera_position[1], camera_position[2]), True)
camera_state.set_target_world(Gf.Vec3d(camera_target[0], camera_target[1], camera_target[2]), True)
def _create_distant_light(self, prim_path="/World/defaultDistantLight", intensity=5000):
stage = get_current_stage()
light = UsdLux.DistantLight.Define(stage, prim_path)
light.CreateIntensityAttr().Set(intensity)
def initialize_views(self, scene):
"""Optionally implemented by individual task classes to initialize views used in the task.
This API is required for the extension workflow, where tasks are expected to train on a pre-defined stage.
Args:
scene (Scene): Scene to remove existing views and initialize/add new views.
"""
self._cloner = GridCloner(spacing=self._env_spacing)
pos, _ = self._cloner.get_clone_transforms(self._num_envs)
self._env_pos = torch.tensor(np.array(pos), device=self._device, dtype=torch.float)
if self._env.render_enabled:
# initialize capturer for viewport recording
if self._cfg.get("enable_recording", False) and not self._dr_randomizer.randomize:
self._env.create_viewport_render_product(resolution=(self.viewport_camera_width, self.viewport_camera_height))
@property
def default_base_env_path(self):
"""Retrieves default path to the parent of all env prims.
Returns:
default_base_env_path(str): Defaults to "/World/envs".
"""
return "/World/envs"
@property
def default_zero_env_path(self):
"""Retrieves default path to the first env prim (index 0).
Returns:
default_zero_env_path(str): Defaults to "/World/envs/env_0".
"""
return f"{self.default_base_env_path}/env_0"
def reset(self):
"""Flags all environments for reset."""
self.reset_buf = torch.ones_like(self.reset_buf)
def post_physics_step(self):
"""Processes RL required computations for observations, states, rewards, resets, and extras.
Also maintains progress buffer for tracking step count per environment.
Returns:
obs_buf(torch.Tensor): Tensor of observation data.
rew_buf(torch.Tensor): Tensor of rewards data.
reset_buf(torch.Tensor): Tensor of resets/dones data.
extras(dict): Dictionary of extras data.
"""
self.progress_buf[:] += 1
if self._env.world.is_playing():
self.get_observations()
self.get_states()
self.calculate_metrics()
self.is_done()
self.get_extras()
return self.obs_buf, self.rew_buf, self.reset_buf, self.extras
@property
def world(self):
"""Retrieves the World object for simulation.
Returns:
world(World): Simulation World.
"""
return self._env.world
@property
def cfg(self):
"""Retrieves the main config.
Returns:
cfg(dict): Main config dictionary.
"""
return self._cfg
def set_is_extension(self, is_extension):
self.is_extension = is_extension
class RLTaskWarp(RLTask):
def cleanup(self) -> None:
"""Prepares torch buffers for RL data collection."""
# prepare tensors
self.obs_buf = wp.zeros((self._num_envs, self.num_observations), device=self._device, dtype=wp.float32)
self.states_buf = wp.zeros((self._num_envs, self.num_states), device=self._device, dtype=wp.float32)
self.rew_buf = wp.zeros(self._num_envs, device=self._device, dtype=wp.float32)
self.reset_buf = wp_utils.ones(self._num_envs, device=self._device, dtype=wp.int32)
self.progress_buf = wp.zeros(self._num_envs, device=self._device, dtype=wp.int32)
self.zero_states_buf_torch = torch.zeros(
(self._num_envs, self.num_states), device=self._device, dtype=torch.float32
)
self.extras = {}
def reset(self):
"""Flags all environments for reset."""
wp.launch(reset_progress, dim=self._num_envs, inputs=[self.progress_buf], device=self._device)
def post_physics_step(self):
"""Processes RL required computations for observations, states, rewards, resets, and extras.
Also maintains progress buffer for tracking step count per environment.
Returns:
obs_buf(torch.Tensor): Tensor of observation data.
rew_buf(torch.Tensor): Tensor of rewards data.
reset_buf(torch.Tensor): Tensor of resets/dones data.
extras(dict): Dictionary of extras data.
"""
wp.launch(increment_progress, dim=self._num_envs, inputs=[self.progress_buf], device=self._device)
if self._env.world.is_playing():
self.get_observations()
self.get_states()
self.calculate_metrics()
self.is_done()
self.get_extras()
obs_buf_torch = wp.to_torch(self.obs_buf)
rew_buf_torch = wp.to_torch(self.rew_buf)
reset_buf_torch = wp.to_torch(self.reset_buf)
return obs_buf_torch, rew_buf_torch, reset_buf_torch, self.extras
def get_states(self):
"""API for retrieving states buffer, used for asymmetric AC training.
Returns:
states_buf(torch.Tensor): States buffer.
"""
if self.num_states > 0:
return wp.to_torch(self.states_buf)
else:
return self.zero_states_buf_torch
def set_up_scene(self, scene) -> None:
"""Clones environments based on value provided in task config and applies collision filters to mask
collisions across environments.
Args:
scene (Scene): Scene to add objects to.
"""
super().set_up_scene(scene)
self._env_pos = wp.from_torch(self._env_pos)
@wp.kernel
def increment_progress(progress_buf: wp.array(dtype=wp.int32)):
i = wp.tid()
progress_buf[i] = progress_buf[i] + 1
@wp.kernel
def reset_progress(progress_buf: wp.array(dtype=wp.int32)):
i = wp.tid()
progress_buf[i] = 1
| 16,184 | Python | 42.16 | 143 | 0.653856 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_base.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: base class.
Inherits Gym's RLTask class and abstract base class. Inherited by environment classes. Not directly executed.
Configuration defined in FactoryBase.yaml. Asset info defined in factory_asset_info_franka_table.yaml.
"""
import carb
import hydra
import math
import numpy as np
import torch
from omni.isaac.core.objects import FixedCuboid
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.stage import get_current_stage
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omniisaacgymenvs.robots.articulations.factory_franka import FactoryFranka
from pxr import PhysxSchema, UsdPhysics
import omniisaacgymenvs.tasks.factory.factory_control as fc
from omniisaacgymenvs.tasks.factory.factory_schema_class_base import FactoryABCBase
from omniisaacgymenvs.tasks.factory.factory_schema_config_base import (
FactorySchemaConfigBase,
)
class FactoryBase(RLTask, FactoryABCBase):
def __init__(self, name, sim_config, env) -> None:
"""Initialize instance variables. Initialize RLTask superclass."""
# Set instance variables from base YAML
self._get_base_yaml_params()
self._env_spacing = self.cfg_base.env.env_spacing
# Set instance variables from task and train YAMLs
self._sim_config = sim_config
self._cfg = sim_config.config # CL args, task config, and train config
self._task_cfg = sim_config.task_config # just task config
self._num_envs = sim_config.task_config["env"]["numEnvs"]
self._num_observations = sim_config.task_config["env"]["numObservations"]
self._num_actions = sim_config.task_config["env"]["numActions"]
super().__init__(name, env)
def _get_base_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name="factory_schema_config_base", node=FactorySchemaConfigBase)
config_path = (
"task/FactoryBase.yaml" # relative to Gym's Hydra search path (cfg dir)
)
self.cfg_base = hydra.compose(config_name=config_path)
self.cfg_base = self.cfg_base["task"] # strip superfluous nesting
asset_info_path = "../tasks/factory/yaml/factory_asset_info_franka_table.yaml" # relative to Gym's Hydra search path (cfg dir)
self.asset_info_franka_table = hydra.compose(config_name=asset_info_path)
self.asset_info_franka_table = self.asset_info_franka_table[""][""][""][
"tasks"
]["factory"][
"yaml"
] # strip superfluous nesting
def import_franka_assets(self, add_to_stage=True):
"""Set Franka and table asset options. Import assets."""
self._stage = get_current_stage()
if add_to_stage:
franka_translation = np.array([self.cfg_base.env.franka_depth, 0.0, 0.0])
franka_orientation = np.array([0.0, 0.0, 0.0, 1.0])
franka = FactoryFranka(
prim_path=self.default_zero_env_path + "/franka",
name="franka",
translation=franka_translation,
orientation=franka_orientation,
)
self._sim_config.apply_articulation_settings(
"franka",
get_prim_at_path(franka.prim_path),
self._sim_config.parse_actor_config("franka"),
)
for link_prim in franka.prim.GetChildren():
if link_prim.HasAPI(PhysxSchema.PhysxRigidBodyAPI):
rb = PhysxSchema.PhysxRigidBodyAPI.Get(
self._stage, link_prim.GetPrimPath()
)
rb.GetDisableGravityAttr().Set(True)
rb.GetRetainAccelerationsAttr().Set(False)
if self.cfg_base.sim.add_damping:
rb.GetLinearDampingAttr().Set(
1.0
) # default = 0.0; increased to improve stability
rb.GetMaxLinearVelocityAttr().Set(
1.0
) # default = 1000.0; reduced to prevent CUDA errors
rb.GetAngularDampingAttr().Set(
5.0
) # default = 0.5; increased to improve stability
rb.GetMaxAngularVelocityAttr().Set(
2 / math.pi * 180
) # default = 64.0; reduced to prevent CUDA errors
else:
rb.GetLinearDampingAttr().Set(0.0)
rb.GetMaxLinearVelocityAttr().Set(1000.0)
rb.GetAngularDampingAttr().Set(0.5)
rb.GetMaxAngularVelocityAttr().Set(64 / math.pi * 180)
table_translation = np.array(
[0.0, 0.0, self.cfg_base.env.table_height * 0.5]
)
table_orientation = np.array([1.0, 0.0, 0.0, 0.0])
table = FixedCuboid(
prim_path=self.default_zero_env_path + "/table",
name="table",
translation=table_translation,
orientation=table_orientation,
scale=np.array(
[
self.asset_info_franka_table.table_depth,
self.asset_info_franka_table.table_width,
self.cfg_base.env.table_height,
]
),
size=1.0,
color=np.array([0, 0, 0]),
)
self.parse_controller_spec(add_to_stage=add_to_stage)
def acquire_base_tensors(self):
"""Acquire tensors."""
self.num_dofs = 9
self.env_pos = self._env_pos
self.dof_pos = torch.zeros((self.num_envs, self.num_dofs), device=self.device)
self.dof_vel = torch.zeros((self.num_envs, self.num_dofs), device=self.device)
self.dof_torque = torch.zeros(
(self.num_envs, self.num_dofs), device=self.device
)
self.fingertip_contact_wrench = torch.zeros(
(self.num_envs, 6), device=self.device
)
self.ctrl_target_fingertip_midpoint_pos = torch.zeros(
(self.num_envs, 3), device=self.device
)
self.ctrl_target_fingertip_midpoint_quat = torch.zeros(
(self.num_envs, 4), device=self.device
)
self.ctrl_target_dof_pos = torch.zeros(
(self.num_envs, self.num_dofs), device=self.device
)
self.ctrl_target_gripper_dof_pos = torch.zeros(
(self.num_envs, 2), device=self.device
)
self.ctrl_target_fingertip_contact_wrench = torch.zeros(
(self.num_envs, 6), device=self.device
)
self.prev_actions = torch.zeros(
(self.num_envs, self.num_actions), device=self.device
)
def refresh_base_tensors(self):
"""Refresh tensors."""
if not self.world.is_playing():
return
self.dof_pos = self.frankas.get_joint_positions(clone=False)
self.dof_vel = self.frankas.get_joint_velocities(clone=False)
# Jacobian shape: [4, 11, 6, 9] (root has no Jacobian)
self.franka_jacobian = self.frankas.get_jacobians()
self.franka_mass_matrix = self.frankas.get_mass_matrices(clone=False)
self.arm_dof_pos = self.dof_pos[:, 0:7]
self.arm_mass_matrix = self.franka_mass_matrix[
:, 0:7, 0:7
] # for Franka arm (not gripper)
self.hand_pos, self.hand_quat = self.frankas._hands.get_world_poses(clone=False)
self.hand_pos -= self.env_pos
hand_velocities = self.frankas._hands.get_velocities(clone=False)
self.hand_linvel = hand_velocities[:, 0:3]
self.hand_angvel = hand_velocities[:, 3:6]
(
self.left_finger_pos,
self.left_finger_quat,
) = self.frankas._lfingers.get_world_poses(clone=False)
self.left_finger_pos -= self.env_pos
left_finger_velocities = self.frankas._lfingers.get_velocities(clone=False)
self.left_finger_linvel = left_finger_velocities[:, 0:3]
self.left_finger_angvel = left_finger_velocities[:, 3:6]
self.left_finger_jacobian = self.franka_jacobian[:, 8, 0:6, 0:7]
left_finger_forces = self.frankas._lfingers.get_net_contact_forces(clone=False)
self.left_finger_force = left_finger_forces[:, 0:3]
(
self.right_finger_pos,
self.right_finger_quat,
) = self.frankas._rfingers.get_world_poses(clone=False)
self.right_finger_pos -= self.env_pos
right_finger_velocities = self.frankas._rfingers.get_velocities(clone=False)
self.right_finger_linvel = right_finger_velocities[:, 0:3]
self.right_finger_angvel = right_finger_velocities[:, 3:6]
self.right_finger_jacobian = self.franka_jacobian[:, 9, 0:6, 0:7]
right_finger_forces = self.frankas._rfingers.get_net_contact_forces(clone=False)
self.right_finger_force = right_finger_forces[:, 0:3]
self.gripper_dof_pos = self.dof_pos[:, 7:9]
(
self.fingertip_centered_pos,
self.fingertip_centered_quat,
) = self.frankas._fingertip_centered.get_world_poses(clone=False)
self.fingertip_centered_pos -= self.env_pos
fingertip_centered_velocities = self.frankas._fingertip_centered.get_velocities(
clone=False
)
self.fingertip_centered_linvel = fingertip_centered_velocities[:, 0:3]
self.fingertip_centered_angvel = fingertip_centered_velocities[:, 3:6]
self.fingertip_centered_jacobian = self.franka_jacobian[:, 10, 0:6, 0:7]
self.finger_midpoint_pos = (self.left_finger_pos + self.right_finger_pos) / 2
self.fingertip_midpoint_pos = fc.translate_along_local_z(
pos=self.finger_midpoint_pos,
quat=self.hand_quat,
offset=self.asset_info_franka_table.franka_finger_length,
device=self.device,
)
self.fingertip_midpoint_quat = self.fingertip_centered_quat # always equal
# TODO: Add relative velocity term (see https://dynamicsmotioncontrol487379916.files.wordpress.com/2020/11/21-me258pointmovingrigidbody.pdf)
self.fingertip_midpoint_linvel = self.fingertip_centered_linvel + torch.cross(
self.fingertip_centered_angvel,
(self.fingertip_midpoint_pos - self.fingertip_centered_pos),
dim=1,
)
# From sum of angular velocities (https://physics.stackexchange.com/questions/547698/understanding-addition-of-angular-velocity),
# angular velocity of midpoint w.r.t. world is equal to sum of
# angular velocity of midpoint w.r.t. hand and angular velocity of hand w.r.t. world.
# Midpoint is in sliding contact (i.e., linear relative motion) with hand; angular velocity of midpoint w.r.t. hand is zero.
# Thus, angular velocity of midpoint w.r.t. world is equal to angular velocity of hand w.r.t. world.
self.fingertip_midpoint_angvel = self.fingertip_centered_angvel # always equal
self.fingertip_midpoint_jacobian = (
self.left_finger_jacobian + self.right_finger_jacobian
) * 0.5
def parse_controller_spec(self, add_to_stage):
"""Parse controller specification into lower-level controller configuration."""
cfg_ctrl_keys = {
"num_envs",
"jacobian_type",
"gripper_prop_gains",
"gripper_deriv_gains",
"motor_ctrl_mode",
"gain_space",
"ik_method",
"joint_prop_gains",
"joint_deriv_gains",
"do_motion_ctrl",
"task_prop_gains",
"task_deriv_gains",
"do_inertial_comp",
"motion_ctrl_axes",
"do_force_ctrl",
"force_ctrl_method",
"wrench_prop_gains",
"force_ctrl_axes",
}
self.cfg_ctrl = {cfg_ctrl_key: None for cfg_ctrl_key in cfg_ctrl_keys}
self.cfg_ctrl["num_envs"] = self.num_envs
self.cfg_ctrl["jacobian_type"] = self.cfg_task.ctrl.all.jacobian_type
self.cfg_ctrl["gripper_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.all.gripper_prop_gains, device=self.device
).repeat((self.num_envs, 1))
self.cfg_ctrl["gripper_deriv_gains"] = torch.tensor(
self.cfg_task.ctrl.all.gripper_deriv_gains, device=self.device
).repeat((self.num_envs, 1))
ctrl_type = self.cfg_task.ctrl.ctrl_type
if ctrl_type == "gym_default":
self.cfg_ctrl["motor_ctrl_mode"] = "gym"
self.cfg_ctrl["gain_space"] = "joint"
self.cfg_ctrl["ik_method"] = self.cfg_task.ctrl.gym_default.ik_method
self.cfg_ctrl["joint_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.gym_default.joint_prop_gains, device=self.device
).repeat((self.num_envs, 1))
self.cfg_ctrl["joint_deriv_gains"] = torch.tensor(
self.cfg_task.ctrl.gym_default.joint_deriv_gains, device=self.device
).repeat((self.num_envs, 1))
self.cfg_ctrl["gripper_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.gym_default.gripper_prop_gains, device=self.device
).repeat((self.num_envs, 1))
self.cfg_ctrl["gripper_deriv_gains"] = torch.tensor(
self.cfg_task.ctrl.gym_default.gripper_deriv_gains, device=self.device
).repeat((self.num_envs, 1))
elif ctrl_type == "joint_space_ik":
self.cfg_ctrl["motor_ctrl_mode"] = "manual"
self.cfg_ctrl["gain_space"] = "joint"
self.cfg_ctrl["ik_method"] = self.cfg_task.ctrl.joint_space_ik.ik_method
self.cfg_ctrl["joint_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.joint_space_ik.joint_prop_gains, device=self.device
).repeat((self.num_envs, 1))
self.cfg_ctrl["joint_deriv_gains"] = torch.tensor(
self.cfg_task.ctrl.joint_space_ik.joint_deriv_gains, device=self.device
).repeat((self.num_envs, 1))
self.cfg_ctrl["do_inertial_comp"] = False
elif ctrl_type == "joint_space_id":
self.cfg_ctrl["motor_ctrl_mode"] = "manual"
self.cfg_ctrl["gain_space"] = "joint"
self.cfg_ctrl["ik_method"] = self.cfg_task.ctrl.joint_space_id.ik_method
self.cfg_ctrl["joint_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.joint_space_id.joint_prop_gains, device=self.device
).repeat((self.num_envs, 1))
self.cfg_ctrl["joint_deriv_gains"] = torch.tensor(
self.cfg_task.ctrl.joint_space_id.joint_deriv_gains, device=self.device
).repeat((self.num_envs, 1))
self.cfg_ctrl["do_inertial_comp"] = True
elif ctrl_type == "task_space_impedance":
self.cfg_ctrl["motor_ctrl_mode"] = "manual"
self.cfg_ctrl["gain_space"] = "task"
self.cfg_ctrl["do_motion_ctrl"] = True
self.cfg_ctrl["task_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.task_space_impedance.task_prop_gains,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["task_deriv_gains"] = torch.tensor(
self.cfg_task.ctrl.task_space_impedance.task_deriv_gains,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["do_inertial_comp"] = False
self.cfg_ctrl["motion_ctrl_axes"] = torch.tensor(
self.cfg_task.ctrl.task_space_impedance.motion_ctrl_axes,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["do_force_ctrl"] = False
elif ctrl_type == "operational_space_motion":
self.cfg_ctrl["motor_ctrl_mode"] = "manual"
self.cfg_ctrl["gain_space"] = "task"
self.cfg_ctrl["do_motion_ctrl"] = True
self.cfg_ctrl["task_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.operational_space_motion.task_prop_gains,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["task_deriv_gains"] = torch.tensor(
self.cfg_task.ctrl.operational_space_motion.task_deriv_gains,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["do_inertial_comp"] = True
self.cfg_ctrl["motion_ctrl_axes"] = torch.tensor(
self.cfg_task.ctrl.operational_space_motion.motion_ctrl_axes,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["do_force_ctrl"] = False
elif ctrl_type == "open_loop_force":
self.cfg_ctrl["motor_ctrl_mode"] = "manual"
self.cfg_ctrl["gain_space"] = "task"
self.cfg_ctrl["do_motion_ctrl"] = False
self.cfg_ctrl["do_force_ctrl"] = True
self.cfg_ctrl["force_ctrl_method"] = "open"
self.cfg_ctrl["force_ctrl_axes"] = torch.tensor(
self.cfg_task.ctrl.open_loop_force.force_ctrl_axes, device=self.device
).repeat((self.num_envs, 1))
elif ctrl_type == "closed_loop_force":
self.cfg_ctrl["motor_ctrl_mode"] = "manual"
self.cfg_ctrl["gain_space"] = "task"
self.cfg_ctrl["do_motion_ctrl"] = False
self.cfg_ctrl["do_force_ctrl"] = True
self.cfg_ctrl["force_ctrl_method"] = "closed"
self.cfg_ctrl["wrench_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.closed_loop_force.wrench_prop_gains,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["force_ctrl_axes"] = torch.tensor(
self.cfg_task.ctrl.closed_loop_force.force_ctrl_axes, device=self.device
).repeat((self.num_envs, 1))
elif ctrl_type == "hybrid_force_motion":
self.cfg_ctrl["motor_ctrl_mode"] = "manual"
self.cfg_ctrl["gain_space"] = "task"
self.cfg_ctrl["do_motion_ctrl"] = True
self.cfg_ctrl["task_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.hybrid_force_motion.task_prop_gains,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["task_deriv_gains"] = torch.tensor(
self.cfg_task.ctrl.hybrid_force_motion.task_deriv_gains,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["do_inertial_comp"] = True
self.cfg_ctrl["motion_ctrl_axes"] = torch.tensor(
self.cfg_task.ctrl.hybrid_force_motion.motion_ctrl_axes,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["do_force_ctrl"] = True
self.cfg_ctrl["force_ctrl_method"] = "closed"
self.cfg_ctrl["wrench_prop_gains"] = torch.tensor(
self.cfg_task.ctrl.hybrid_force_motion.wrench_prop_gains,
device=self.device,
).repeat((self.num_envs, 1))
self.cfg_ctrl["force_ctrl_axes"] = torch.tensor(
self.cfg_task.ctrl.hybrid_force_motion.force_ctrl_axes,
device=self.device,
).repeat((self.num_envs, 1))
if add_to_stage:
if self.cfg_ctrl["motor_ctrl_mode"] == "gym":
for i in range(7):
joint_prim = self._stage.GetPrimAtPath(
self.default_zero_env_path
+ f"/franka/panda_link{i}/panda_joint{i+1}"
)
drive = UsdPhysics.DriveAPI.Apply(joint_prim, "angular")
drive.GetStiffnessAttr().Set(
self.cfg_ctrl["joint_prop_gains"][0, i].item() * np.pi / 180
)
drive.GetDampingAttr().Set(
self.cfg_ctrl["joint_deriv_gains"][0, i].item() * np.pi / 180
)
for i in range(2):
joint_prim = self._stage.GetPrimAtPath(
self.default_zero_env_path
+ f"/franka/panda_hand/panda_finger_joint{i+1}"
)
drive = UsdPhysics.DriveAPI.Apply(joint_prim, "linear")
drive.GetStiffnessAttr().Set(
self.cfg_ctrl["gripper_deriv_gains"][0, i].item()
)
drive.GetDampingAttr().Set(
self.cfg_ctrl["gripper_deriv_gains"][0, i].item()
)
elif self.cfg_ctrl["motor_ctrl_mode"] == "manual":
for i in range(7):
joint_prim = self._stage.GetPrimAtPath(
self.default_zero_env_path
+ f"/franka/panda_link{i}/panda_joint{i+1}"
)
joint_prim.RemoveAPI(UsdPhysics.DriveAPI, "angular")
drive = UsdPhysics.DriveAPI.Apply(joint_prim, "None")
drive.GetStiffnessAttr().Set(0.0)
drive.GetDampingAttr().Set(0.0)
for i in range(2):
joint_prim = self._stage.GetPrimAtPath(
self.default_zero_env_path
+ f"/franka/panda_hand/panda_finger_joint{i+1}"
)
joint_prim.RemoveAPI(UsdPhysics.DriveAPI, "linear")
drive = UsdPhysics.DriveAPI.Apply(joint_prim, "None")
drive.GetStiffnessAttr().Set(0.0)
drive.GetDampingAttr().Set(0.0)
def generate_ctrl_signals(self):
"""Get Jacobian. Set Franka DOF position targets or DOF torques."""
# Get desired Jacobian
if self.cfg_ctrl["jacobian_type"] == "geometric":
self.fingertip_midpoint_jacobian_tf = self.fingertip_midpoint_jacobian
elif self.cfg_ctrl["jacobian_type"] == "analytic":
self.fingertip_midpoint_jacobian_tf = fc.get_analytic_jacobian(
fingertip_quat=self.fingertip_quat,
fingertip_jacobian=self.fingertip_midpoint_jacobian,
num_envs=self.num_envs,
device=self.device,
)
# Set PD joint pos target or joint torque
if self.cfg_ctrl["motor_ctrl_mode"] == "gym":
self._set_dof_pos_target()
elif self.cfg_ctrl["motor_ctrl_mode"] == "manual":
self._set_dof_torque()
def _set_dof_pos_target(self):
"""Set Franka DOF position target to move fingertips towards target pose."""
self.ctrl_target_dof_pos = fc.compute_dof_pos_target(
cfg_ctrl=self.cfg_ctrl,
arm_dof_pos=self.arm_dof_pos,
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
jacobian=self.fingertip_midpoint_jacobian_tf,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
ctrl_target_gripper_dof_pos=self.ctrl_target_gripper_dof_pos,
device=self.device,
)
self.frankas.set_joint_position_targets(positions=self.ctrl_target_dof_pos)
def _set_dof_torque(self):
"""Set Franka DOF torque to move fingertips towards target pose."""
self.dof_torque = fc.compute_dof_torque(
cfg_ctrl=self.cfg_ctrl,
dof_pos=self.dof_pos,
dof_vel=self.dof_vel,
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
fingertip_midpoint_linvel=self.fingertip_midpoint_linvel,
fingertip_midpoint_angvel=self.fingertip_midpoint_angvel,
left_finger_force=self.left_finger_force,
right_finger_force=self.right_finger_force,
jacobian=self.fingertip_midpoint_jacobian_tf,
arm_mass_matrix=self.arm_mass_matrix,
ctrl_target_gripper_dof_pos=self.ctrl_target_gripper_dof_pos,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
ctrl_target_fingertip_contact_wrench=self.ctrl_target_fingertip_contact_wrench,
device=self.device,
)
self.frankas.set_joint_efforts(efforts=self.dof_torque)
def enable_gravity(self, gravity_mag):
"""Enable gravity."""
gravity = [0.0, 0.0, -gravity_mag]
self.world._physics_sim_view.set_gravity(
carb.Float3(gravity[0], gravity[1], gravity[2])
)
def disable_gravity(self):
"""Disable gravity."""
gravity = [0.0, 0.0, 0.0]
self.world._physics_sim_view.set_gravity(
carb.Float3(gravity[0], gravity[1], gravity[2])
)
| 26,820 | Python | 45.88986 | 148 | 0.588479 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_schema_config_task.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: schema for task class configurations.
Used by Hydra. Defines template for task class YAML files. Not enforced.
"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class Sim:
use_gpu_pipeline: bool # use GPU pipeline
dt: float # timestep size
gravity: list[float] # gravity vector
@dataclass
class Env:
numObservations: int # number of observations per env; camel case required by VecTask
numActions: int # number of actions per env; camel case required by VecTask
numEnvs: int # number of envs; camel case required by VecTask
@dataclass
class Randomize:
franka_arm_initial_dof_pos: list[float] # initial Franka arm DOF position (7)
@dataclass
class RL:
pos_action_scale: list[
float
] # scale on pos displacement targets (3), to convert [-1, 1] to +- x m
rot_action_scale: list[
float
] # scale on rot displacement targets (3), to convert [-1, 1] to +- x rad
force_action_scale: list[
float
] # scale on force targets (3), to convert [-1, 1] to +- x N
torque_action_scale: list[
float
] # scale on torque targets (3), to convert [-1, 1] to +- x Nm
clamp_rot: bool # clamp small values of rotation actions to zero
clamp_rot_thresh: float # smallest acceptable value
max_episode_length: int # max number of timesteps in each episode
@dataclass
class All:
jacobian_type: str # map between joint space and task space via geometric or analytic Jacobian {geometric, analytic}
gripper_prop_gains: list[
float
] # proportional gains on left and right Franka gripper finger DOF position (2)
gripper_deriv_gains: list[
float
] # derivative gains on left and right Franka gripper finger DOF position (2)
@dataclass
class GymDefault:
joint_prop_gains: list[int] # proportional gains on Franka arm DOF position (7)
joint_deriv_gains: list[int] # derivative gains on Franka arm DOF position (7)
@dataclass
class JointSpaceIK:
ik_method: str # use Jacobian pseudoinverse, Jacobian transpose, damped least squares or adaptive SVD {pinv, trans, dls, svd}
joint_prop_gains: list[int]
joint_deriv_gains: list[int]
@dataclass
class JointSpaceID:
ik_method: str
joint_prop_gains: list[int]
joint_deriv_gains: list[int]
@dataclass
class TaskSpaceImpedance:
motion_ctrl_axes: list[bool] # axes for which to enable motion control {0, 1} (6)
task_prop_gains: list[float] # proportional gains on Franka fingertip pose (6)
task_deriv_gains: list[float] # derivative gains on Franka fingertip pose (6)
@dataclass
class OperationalSpaceMotion:
motion_ctrl_axes: list[bool]
task_prop_gains: list[float]
task_deriv_gains: list[float]
@dataclass
class OpenLoopForce:
force_ctrl_axes: list[bool] # axes for which to enable force control {0, 1} (6)
@dataclass
class ClosedLoopForce:
force_ctrl_axes: list[bool]
wrench_prop_gains: list[float] # proportional gains on Franka finger force (6)
@dataclass
class HybridForceMotion:
motion_ctrl_axes: list[bool]
task_prop_gains: list[float]
task_deriv_gains: list[float]
force_ctrl_axes: list[bool]
wrench_prop_gains: list[float]
@dataclass
class Ctrl:
ctrl_type: str # {gym_default,
# joint_space_ik,
# joint_space_id,
# task_space_impedance,
# operational_space_motion,
# open_loop_force,
# closed_loop_force,
# hybrid_force_motion}
gym_default: GymDefault
joint_space_ik: JointSpaceIK
joint_space_id: JointSpaceID
task_space_impedance: TaskSpaceImpedance
operational_space_motion: OperationalSpaceMotion
open_loop_force: OpenLoopForce
closed_loop_force: ClosedLoopForce
hybrid_force_motion: HybridForceMotion
@dataclass
class FactorySchemaConfigTask:
name: str
physics_engine: str
sim: Sim
env: Env
rl: RL
ctrl: Ctrl
| 5,517 | Python | 30.895954 | 130 | 0.719413 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_task_nut_bolt_place.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
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"""Factory: Class for nut-bolt place task.
Inherits nut-bolt environment class and abstract task class (not enforced). Can be executed with
PYTHON_PATH omniisaacgymenvs/scripts/rlgames_train.py task=FactoryTaskNutBoltPlace
"""
import asyncio
import hydra
import math
import omegaconf
import torch
from typing import Tuple
import omni.kit
from omni.isaac.core.simulation_context import SimulationContext
import omni.isaac.core.utils.torch as torch_utils
from omni.isaac.core.utils.torch.transformations import tf_combine
import omniisaacgymenvs.tasks.factory.factory_control as fc
from omniisaacgymenvs.tasks.factory.factory_env_nut_bolt import FactoryEnvNutBolt
from omniisaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from omniisaacgymenvs.tasks.factory.factory_schema_config_task import (
FactorySchemaConfigTask,
)
class FactoryTaskNutBoltPlace(FactoryEnvNutBolt, FactoryABCTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
"""Initialize environment superclass. Initialize instance variables."""
super().__init__(name, sim_config, env)
self._get_task_yaml_params()
def _get_task_yaml_params(self) -> None:
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name="factory_schema_config_task", node=FactorySchemaConfigTask)
self.cfg_task = omegaconf.OmegaConf.create(self._task_cfg)
self.max_episode_length = (
self.cfg_task.rl.max_episode_length
) # required instance var for VecTask
asset_info_path = "../tasks/factory/yaml/factory_asset_info_nut_bolt.yaml" # relative to Gym's Hydra search path (cfg dir)
self.asset_info_nut_bolt = hydra.compose(config_name=asset_info_path)
self.asset_info_nut_bolt = self.asset_info_nut_bolt[""][""][""]["tasks"][
"factory"
][
"yaml"
] # strip superfluous nesting
ppo_path = "train/FactoryTaskNutBoltPlacePPO.yaml" # relative to Gym's Hydra search path (cfg dir)
self.cfg_ppo = hydra.compose(config_name=ppo_path)
self.cfg_ppo = self.cfg_ppo["train"] # strip superfluous nesting
def post_reset(self) -> None:
"""Reset the world. Called only once, before simulation begins."""
if self.cfg_task.sim.disable_gravity:
self.disable_gravity()
self.acquire_base_tensors()
self._acquire_task_tensors()
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
# Reset all envs
indices = torch.arange(self.num_envs, dtype=torch.int64, device=self.device)
asyncio.ensure_future(
self.reset_idx_async(indices, randomize_gripper_pose=False)
)
def _acquire_task_tensors(self) -> None:
"""Acquire tensors."""
# Nut-bolt tensors
self.nut_base_pos_local = self.bolt_head_heights * torch.tensor(
[0.0, 0.0, 1.0], device=self.device
).repeat((self.num_envs, 1))
bolt_heights = self.bolt_head_heights + self.bolt_shank_lengths
self.bolt_tip_pos_local = bolt_heights * torch.tensor(
[0.0, 0.0, 1.0], device=self.device
).repeat((self.num_envs, 1))
# Keypoint tensors
self.keypoint_offsets = (
self._get_keypoint_offsets(self.cfg_task.rl.num_keypoints)
* self.cfg_task.rl.keypoint_scale
)
self.keypoints_nut = torch.zeros(
(self.num_envs, self.cfg_task.rl.num_keypoints, 3),
dtype=torch.float32,
device=self.device,
)
self.keypoints_bolt = torch.zeros_like(self.keypoints_nut, device=self.device)
self.identity_quat = (
torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
self.actions = torch.zeros(
(self.num_envs, self.num_actions), device=self.device
)
def pre_physics_step(self, actions) -> None:
"""Reset environments. Apply actions from policy. Simulation step called after this method."""
if not self.world.is_playing():
return
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids, randomize_gripper_pose=True)
self.actions = actions.clone().to(
self.device
) # shape = (num_envs, num_actions); values = [-1, 1]
self._apply_actions_as_ctrl_targets(
actions=self.actions, ctrl_target_gripper_dof_pos=0.0, do_scale=True
)
async def pre_physics_step_async(self, actions) -> None:
"""Reset environments. Apply actions from policy. Simulation step called after this method."""
if not self.world.is_playing():
return
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
await self.reset_idx_async(env_ids, randomize_gripper_pose=True)
self.actions = actions.clone().to(
self.device
) # shape = (num_envs, num_actions); values = [-1, 1]
self._apply_actions_as_ctrl_targets(
actions=self.actions,
ctrl_target_gripper_dof_pos=0.0,
do_scale=True,
)
def reset_idx(self, env_ids, randomize_gripper_pose) -> None:
"""Reset specified environments."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
# Close gripper onto nut
self.disable_gravity() # to prevent nut from falling
self._close_gripper(sim_steps=self.cfg_task.env.num_gripper_close_sim_steps)
self.enable_gravity(gravity_mag=self.cfg_task.sim.gravity_mag)
if randomize_gripper_pose:
self._randomize_gripper_pose(
env_ids, sim_steps=self.cfg_task.env.num_gripper_move_sim_steps
)
self._reset_buffers(env_ids)
async def reset_idx_async(self, env_ids, randomize_gripper_pose) -> None:
"""Reset specified environments."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
# Close gripper onto nut
self.disable_gravity() # to prevent nut from falling
await self._close_gripper_async(
sim_steps=self.cfg_task.env.num_gripper_close_sim_steps
)
self.enable_gravity(gravity_mag=self.cfg_task.sim.gravity_mag)
if randomize_gripper_pose:
await self._randomize_gripper_pose_async(
env_ids, sim_steps=self.cfg_task.env.num_gripper_move_sim_steps
)
self._reset_buffers(env_ids)
def _reset_franka(self, env_ids) -> None:
"""Reset DOF states and DOF targets of Franka."""
self.dof_pos[env_ids] = torch.cat(
(
torch.tensor(
self.cfg_task.randomize.franka_arm_initial_dof_pos,
device=self.device,
).repeat((len(env_ids), 1)),
(self.nut_widths_max * 0.5)
* 1.1, # buffer on gripper DOF pos to prevent initial contact
(self.nut_widths_max * 0.5) * 1.1,
), # buffer on gripper DOF pos to prevent initial contact
dim=-1,
) # shape = (num_envs, num_dofs)
self.dof_vel[env_ids] = 0.0 # shape = (num_envs, num_dofs)
self.ctrl_target_dof_pos[env_ids] = self.dof_pos[env_ids]
indices = env_ids.to(dtype=torch.int32)
self.frankas.set_joint_positions(self.dof_pos[env_ids], indices=indices)
self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)
def _reset_object(self, env_ids) -> None:
"""Reset root states of nut and bolt."""
# Randomize root state of nut within gripper
self.nut_pos[env_ids, 0] = 0.0
self.nut_pos[env_ids, 1] = 0.0
fingertip_midpoint_pos_reset = 0.58781 # self.fingertip_midpoint_pos at reset
nut_base_pos_local = self.bolt_head_heights.squeeze(-1)
self.nut_pos[env_ids, 2] = fingertip_midpoint_pos_reset - nut_base_pos_local
nut_noise_pos_in_gripper = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
nut_noise_pos_in_gripper = nut_noise_pos_in_gripper @ torch.diag(
torch.tensor(
self.cfg_task.randomize.nut_noise_pos_in_gripper, device=self.device
)
)
self.nut_pos[env_ids, :] += nut_noise_pos_in_gripper[env_ids]
nut_rot_euler = torch.tensor(
[0.0, 0.0, math.pi * 0.5], device=self.device
).repeat(len(env_ids), 1)
nut_noise_rot_in_gripper = 2 * (
torch.rand(self.num_envs, dtype=torch.float32, device=self.device) - 0.5
) # [-1, 1]
nut_noise_rot_in_gripper *= self.cfg_task.randomize.nut_noise_rot_in_gripper
nut_rot_euler[:, 2] += nut_noise_rot_in_gripper
nut_rot_quat = torch_utils.quat_from_euler_xyz(
nut_rot_euler[:, 0], nut_rot_euler[:, 1], nut_rot_euler[:, 2]
)
self.nut_quat[env_ids, :] = nut_rot_quat
self.nut_linvel[env_ids, :] = 0.0
self.nut_angvel[env_ids, :] = 0.0
indices = env_ids.to(dtype=torch.int32)
self.nuts.set_world_poses(
self.nut_pos[env_ids] + self.env_pos[env_ids],
self.nut_quat[env_ids],
indices,
)
self.nuts.set_velocities(
torch.cat((self.nut_linvel[env_ids], self.nut_angvel[env_ids]), dim=1),
indices,
)
# Randomize root state of bolt
bolt_noise_xy = 2 * (
torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
bolt_noise_xy = bolt_noise_xy @ torch.diag(
torch.tensor(
self.cfg_task.randomize.bolt_pos_xy_noise,
dtype=torch.float32,
device=self.device,
)
)
self.bolt_pos[env_ids, 0] = (
self.cfg_task.randomize.bolt_pos_xy_initial[0] + bolt_noise_xy[env_ids, 0]
)
self.bolt_pos[env_ids, 1] = (
self.cfg_task.randomize.bolt_pos_xy_initial[1] + bolt_noise_xy[env_ids, 1]
)
self.bolt_pos[env_ids, 2] = self.cfg_base.env.table_height
self.bolt_quat[env_ids, :] = torch.tensor(
[1.0, 0.0, 0.0, 0.0], dtype=torch.float32, device=self.device
).repeat(len(env_ids), 1)
indices = env_ids.to(dtype=torch.int32)
self.bolts.set_world_poses(
self.bolt_pos[env_ids] + self.env_pos[env_ids],
self.bolt_quat[env_ids],
indices,
)
def _reset_buffers(self, env_ids) -> None:
"""Reset buffers."""
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _apply_actions_as_ctrl_targets(
self, actions, ctrl_target_gripper_dof_pos, do_scale
) -> None:
"""Apply actions from policy as position/rotation/force/torque targets."""
# Interpret actions as target pos displacements and set pos target
pos_actions = actions[:, 0:3]
if do_scale:
pos_actions = pos_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.pos_action_scale, device=self.device)
)
self.ctrl_target_fingertip_midpoint_pos = (
self.fingertip_midpoint_pos + pos_actions
)
# Interpret actions as target rot (axis-angle) displacements
rot_actions = actions[:, 3:6]
if do_scale:
rot_actions = rot_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.rot_action_scale, device=self.device)
)
# Convert to quat and set rot target
angle = torch.norm(rot_actions, p=2, dim=-1)
axis = rot_actions / angle.unsqueeze(-1)
rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis)
if self.cfg_task.rl.clamp_rot:
rot_actions_quat = torch.where(
angle.unsqueeze(-1).repeat(1, 4) > self.cfg_task.rl.clamp_rot_thresh,
rot_actions_quat,
torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(
self.num_envs, 1
),
)
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(
rot_actions_quat, self.fingertip_midpoint_quat
)
if self.cfg_ctrl["do_force_ctrl"]:
# Interpret actions as target forces and target torques
force_actions = actions[:, 6:9]
if do_scale:
force_actions = force_actions @ torch.diag(
torch.tensor(
self.cfg_task.rl.force_action_scale, device=self.device
)
)
torque_actions = actions[:, 9:12]
if do_scale:
torque_actions = torque_actions @ torch.diag(
torch.tensor(
self.cfg_task.rl.torque_action_scale, device=self.device
)
)
self.ctrl_target_fingertip_contact_wrench = torch.cat(
(force_actions, torque_actions), dim=-1
)
self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos
self.generate_ctrl_signals()
def post_physics_step(
self,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Step buffers. Refresh tensors. Compute observations and reward. Reset environments."""
self.progress_buf[:] += 1
if self.world.is_playing():
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.get_observations()
self.calculate_metrics()
self.get_extras()
return self.obs_buf, self.rew_buf, self.reset_buf, self.extras
def _refresh_task_tensors(self) -> None:
"""Refresh tensors."""
# Compute pos of keypoints on gripper, nut, and bolt in world frame
for idx, keypoint_offset in enumerate(self.keypoint_offsets):
self.keypoints_nut[:, idx] = tf_combine(
self.nut_quat,
self.nut_pos,
self.identity_quat,
(keypoint_offset + self.nut_base_pos_local),
)[1]
self.keypoints_bolt[:, idx] = tf_combine(
self.bolt_quat,
self.bolt_pos,
self.identity_quat,
(keypoint_offset + self.bolt_tip_pos_local),
)[1]
def get_observations(self) -> dict:
"""Compute observations."""
# Shallow copies of tensors
obs_tensors = [
self.fingertip_midpoint_pos,
self.fingertip_midpoint_quat,
self.fingertip_midpoint_linvel,
self.fingertip_midpoint_angvel,
self.nut_pos,
self.nut_quat,
self.bolt_pos,
self.bolt_quat,
]
if self.cfg_task.rl.add_obs_bolt_tip_pos:
obs_tensors += [self.bolt_tip_pos_local]
self.obs_buf = torch.cat(
obs_tensors, dim=-1
) # shape = (num_envs, num_observations)
observations = {self.frankas.name: {"obs_buf": self.obs_buf}}
return observations
def calculate_metrics(self) -> None:
"""Update reset and reward buffers."""
self._update_reset_buf()
self._update_rew_buf()
def _update_reset_buf(self) -> None:
"""Assign environments for reset if successful or failed."""
# If max episode length has been reached
self.reset_buf[:] = torch.where(
self.progress_buf[:] >= self.max_episode_length - 1,
torch.ones_like(self.reset_buf),
self.reset_buf,
)
def _update_rew_buf(self) -> None:
"""Compute reward at current timestep."""
keypoint_reward = -self._get_keypoint_dist()
action_penalty = (
torch.norm(self.actions, p=2, dim=-1)
* self.cfg_task.rl.action_penalty_scale
)
self.rew_buf[:] = (
keypoint_reward * self.cfg_task.rl.keypoint_reward_scale
- action_penalty * self.cfg_task.rl.action_penalty_scale
)
# In this policy, episode length is constant across all envs
is_last_step = self.progress_buf[0] == self.max_episode_length - 1
if is_last_step:
# Check if nut is close enough to bolt
is_nut_close_to_bolt = self._check_nut_close_to_bolt()
self.rew_buf[:] += is_nut_close_to_bolt * self.cfg_task.rl.success_bonus
self.extras["successes"] = torch.mean(is_nut_close_to_bolt.float())
def _get_keypoint_offsets(self, num_keypoints) -> torch.Tensor:
"""Get uniformly-spaced keypoints along a line of unit length, centered at 0."""
keypoint_offsets = torch.zeros((num_keypoints, 3), device=self.device)
keypoint_offsets[:, -1] = (
torch.linspace(0.0, 1.0, num_keypoints, device=self.device) - 0.5
)
return keypoint_offsets
def _get_keypoint_dist(self) -> torch.Tensor:
"""Get keypoint distance between nut and bolt."""
keypoint_dist = torch.sum(
torch.norm(self.keypoints_bolt - self.keypoints_nut, p=2, dim=-1), dim=-1
)
return keypoint_dist
def _randomize_gripper_pose(self, env_ids, sim_steps) -> None:
"""Move gripper to random pose."""
# Step once to update PhysX with new joint positions and velocities from reset_franka()
SimulationContext.step(self.world, render=True)
# Set target pos above table
self.ctrl_target_fingertip_midpoint_pos = torch.tensor(
[0.0, 0.0, self.cfg_base.env.table_height], device=self.device
) + torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_pos_initial, device=self.device
)
self.ctrl_target_fingertip_midpoint_pos = (
self.ctrl_target_fingertip_midpoint_pos.unsqueeze(0).repeat(
self.num_envs, 1
)
)
fingertip_midpoint_pos_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
fingertip_midpoint_pos_noise = fingertip_midpoint_pos_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_pos_noise, device=self.device
)
)
self.ctrl_target_fingertip_midpoint_pos += fingertip_midpoint_pos_noise
# Set target rot
ctrl_target_fingertip_midpoint_euler = (
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_rot_initial,
device=self.device,
)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
fingertip_midpoint_rot_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
fingertip_midpoint_rot_noise = fingertip_midpoint_rot_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_rot_noise, device=self.device
)
)
ctrl_target_fingertip_midpoint_euler += fingertip_midpoint_rot_noise
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
ctrl_target_fingertip_midpoint_euler[:, 0],
ctrl_target_fingertip_midpoint_euler[:, 1],
ctrl_target_fingertip_midpoint_euler[:, 2],
)
# Step sim and render
for _ in range(sim_steps):
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
pos_error, axis_angle_error = fc.get_pose_error(
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
jacobian_type=self.cfg_ctrl["jacobian_type"],
rot_error_type="axis_angle",
)
delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1)
actions = torch.zeros(
(self.num_envs, self.cfg_task.env.numActions), device=self.device
)
actions[:, :6] = delta_hand_pose
self._apply_actions_as_ctrl_targets(
actions=actions,
ctrl_target_gripper_dof_pos=0.0,
do_scale=False,
)
SimulationContext.step(self.world, render=True)
self.dof_vel[env_ids, :] = torch.zeros_like(self.dof_vel[env_ids])
indices = env_ids.to(dtype=torch.int32)
self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)
# Step once to update PhysX with new joint velocities
SimulationContext.step(self.world, render=True)
async def _randomize_gripper_pose_async(self, env_ids, sim_steps) -> None:
"""Move gripper to random pose."""
# Step once to update PhysX with new joint positions and velocities from reset_franka()
self.world.physics_sim_view.flush()
await omni.kit.app.get_app().next_update_async()
# Set target pos above table
self.ctrl_target_fingertip_midpoint_pos = torch.tensor(
[0.0, 0.0, self.cfg_base.env.table_height], device=self.device
) + torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_pos_initial, device=self.device
)
self.ctrl_target_fingertip_midpoint_pos = (
self.ctrl_target_fingertip_midpoint_pos.unsqueeze(0).repeat(
self.num_envs, 1
)
)
fingertip_midpoint_pos_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
fingertip_midpoint_pos_noise = fingertip_midpoint_pos_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_pos_noise, device=self.device
)
)
self.ctrl_target_fingertip_midpoint_pos += fingertip_midpoint_pos_noise
# Set target rot
ctrl_target_fingertip_midpoint_euler = (
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_rot_initial,
device=self.device,
)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
fingertip_midpoint_rot_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
fingertip_midpoint_rot_noise = fingertip_midpoint_rot_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_rot_noise, device=self.device
)
)
ctrl_target_fingertip_midpoint_euler += fingertip_midpoint_rot_noise
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
ctrl_target_fingertip_midpoint_euler[:, 0],
ctrl_target_fingertip_midpoint_euler[:, 1],
ctrl_target_fingertip_midpoint_euler[:, 2],
)
# Step sim and render
for _ in range(sim_steps):
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
pos_error, axis_angle_error = fc.get_pose_error(
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
jacobian_type=self.cfg_ctrl["jacobian_type"],
rot_error_type="axis_angle",
)
delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1)
actions = torch.zeros(
(self.num_envs, self.cfg_task.env.numActions), device=self.device
)
actions[:, :6] = delta_hand_pose
self._apply_actions_as_ctrl_targets(
actions=actions,
ctrl_target_gripper_dof_pos=0.0,
do_scale=False,
)
self.world.physics_sim_view.flush()
await omni.kit.app.get_app().next_update_async()
self.dof_vel[env_ids, :] = torch.zeros_like(self.dof_vel[env_ids])
indices = env_ids.to(dtype=torch.int32)
self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)
# Step once to update PhysX with new joint velocities
self.world.physics_sim_view.flush()
await omni.kit.app.get_app().next_update_async()
def _close_gripper(self, sim_steps) -> None:
"""Fully close gripper using controller. Called outside RL loop (i.e., after last step of episode)."""
self._move_gripper_to_dof_pos(gripper_dof_pos=0.0, sim_steps=sim_steps)
def _move_gripper_to_dof_pos(self, gripper_dof_pos, sim_steps) -> None:
"""Move gripper fingers to specified DOF position using controller."""
delta_hand_pose = torch.zeros(
(self.num_envs, 6), device=self.device
) # No hand motion
# Step sim
for _ in range(sim_steps):
self._apply_actions_as_ctrl_targets(
delta_hand_pose, gripper_dof_pos, do_scale=False
)
SimulationContext.step(self.world, render=True)
async def _close_gripper_async(self, sim_steps) -> None:
"""Fully close gripper using controller. Called outside RL loop (i.e., after last step of episode)."""
await self._move_gripper_to_dof_pos_async(
gripper_dof_pos=0.0, sim_steps=sim_steps
)
async def _move_gripper_to_dof_pos_async(
self, gripper_dof_pos, sim_steps
) -> None:
"""Move gripper fingers to specified DOF position using controller."""
delta_hand_pose = torch.zeros(
(self.num_envs, 6), device=self.device
) # No hand motion
# Step sim
for _ in range(sim_steps):
self._apply_actions_as_ctrl_targets(
delta_hand_pose, gripper_dof_pos, do_scale=False
)
self.world.physics_sim_view.flush()
await omni.kit.app.get_app().next_update_async()
def _check_nut_close_to_bolt(self) -> torch.Tensor:
"""Check if nut is close to bolt."""
keypoint_dist = torch.norm(
self.keypoints_bolt - self.keypoints_nut, p=2, dim=-1
)
is_nut_close_to_bolt = torch.where(
torch.sum(keypoint_dist, dim=-1) < self.cfg_task.rl.close_error_thresh,
torch.ones_like(self.progress_buf),
torch.zeros_like(self.progress_buf),
)
return is_nut_close_to_bolt
| 28,968 | Python | 37.780455 | 131 | 0.594518 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_schema_config_env.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: schema for environment class configurations.
Used by Hydra. Defines template for environment class YAML files.
"""
from dataclasses import dataclass
@dataclass
class Sim:
disable_franka_collisions: bool # disable collisions between Franka and objects
@dataclass
class Env:
env_name: str # name of scene
@dataclass
class FactorySchemaConfigEnv:
sim: Sim
env: Env
| 1,960 | Python | 36.711538 | 84 | 0.776531 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_schema_class_task.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: abstract base class for task classes.
Inherits ABC class. Inherited by task classes. Defines template for task classes.
"""
from abc import ABC, abstractmethod
class FactoryABCTask(ABC):
@abstractmethod
def __init__(self):
"""Initialize instance variables. Initialize environment superclass."""
pass
@abstractmethod
def _get_task_yaml_params(self):
"""Initialize instance variables from YAML files."""
pass
@abstractmethod
def _acquire_task_tensors(self):
"""Acquire tensors."""
pass
@abstractmethod
def _refresh_task_tensors(self):
"""Refresh tensors."""
pass
@abstractmethod
def pre_physics_step(self):
"""Reset environments. Apply actions from policy as controller targets. Simulation step called after this method."""
pass
@abstractmethod
def post_physics_step(self):
"""Step buffers. Refresh tensors. Compute observations and reward."""
pass
@abstractmethod
def get_observations(self):
"""Compute observations."""
pass
@abstractmethod
def calculate_metrics(self):
"""Detect successes and failures. Update reward and reset buffers."""
pass
@abstractmethod
def _update_rew_buf(self):
"""Compute reward at current timestep."""
pass
@abstractmethod
def _update_reset_buf(self):
"""Assign environments for reset if successful or failed."""
pass
@abstractmethod
def reset_idx(self):
"""Reset specified environments."""
pass
@abstractmethod
def _reset_franka(self):
"""Reset DOF states and DOF targets of Franka."""
pass
@abstractmethod
def _reset_object(self):
"""Reset root state of object."""
pass
@abstractmethod
def _reset_buffers(self):
"""Reset buffers."""
pass
| 3,492 | Python | 31.342592 | 124 | 0.69559 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_schema_class_env.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: abstract base class for environment classes.
Inherits ABC class. Inherited by environment classes. Defines template for environment classes.
"""
from abc import ABC, abstractmethod
class FactoryABCEnv(ABC):
@abstractmethod
def __init__(self):
"""Initialize instance variables. Initialize base superclass. Acquire tensors."""
pass
@abstractmethod
def _get_env_yaml_params(self):
"""Initialize instance variables from YAML files."""
pass
@abstractmethod
def set_up_scene(self):
"""Set env options. Import assets. Create actors."""
pass
@abstractmethod
def _import_env_assets(self):
"""Set asset options. Import assets."""
pass
@abstractmethod
def refresh_env_tensors(self):
"""Refresh tensors."""
# NOTE: Tensor refresh functions should be called once per step, before setters.
pass
| 2,489 | Python | 37.906249 | 95 | 0.73644 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_task_nut_bolt_screw.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: Class for nut-bolt screw task.
Inherits nut-bolt environment class and abstract task class (not enforced). Can be executed with
PYTHON_PATH omniisaacgymenvs/scripts/rlgames_train.py task=FactoryTaskNutBoltScrew
"""
import hydra
import math
import omegaconf
import torch
from typing import Tuple
import omni.isaac.core.utils.torch as torch_utils
import omniisaacgymenvs.tasks.factory.factory_control as fc
from omniisaacgymenvs.tasks.factory.factory_env_nut_bolt import FactoryEnvNutBolt
from omniisaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from omniisaacgymenvs.tasks.factory.factory_schema_config_task import (
FactorySchemaConfigTask,
)
class FactoryTaskNutBoltScrew(FactoryEnvNutBolt, FactoryABCTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
"""Initialize environment superclass. Initialize instance variables."""
super().__init__(name, sim_config, env)
self._get_task_yaml_params()
def _get_task_yaml_params(self) -> None:
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name="factory_schema_config_task", node=FactorySchemaConfigTask)
self.cfg_task = omegaconf.OmegaConf.create(self._task_cfg)
self.max_episode_length = (
self.cfg_task.rl.max_episode_length
) # required instance var for VecTask
asset_info_path = "../tasks/factory/yaml/factory_asset_info_nut_bolt.yaml" # relative to Gym's Hydra search path (cfg dir)
self.asset_info_nut_bolt = hydra.compose(config_name=asset_info_path)
self.asset_info_nut_bolt = self.asset_info_nut_bolt[""][""][""]["tasks"][
"factory"
][
"yaml"
] # strip superfluous nesting
ppo_path = "train/FactoryTaskNutBoltScrewPPO.yaml" # relative to Gym's Hydra search path (cfg dir)
self.cfg_ppo = hydra.compose(config_name=ppo_path)
self.cfg_ppo = self.cfg_ppo["train"] # strip superfluous nesting
def post_reset(self) -> None:
"""Reset the world. Called only once, before simulation begins."""
if self.cfg_task.sim.disable_gravity:
self.disable_gravity()
self.acquire_base_tensors()
self._acquire_task_tensors()
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
# Reset all envs
indices = torch.arange(self.num_envs, dtype=torch.int64, device=self.device)
self.reset_idx(indices)
def _acquire_task_tensors(self) -> None:
"""Acquire tensors."""
target_heights = (
self.cfg_base.env.table_height
+ self.bolt_head_heights
+ self.nut_heights * 0.5
)
self.target_pos = target_heights * torch.tensor(
[0.0, 0.0, 1.0], device=self.device
).repeat((self.num_envs, 1))
self.identity_quat = (
torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
self.actions = torch.zeros(
(self.num_envs, self.num_actions), device=self.device
)
def pre_physics_step(self, actions) -> None:
"""Reset environments. Apply actions from policy. Simulation step called after this method."""
if not self.world.is_playing():
return
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.actions = actions.clone().to(
self.device
) # shape = (num_envs, num_actions); values = [-1, 1]
self._apply_actions_as_ctrl_targets(
actions=self.actions, ctrl_target_gripper_dof_pos=0.0, do_scale=True
)
def reset_idx(self, env_ids) -> None:
"""Reset specified environments."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
self._reset_buffers(env_ids)
def _reset_franka(self, env_ids) -> None:
"""Reset DOF states and DOF targets of Franka."""
self.dof_pos[env_ids] = torch.cat(
(
torch.tensor(
self.cfg_task.randomize.franka_arm_initial_dof_pos,
device=self.device,
).repeat((len(env_ids), 1)),
(self.nut_widths_max[env_ids] * 0.5)
* 1.1, # buffer on gripper DOF pos to prevent initial contact
(self.nut_widths_max[env_ids] * 0.5) * 1.1,
), # buffer on gripper DOF pos to prevent initial contact
dim=-1,
) # shape = (num_envs, num_dofs)
self.dof_vel[env_ids] = 0.0 # shape = (num_envs, num_dofs)
self.ctrl_target_dof_pos[env_ids] = self.dof_pos[env_ids]
indices = env_ids.to(dtype=torch.int32)
self.frankas.set_joint_positions(self.dof_pos[env_ids], indices=indices)
self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)
def _reset_object(self, env_ids) -> None:
"""Reset root state of nut."""
nut_pos = self.cfg_base.env.table_height + self.bolt_shank_lengths[env_ids]
self.nut_pos[env_ids, :] = nut_pos * torch.tensor(
[0.0, 0.0, 1.0], device=self.device
).repeat(len(env_ids), 1)
nut_rot = (
self.cfg_task.randomize.nut_rot_initial
* torch.ones((len(env_ids), 1), device=self.device)
* math.pi
/ 180.0
)
self.nut_quat[env_ids, :] = torch.cat(
(
torch.cos(nut_rot * 0.5),
torch.zeros((len(env_ids), 1), device=self.device),
torch.zeros((len(env_ids), 1), device=self.device),
torch.sin(nut_rot * 0.5),
),
dim=-1,
)
self.nut_linvel[env_ids, :] = 0.0
self.nut_angvel[env_ids, :] = 0.0
indices = env_ids.to(dtype=torch.int32)
self.nuts.set_world_poses(
self.nut_pos[env_ids] + self.env_pos[env_ids],
self.nut_quat[env_ids],
indices,
)
self.nuts.set_velocities(
torch.cat((self.nut_linvel[env_ids], self.nut_angvel[env_ids]), dim=1),
indices,
)
def _reset_buffers(self, env_ids) -> None:
"""Reset buffers."""
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _apply_actions_as_ctrl_targets(
self, actions, ctrl_target_gripper_dof_pos, do_scale
) -> None:
"""Apply actions from policy as position/rotation/force/torque targets."""
# Interpret actions as target pos displacements and set pos target
pos_actions = actions[:, 0:3]
if self.cfg_task.rl.unidirectional_pos:
pos_actions[:, 2] = -(pos_actions[:, 2] + 1.0) * 0.5 # [-1, 0]
if do_scale:
pos_actions = pos_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.pos_action_scale, device=self.device)
)
self.ctrl_target_fingertip_midpoint_pos = (
self.fingertip_midpoint_pos + pos_actions
)
# Interpret actions as target rot (axis-angle) displacements
rot_actions = actions[:, 3:6]
if self.cfg_task.rl.unidirectional_rot:
rot_actions[:, 2] = -(rot_actions[:, 2] + 1.0) * 0.5 # [-1, 0]
if do_scale:
rot_actions = rot_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.rot_action_scale, device=self.device)
)
# Convert to quat and set rot target
angle = torch.norm(rot_actions, p=2, dim=-1)
axis = rot_actions / angle.unsqueeze(-1)
rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis)
if self.cfg_task.rl.clamp_rot:
rot_actions_quat = torch.where(
angle.unsqueeze(-1).repeat(1, 4) > self.cfg_task.rl.clamp_rot_thresh,
rot_actions_quat,
torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(
self.num_envs, 1
),
)
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(
rot_actions_quat, self.fingertip_midpoint_quat
)
if self.cfg_ctrl["do_force_ctrl"]:
# Interpret actions as target forces and target torques
force_actions = actions[:, 6:9]
if self.cfg_task.rl.unidirectional_force:
force_actions[:, 2] = -(force_actions[:, 2] + 1.0) * 0.5 # [-1, 0]
if do_scale:
force_actions = force_actions @ torch.diag(
torch.tensor(
self.cfg_task.rl.force_action_scale, device=self.device
)
)
torque_actions = actions[:, 9:12]
if do_scale:
torque_actions = torque_actions @ torch.diag(
torch.tensor(
self.cfg_task.rl.torque_action_scale, device=self.device
)
)
self.ctrl_target_fingertip_contact_wrench = torch.cat(
(force_actions, torque_actions), dim=-1
)
self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos
self.generate_ctrl_signals()
def post_physics_step(
self,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Step buffers. Refresh tensors. Compute observations and reward. Reset environments."""
self.progress_buf[:] += 1
if self.world.is_playing():
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.get_observations()
self.calculate_metrics()
self.get_extras()
return self.obs_buf, self.rew_buf, self.reset_buf, self.extras
def _refresh_task_tensors(self) -> None:
"""Refresh tensors."""
self.fingerpad_midpoint_pos = fc.translate_along_local_z(
pos=self.finger_midpoint_pos,
quat=self.hand_quat,
offset=self.asset_info_franka_table.franka_finger_length
- self.asset_info_franka_table.franka_fingerpad_length * 0.5,
device=self.device,
)
self.finger_nut_keypoint_dist = self._get_keypoint_dist(body="finger_nut")
self.nut_keypoint_dist = self._get_keypoint_dist(body="nut")
self.nut_dist_to_target = torch.norm(
self.target_pos - self.nut_com_pos, p=2, dim=-1
) # distance between nut COM and target
self.nut_dist_to_fingerpads = torch.norm(
self.fingerpad_midpoint_pos - self.nut_com_pos, p=2, dim=-1
) # distance between nut COM and midpoint between centers of fingerpads
self.was_success = torch.zeros_like(self.progress_buf, dtype=torch.bool)
def get_observations(self) -> dict:
"""Compute observations."""
# Shallow copies of tensors
obs_tensors = [
self.fingertip_midpoint_pos,
self.fingertip_midpoint_quat,
self.fingertip_midpoint_linvel,
self.fingertip_midpoint_angvel,
self.nut_com_pos,
self.nut_com_quat,
self.nut_com_linvel,
self.nut_com_angvel,
]
if self.cfg_task.rl.add_obs_finger_force:
obs_tensors += [self.left_finger_force, self.right_finger_force]
else:
obs_tensors += [
torch.zeros_like(self.left_finger_force),
torch.zeros_like(self.right_finger_force),
]
self.obs_buf = torch.cat(
obs_tensors, dim=-1
) # shape = (num_envs, num_observations)
observations = {self.frankas.name: {"obs_buf": self.obs_buf}}
return observations
def calculate_metrics(self) -> None:
"""Update reset and reward buffers."""
# Get successful and failed envs at current timestep
curr_successes = self._get_curr_successes()
curr_failures = self._get_curr_failures(curr_successes)
self._update_reset_buf(curr_successes, curr_failures)
self._update_rew_buf(curr_successes)
if torch.any(self.is_expired):
self.extras["successes"] = torch.mean(curr_successes.float())
def _update_reset_buf(self, curr_successes, curr_failures) -> None:
"""Assign environments for reset if successful or failed."""
self.reset_buf[:] = self.is_expired
def _update_rew_buf(self, curr_successes) -> None:
"""Compute reward at current timestep."""
keypoint_reward = -(self.nut_keypoint_dist + self.finger_nut_keypoint_dist)
action_penalty = torch.norm(self.actions, p=2, dim=-1)
self.rew_buf[:] = (
keypoint_reward * self.cfg_task.rl.keypoint_reward_scale
- action_penalty * self.cfg_task.rl.action_penalty_scale
+ curr_successes * self.cfg_task.rl.success_bonus
)
def _get_keypoint_dist(self, body) -> torch.Tensor:
"""Get keypoint distance."""
axis_length = (
self.asset_info_franka_table.franka_hand_length
+ self.asset_info_franka_table.franka_finger_length
)
if body == "finger" or body == "nut":
# Keypoint distance between finger/nut and target
if body == "finger":
self.keypoint1 = self.fingertip_midpoint_pos
self.keypoint2 = fc.translate_along_local_z(
pos=self.keypoint1,
quat=self.fingertip_midpoint_quat,
offset=-axis_length,
device=self.device,
)
elif body == "nut":
self.keypoint1 = self.nut_com_pos
self.keypoint2 = fc.translate_along_local_z(
pos=self.nut_com_pos,
quat=self.nut_com_quat,
offset=axis_length,
device=self.device,
)
self.keypoint1_targ = self.target_pos
self.keypoint2_targ = self.keypoint1_targ + torch.tensor(
[0.0, 0.0, axis_length], device=self.device
)
elif body == "finger_nut":
# Keypoint distance between finger and nut
self.keypoint1 = self.fingerpad_midpoint_pos
self.keypoint2 = fc.translate_along_local_z(
pos=self.keypoint1,
quat=self.fingertip_midpoint_quat,
offset=-axis_length,
device=self.device,
)
self.keypoint1_targ = self.nut_com_pos
self.keypoint2_targ = fc.translate_along_local_z(
pos=self.nut_com_pos,
quat=self.nut_com_quat,
offset=axis_length,
device=self.device,
)
self.keypoint3 = self.keypoint1 + (self.keypoint2 - self.keypoint1) * 1.0 / 3.0
self.keypoint4 = self.keypoint1 + (self.keypoint2 - self.keypoint1) * 2.0 / 3.0
self.keypoint3_targ = (
self.keypoint1_targ
+ (self.keypoint2_targ - self.keypoint1_targ) * 1.0 / 3.0
)
self.keypoint4_targ = (
self.keypoint1_targ
+ (self.keypoint2_targ - self.keypoint1_targ) * 2.0 / 3.0
)
keypoint_dist = (
torch.norm(self.keypoint1_targ - self.keypoint1, p=2, dim=-1)
+ torch.norm(self.keypoint2_targ - self.keypoint2, p=2, dim=-1)
+ torch.norm(self.keypoint3_targ - self.keypoint3, p=2, dim=-1)
+ torch.norm(self.keypoint4_targ - self.keypoint4, p=2, dim=-1)
)
return keypoint_dist
def _get_curr_successes(self) -> torch.Tensor:
"""Get success mask at current timestep."""
curr_successes = torch.zeros(
(self.num_envs,), dtype=torch.bool, device=self.device
)
# If nut is close enough to target pos
is_close = torch.where(
self.nut_dist_to_target < self.thread_pitches.squeeze(-1) * 5,
torch.ones_like(curr_successes),
torch.zeros_like(curr_successes),
)
curr_successes = torch.logical_or(curr_successes, is_close)
return curr_successes
def _get_curr_failures(self, curr_successes) -> torch.Tensor:
"""Get failure mask at current timestep."""
curr_failures = torch.zeros(
(self.num_envs,), dtype=torch.bool, device=self.device
)
# If max episode length has been reached
self.is_expired = torch.where(
self.progress_buf[:] >= self.cfg_task.rl.max_episode_length,
torch.ones_like(curr_failures),
curr_failures,
)
# If nut is too far from target pos
self.is_far = torch.where(
self.nut_dist_to_target > self.cfg_task.rl.far_error_thresh,
torch.ones_like(curr_failures),
curr_failures,
)
# If nut has slipped (distance-based definition)
self.is_slipped = torch.where(
self.nut_dist_to_fingerpads
> self.asset_info_franka_table.franka_fingerpad_length * 0.5
+ self.nut_heights.squeeze(-1) * 0.5,
torch.ones_like(curr_failures),
curr_failures,
)
self.is_slipped = torch.logical_and(
self.is_slipped, torch.logical_not(curr_successes)
) # ignore slip if successful
# If nut has fallen (i.e., if nut XY pos has drifted from center of bolt and nut Z pos has drifted below top of bolt)
self.is_fallen = torch.logical_and(
torch.norm(self.nut_com_pos[:, 0:2], p=2, dim=-1)
> self.bolt_widths.squeeze(-1) * 0.5,
self.nut_com_pos[:, 2]
< self.cfg_base.env.table_height
+ self.bolt_head_heights.squeeze(-1)
+ self.bolt_shank_lengths.squeeze(-1)
+ self.nut_heights.squeeze(-1) * 0.5,
)
curr_failures = torch.logical_or(curr_failures, self.is_expired)
curr_failures = torch.logical_or(curr_failures, self.is_far)
curr_failures = torch.logical_or(curr_failures, self.is_slipped)
curr_failures = torch.logical_or(curr_failures, self.is_fallen)
return curr_failures
| 20,027 | Python | 37.367816 | 131 | 0.589105 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_task_nut_bolt_pick.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: Class for nut-bolt pick task.
Inherits nut-bolt environment class and abstract task class (not enforced). Can be executed with
PYTHON_PATH omniisaacgymenvs/scripts/rlgames_train.py task=FactoryTaskNutBoltPick
"""
import asyncio
import hydra
import omegaconf
import torch
import omni.kit
from omni.isaac.core.simulation_context import SimulationContext
from omni.isaac.core.utils.torch.transformations import tf_combine
from typing import Tuple
import omni.isaac.core.utils.torch as torch_utils
import omniisaacgymenvs.tasks.factory.factory_control as fc
from omniisaacgymenvs.tasks.factory.factory_env_nut_bolt import FactoryEnvNutBolt
from omniisaacgymenvs.tasks.factory.factory_schema_class_task import FactoryABCTask
from omniisaacgymenvs.tasks.factory.factory_schema_config_task import (
FactorySchemaConfigTask,
)
class FactoryTaskNutBoltPick(FactoryEnvNutBolt, FactoryABCTask):
def __init__(self, name, sim_config, env, offset=None) -> None:
"""Initialize environment superclass. Initialize instance variables."""
super().__init__(name, sim_config, env)
self._get_task_yaml_params()
def _get_task_yaml_params(self) -> None:
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name="factory_schema_config_task", node=FactorySchemaConfigTask)
self.cfg_task = omegaconf.OmegaConf.create(self._task_cfg)
self.max_episode_length = (
self.cfg_task.rl.max_episode_length
) # required instance var for VecTask
asset_info_path = "../tasks/factory/yaml/factory_asset_info_nut_bolt.yaml" # relative to Gym's Hydra search path (cfg dir)
self.asset_info_nut_bolt = hydra.compose(config_name=asset_info_path)
self.asset_info_nut_bolt = self.asset_info_nut_bolt[""][""][""]["tasks"][
"factory"
][
"yaml"
] # strip superfluous nesting
ppo_path = "train/FactoryTaskNutBoltPickPPO.yaml" # relative to Gym's Hydra search path (cfg dir)
self.cfg_ppo = hydra.compose(config_name=ppo_path)
self.cfg_ppo = self.cfg_ppo["train"] # strip superfluous nesting
def post_reset(self) -> None:
"""Reset the world. Called only once, before simulation begins."""
if self.cfg_task.sim.disable_gravity:
self.disable_gravity()
self.acquire_base_tensors()
self._acquire_task_tensors()
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
# Reset all envs
indices = torch.arange(self._num_envs, dtype=torch.int64, device=self._device)
asyncio.ensure_future(
self.reset_idx_async(indices, randomize_gripper_pose=False)
)
def _acquire_task_tensors(self) -> None:
"""Acquire tensors."""
# Grasp pose tensors
nut_grasp_heights = self.bolt_head_heights + self.nut_heights * 0.5 # nut COM
self.nut_grasp_pos_local = nut_grasp_heights * torch.tensor(
[0.0, 0.0, 1.0], device=self.device
).repeat((self.num_envs, 1))
self.nut_grasp_quat_local = (
torch.tensor([0.0, 0.0, 1.0, 0.0], device=self.device)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
# Keypoint tensors
self.keypoint_offsets = (
self._get_keypoint_offsets(self.cfg_task.rl.num_keypoints)
* self.cfg_task.rl.keypoint_scale
)
self.keypoints_gripper = torch.zeros(
(self.num_envs, self.cfg_task.rl.num_keypoints, 3),
dtype=torch.float32,
device=self.device,
)
self.keypoints_nut = torch.zeros_like(
self.keypoints_gripper, device=self.device
)
self.identity_quat = (
torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
self.actions = torch.zeros(
(self.num_envs, self.num_actions), device=self.device
)
def pre_physics_step(self, actions) -> None:
"""Reset environments. Apply actions from policy. Simulation step called after this method."""
if not self.world.is_playing():
return
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
self.reset_idx(env_ids, randomize_gripper_pose=True)
self.actions = actions.clone().to(
self.device
) # shape = (num_envs, num_actions); values = [-1, 1]
self._apply_actions_as_ctrl_targets(
actions=self.actions,
ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
do_scale=True,
)
async def pre_physics_step_async(self, actions) -> None:
"""Reset environments. Apply actions from policy. Simulation step called after this method."""
if not self.world.is_playing():
return
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(env_ids) > 0:
await self.reset_idx_async(env_ids, randomize_gripper_pose=True)
self.actions = actions.clone().to(
self.device
) # shape = (num_envs, num_actions); values = [-1, 1]
self._apply_actions_as_ctrl_targets(
actions=self.actions,
ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
do_scale=True,
)
def reset_idx(self, env_ids, randomize_gripper_pose) -> None:
"""Reset specified environments."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
if randomize_gripper_pose:
self._randomize_gripper_pose(
env_ids, sim_steps=self.cfg_task.env.num_gripper_move_sim_steps
)
self._reset_buffers(env_ids)
async def reset_idx_async(self, env_ids, randomize_gripper_pose) -> None:
"""Reset specified environments."""
self._reset_franka(env_ids)
self._reset_object(env_ids)
if randomize_gripper_pose:
await self._randomize_gripper_pose_async(
env_ids, sim_steps=self.cfg_task.env.num_gripper_move_sim_steps
)
self._reset_buffers(env_ids)
def _reset_franka(self, env_ids) -> None:
"""Reset DOF states and DOF targets of Franka."""
self.dof_pos[env_ids] = torch.cat(
(
torch.tensor(
self.cfg_task.randomize.franka_arm_initial_dof_pos,
device=self.device,
),
torch.tensor(
[self.asset_info_franka_table.franka_gripper_width_max],
device=self.device,
),
torch.tensor(
[self.asset_info_franka_table.franka_gripper_width_max],
device=self.device,
),
),
dim=-1,
) # shape = (num_envs, num_dofs)
self.dof_vel[env_ids] = 0.0 # shape = (num_envs, num_dofs)
self.ctrl_target_dof_pos[env_ids] = self.dof_pos[env_ids]
indices = env_ids.to(dtype=torch.int32)
self.frankas.set_joint_positions(self.dof_pos[env_ids], indices=indices)
self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)
def _reset_object(self, env_ids) -> None:
"""Reset root states of nut and bolt."""
# Randomize root state of nut
nut_noise_xy = 2 * (
torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
nut_noise_xy = nut_noise_xy @ torch.diag(
torch.tensor(self.cfg_task.randomize.nut_pos_xy_noise, device=self.device)
)
self.nut_pos[env_ids, 0] = (
self.cfg_task.randomize.nut_pos_xy_initial[0] + nut_noise_xy[env_ids, 0]
)
self.nut_pos[env_ids, 1] = (
self.cfg_task.randomize.nut_pos_xy_initial[1] + nut_noise_xy[env_ids, 1]
)
self.nut_pos[
env_ids, 2
] = self.cfg_base.env.table_height - self.bolt_head_heights.squeeze(-1)
self.nut_quat[env_ids, :] = torch.tensor(
[1.0, 0.0, 0.0, 0.0], dtype=torch.float32, device=self.device
).repeat(len(env_ids), 1)
self.nut_linvel[env_ids, :] = 0.0
self.nut_angvel[env_ids, :] = 0.0
indices = env_ids.to(dtype=torch.int32)
self.nuts.set_world_poses(
self.nut_pos[env_ids] + self.env_pos[env_ids],
self.nut_quat[env_ids],
indices,
)
self.nuts.set_velocities(
torch.cat((self.nut_linvel[env_ids], self.nut_angvel[env_ids]), dim=1),
indices,
)
# Randomize root state of bolt
bolt_noise_xy = 2 * (
torch.rand((self.num_envs, 2), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
bolt_noise_xy = bolt_noise_xy @ torch.diag(
torch.tensor(self.cfg_task.randomize.bolt_pos_xy_noise, device=self.device)
)
self.bolt_pos[env_ids, 0] = (
self.cfg_task.randomize.bolt_pos_xy_initial[0] + bolt_noise_xy[env_ids, 0]
)
self.bolt_pos[env_ids, 1] = (
self.cfg_task.randomize.bolt_pos_xy_initial[1] + bolt_noise_xy[env_ids, 1]
)
self.bolt_pos[env_ids, 2] = self.cfg_base.env.table_height
self.bolt_quat[env_ids, :] = torch.tensor(
[1.0, 0.0, 0.0, 0.0], dtype=torch.float32, device=self.device
).repeat(len(env_ids), 1)
indices = env_ids.to(dtype=torch.int32)
self.bolts.set_world_poses(
self.bolt_pos[env_ids] + self.env_pos[env_ids],
self.bolt_quat[env_ids],
indices,
)
def _reset_buffers(self, env_ids) -> None:
"""Reset buffers."""
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
def _apply_actions_as_ctrl_targets(
self, actions, ctrl_target_gripper_dof_pos, do_scale
) -> None:
"""Apply actions from policy as position/rotation/force/torque targets."""
# Interpret actions as target pos displacements and set pos target
pos_actions = actions[:, 0:3]
if do_scale:
pos_actions = pos_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.pos_action_scale, device=self.device)
)
self.ctrl_target_fingertip_midpoint_pos = (
self.fingertip_midpoint_pos + pos_actions
)
# Interpret actions as target rot (axis-angle) displacements
rot_actions = actions[:, 3:6]
if do_scale:
rot_actions = rot_actions @ torch.diag(
torch.tensor(self.cfg_task.rl.rot_action_scale, device=self.device)
)
# Convert to quat and set rot target
angle = torch.norm(rot_actions, p=2, dim=-1)
axis = rot_actions / angle.unsqueeze(-1)
rot_actions_quat = torch_utils.quat_from_angle_axis(angle, axis)
if self.cfg_task.rl.clamp_rot:
rot_actions_quat = torch.where(
angle.unsqueeze(-1).repeat(1, 4) > self.cfg_task.rl.clamp_rot_thresh,
rot_actions_quat,
torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device).repeat(
self.num_envs, 1
),
)
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_mul(
rot_actions_quat, self.fingertip_midpoint_quat
)
if self.cfg_ctrl["do_force_ctrl"]:
# Interpret actions as target forces and target torques
force_actions = actions[:, 6:9]
if do_scale:
force_actions = force_actions @ torch.diag(
torch.tensor(
self.cfg_task.rl.force_action_scale, device=self.device
)
)
torque_actions = actions[:, 9:12]
if do_scale:
torque_actions = torque_actions @ torch.diag(
torch.tensor(
self.cfg_task.rl.torque_action_scale, device=self.device
)
)
self.ctrl_target_fingertip_contact_wrench = torch.cat(
(force_actions, torque_actions), dim=-1
)
self.ctrl_target_gripper_dof_pos = ctrl_target_gripper_dof_pos
self.generate_ctrl_signals()
def post_physics_step(
self,
) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
"""Step buffers. Refresh tensors. Compute observations and reward. Reset environments."""
self.progress_buf[:] += 1
if self.world.is_playing():
# In this policy, episode length is constant
is_last_step = self.progress_buf[0] == self.max_episode_length - 1
if is_last_step:
# At this point, robot has executed RL policy. Now close gripper and lift (open-loop)
if self.cfg_task.env.close_and_lift:
self._close_gripper(
sim_steps=self.cfg_task.env.num_gripper_close_sim_steps
)
self._lift_gripper(
franka_gripper_width=0.0,
lift_distance=0.3,
sim_steps=self.cfg_task.env.num_gripper_lift_sim_steps,
)
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.get_observations()
self.get_states()
self.calculate_metrics()
self.get_extras()
return self.obs_buf, self.rew_buf, self.reset_buf, self.extras
async def post_physics_step_async(self):
"""Step buffers. Refresh tensors. Compute observations and reward. Reset environments."""
self.progress_buf[:] += 1
if self.world.is_playing():
# In this policy, episode length is constant
is_last_step = self.progress_buf[0] == self.max_episode_length - 1
if self.cfg_task.env.close_and_lift:
# At this point, robot has executed RL policy. Now close gripper and lift (open-loop)
if is_last_step:
await self._close_gripper_async(
sim_steps=self.cfg_task.env.num_gripper_close_sim_steps
)
await self._lift_gripper_async(
sim_steps=self.cfg_task.env.num_gripper_lift_sim_steps
)
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
self.get_observations()
self.get_states()
self.calculate_metrics()
self.get_extras()
return self.obs_buf, self.rew_buf, self.reset_buf, self.extras
def _refresh_task_tensors(self):
"""Refresh tensors."""
# Compute pose of nut grasping frame
self.nut_grasp_quat, self.nut_grasp_pos = tf_combine(
self.nut_quat,
self.nut_pos,
self.nut_grasp_quat_local,
self.nut_grasp_pos_local,
)
# Compute pos of keypoints on gripper and nut in world frame
for idx, keypoint_offset in enumerate(self.keypoint_offsets):
self.keypoints_gripper[:, idx] = tf_combine(
self.fingertip_midpoint_quat,
self.fingertip_midpoint_pos,
self.identity_quat,
keypoint_offset.repeat(self.num_envs, 1),
)[1]
self.keypoints_nut[:, idx] = tf_combine(
self.nut_grasp_quat,
self.nut_grasp_pos,
self.identity_quat,
keypoint_offset.repeat(self.num_envs, 1),
)[1]
def get_observations(self) -> dict:
"""Compute observations."""
# Shallow copies of tensors
obs_tensors = [
self.fingertip_midpoint_pos,
self.fingertip_midpoint_quat,
self.fingertip_midpoint_linvel,
self.fingertip_midpoint_angvel,
self.nut_grasp_pos,
self.nut_grasp_quat,
]
self.obs_buf = torch.cat(
obs_tensors, dim=-1
) # shape = (num_envs, num_observations)
observations = {self.frankas.name: {"obs_buf": self.obs_buf}}
return observations
def calculate_metrics(self) -> None:
"""Update reward and reset buffers."""
self._update_reset_buf()
self._update_rew_buf()
def _update_reset_buf(self) -> None:
"""Assign environments for reset if successful or failed."""
# If max episode length has been reached
self.reset_buf[:] = torch.where(
self.progress_buf[:] >= self.max_episode_length - 1,
torch.ones_like(self.reset_buf),
self.reset_buf,
)
def _update_rew_buf(self) -> None:
"""Compute reward at current timestep."""
keypoint_reward = -self._get_keypoint_dist()
action_penalty = (
torch.norm(self.actions, p=2, dim=-1)
* self.cfg_task.rl.action_penalty_scale
)
self.rew_buf[:] = (
keypoint_reward * self.cfg_task.rl.keypoint_reward_scale
- action_penalty * self.cfg_task.rl.action_penalty_scale
)
# In this policy, episode length is constant across all envs
is_last_step = self.progress_buf[0] == self.max_episode_length - 1
if is_last_step:
# Check if nut is picked up and above table
lift_success = self._check_lift_success(height_multiple=3.0)
self.rew_buf[:] += lift_success * self.cfg_task.rl.success_bonus
self.extras["successes"] = torch.mean(lift_success.float())
def _get_keypoint_offsets(self, num_keypoints) -> torch.Tensor:
"""Get uniformly-spaced keypoints along a line of unit length, centered at 0."""
keypoint_offsets = torch.zeros((num_keypoints, 3), device=self.device)
keypoint_offsets[:, -1] = (
torch.linspace(0.0, 1.0, num_keypoints, device=self.device) - 0.5
)
return keypoint_offsets
def _get_keypoint_dist(self) -> torch.Tensor:
"""Get keypoint distance."""
keypoint_dist = torch.sum(
torch.norm(self.keypoints_nut - self.keypoints_gripper, p=2, dim=-1), dim=-1
)
return keypoint_dist
def _close_gripper(self, sim_steps=20) -> None:
"""Fully close gripper using controller. Called outside RL loop (i.e., after last step of episode)."""
self._move_gripper_to_dof_pos(gripper_dof_pos=0.0, sim_steps=sim_steps)
def _move_gripper_to_dof_pos(self, gripper_dof_pos, sim_steps=20) -> None:
"""Move gripper fingers to specified DOF position using controller."""
delta_hand_pose = torch.zeros(
(self.num_envs, 6), device=self.device
) # No hand motion
self._apply_actions_as_ctrl_targets(
delta_hand_pose, gripper_dof_pos, do_scale=False
)
# Step sim
for _ in range(sim_steps):
SimulationContext.step(self.world, render=True)
def _lift_gripper(
self, franka_gripper_width=0.0, lift_distance=0.3, sim_steps=20
) -> None:
"""Lift gripper by specified distance. Called outside RL loop (i.e., after last step of episode)."""
delta_hand_pose = torch.zeros([self.num_envs, 6], device=self.device)
delta_hand_pose[:, 2] = lift_distance
# Step sim
for _ in range(sim_steps):
self._apply_actions_as_ctrl_targets(
delta_hand_pose, franka_gripper_width, do_scale=False
)
SimulationContext.step(self.world, render=True)
async def _close_gripper_async(self, sim_steps=20) -> None:
"""Fully close gripper using controller. Called outside RL loop (i.e., after last step of episode)."""
await self._move_gripper_to_dof_pos_async(
gripper_dof_pos=0.0, sim_steps=sim_steps
)
async def _move_gripper_to_dof_pos_async(
self, gripper_dof_pos, sim_steps=20
) -> None:
"""Move gripper fingers to specified DOF position using controller."""
delta_hand_pose = torch.zeros(
(self.num_envs, self.cfg_task.env.numActions), device=self.device
) # No hand motion
self._apply_actions_as_ctrl_targets(
delta_hand_pose, gripper_dof_pos, do_scale=False
)
# Step sim
for _ in range(sim_steps):
self.world.physics_sim_view.flush()
await omni.kit.app.get_app().next_update_async()
async def _lift_gripper_async(
self, franka_gripper_width=0.0, lift_distance=0.3, sim_steps=20
) -> None:
"""Lift gripper by specified distance. Called outside RL loop (i.e., after last step of episode)."""
delta_hand_pose = torch.zeros([self.num_envs, 6], device=self.device)
delta_hand_pose[:, 2] = lift_distance
# Step sim
for _ in range(sim_steps):
self._apply_actions_as_ctrl_targets(
delta_hand_pose, franka_gripper_width, do_scale=False
)
self.world.physics_sim_view.flush()
await omni.kit.app.get_app().next_update_async()
def _check_lift_success(self, height_multiple) -> torch.Tensor:
"""Check if nut is above table by more than specified multiple times height of nut."""
lift_success = torch.where(
self.nut_pos[:, 2]
> self.cfg_base.env.table_height
+ self.nut_heights.squeeze(-1) * height_multiple,
torch.ones((self.num_envs,), device=self.device),
torch.zeros((self.num_envs,), device=self.device),
)
return lift_success
def _randomize_gripper_pose(self, env_ids, sim_steps) -> None:
"""Move gripper to random pose."""
# step once to update physx with the newly set joint positions from reset_franka()
SimulationContext.step(self.world, render=True)
# Set target pos above table
self.ctrl_target_fingertip_midpoint_pos = torch.tensor(
[0.0, 0.0, self.cfg_base.env.table_height], device=self.device
) + torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_pos_initial, device=self.device
)
self.ctrl_target_fingertip_midpoint_pos = (
self.ctrl_target_fingertip_midpoint_pos.unsqueeze(0).repeat(
self.num_envs, 1
)
)
fingertip_midpoint_pos_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
fingertip_midpoint_pos_noise = fingertip_midpoint_pos_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_pos_noise, device=self.device
)
)
self.ctrl_target_fingertip_midpoint_pos += fingertip_midpoint_pos_noise
# Set target rot
ctrl_target_fingertip_midpoint_euler = (
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_rot_initial,
device=self.device,
)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
fingertip_midpoint_rot_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
fingertip_midpoint_rot_noise = fingertip_midpoint_rot_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_rot_noise, device=self.device
)
)
ctrl_target_fingertip_midpoint_euler += fingertip_midpoint_rot_noise
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
ctrl_target_fingertip_midpoint_euler[:, 0],
ctrl_target_fingertip_midpoint_euler[:, 1],
ctrl_target_fingertip_midpoint_euler[:, 2],
)
# Step sim and render
for _ in range(sim_steps):
if not self.world.is_playing():
return
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
pos_error, axis_angle_error = fc.get_pose_error(
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
jacobian_type=self.cfg_ctrl["jacobian_type"],
rot_error_type="axis_angle",
)
delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1)
actions = torch.zeros(
(self.num_envs, self.cfg_task.env.numActions), device=self.device
)
actions[:, :6] = delta_hand_pose
self._apply_actions_as_ctrl_targets(
actions=actions,
ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
do_scale=False,
)
SimulationContext.step(self.world, render=True)
self.dof_vel[env_ids, :] = torch.zeros_like(self.dof_vel[env_ids])
indices = env_ids.to(dtype=torch.int32)
self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)
# step once to update physx with the newly set joint velocities
SimulationContext.step(self.world, render=True)
async def _randomize_gripper_pose_async(self, env_ids, sim_steps) -> None:
"""Move gripper to random pose."""
# step once to update physx with the newly set joint positions from reset_franka()
await omni.kit.app.get_app().next_update_async()
# Set target pos above table
self.ctrl_target_fingertip_midpoint_pos = torch.tensor(
[0.0, 0.0, self.cfg_base.env.table_height], device=self.device
) + torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_pos_initial, device=self.device
)
self.ctrl_target_fingertip_midpoint_pos = (
self.ctrl_target_fingertip_midpoint_pos.unsqueeze(0).repeat(
self.num_envs, 1
)
)
fingertip_midpoint_pos_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
fingertip_midpoint_pos_noise = fingertip_midpoint_pos_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_pos_noise, device=self.device
)
)
self.ctrl_target_fingertip_midpoint_pos += fingertip_midpoint_pos_noise
# Set target rot
ctrl_target_fingertip_midpoint_euler = (
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_rot_initial,
device=self.device,
)
.unsqueeze(0)
.repeat(self.num_envs, 1)
)
fingertip_midpoint_rot_noise = 2 * (
torch.rand((self.num_envs, 3), dtype=torch.float32, device=self.device)
- 0.5
) # [-1, 1]
fingertip_midpoint_rot_noise = fingertip_midpoint_rot_noise @ torch.diag(
torch.tensor(
self.cfg_task.randomize.fingertip_midpoint_rot_noise, device=self.device
)
)
ctrl_target_fingertip_midpoint_euler += fingertip_midpoint_rot_noise
self.ctrl_target_fingertip_midpoint_quat = torch_utils.quat_from_euler_xyz(
ctrl_target_fingertip_midpoint_euler[:, 0],
ctrl_target_fingertip_midpoint_euler[:, 1],
ctrl_target_fingertip_midpoint_euler[:, 2],
)
# Step sim and render
for _ in range(sim_steps):
self.refresh_base_tensors()
self.refresh_env_tensors()
self._refresh_task_tensors()
pos_error, axis_angle_error = fc.get_pose_error(
fingertip_midpoint_pos=self.fingertip_midpoint_pos,
fingertip_midpoint_quat=self.fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=self.ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=self.ctrl_target_fingertip_midpoint_quat,
jacobian_type=self.cfg_ctrl["jacobian_type"],
rot_error_type="axis_angle",
)
delta_hand_pose = torch.cat((pos_error, axis_angle_error), dim=-1)
actions = torch.zeros(
(self.num_envs, self.cfg_task.env.numActions), device=self.device
)
actions[:, :6] = delta_hand_pose
self._apply_actions_as_ctrl_targets(
actions=actions,
ctrl_target_gripper_dof_pos=self.asset_info_franka_table.franka_gripper_width_max,
do_scale=False,
)
self.world.physics_sim_view.flush()
await omni.kit.app.get_app().next_update_async()
self.dof_vel[env_ids, :] = torch.zeros_like(self.dof_vel[env_ids])
indices = env_ids.to(dtype=torch.int32)
self.frankas.set_joint_velocities(self.dof_vel[env_ids], indices=indices)
# step once to update physx with the newly set joint velocities
self.world.physics_sim_view.flush()
await omni.kit.app.get_app().next_update_async()
| 31,484 | Python | 37.822441 | 131 | 0.589506 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_schema_class_base.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: abstract base class for base class.
Inherits ABC class. Inherited by base class. Defines template for base class.
"""
from abc import ABC, abstractmethod
class FactoryABCBase(ABC):
@abstractmethod
def __init__(self):
"""Initialize instance variables. Initialize VecTask superclass."""
pass
@abstractmethod
def _get_base_yaml_params(self):
"""Initialize instance variables from YAML files."""
pass
@abstractmethod
def import_franka_assets(self):
"""Set Franka and table asset options. Import assets."""
pass
@abstractmethod
def refresh_base_tensors(self):
"""Refresh tensors."""
# NOTE: Tensor refresh functions should be called once per step, before setters.
pass
@abstractmethod
def parse_controller_spec(self):
"""Parse controller specification into lower-level controller configuration."""
pass
@abstractmethod
def generate_ctrl_signals(self):
"""Get Jacobian. Set Franka DOF position targets or DOF torques."""
pass
@abstractmethod
def enable_gravity(self):
"""Enable gravity."""
pass
@abstractmethod
def disable_gravity(self):
"""Disable gravity."""
pass
| 2,843 | Python | 35 | 88 | 0.721069 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_schema_config_base.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: schema for base class configuration.
Used by Hydra. Defines template for base class YAML file.
"""
from dataclasses import dataclass
@dataclass
class Mode:
export_scene: bool # export scene to USD
export_states: bool # export states to NPY
@dataclass
class Sim:
dt: float # timestep size (default = 1.0 / 60.0)
num_substeps: int # number of substeps (default = 2)
num_pos_iters: int # number of position iterations for PhysX TGS solver (default = 4)
num_vel_iters: int # number of velocity iterations for PhysX TGS solver (default = 1)
gravity_mag: float # magnitude of gravitational acceleration
add_damping: bool # add damping to stabilize gripper-object interactions
@dataclass
class Env:
env_spacing: float # lateral offset between envs
franka_depth: float # depth offset of Franka base relative to env origin
table_height: float # height of table
franka_friction: float # coefficient of friction associated with Franka
table_friction: float # coefficient of friction associated with table
@dataclass
class FactorySchemaConfigBase:
mode: Mode
sim: Sim
env: Env
| 2,724 | Python | 39.073529 | 90 | 0.757342 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_env_nut_bolt.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: class for nut-bolt env.
Inherits base class and abstract environment class. Inherited by nut-bolt task classes. Not directly executed.
Configuration defined in FactoryEnvNutBolt.yaml. Asset info defined in factory_asset_info_nut_bolt.yaml.
"""
import hydra
import numpy as np
import torch
from omni.isaac.core.prims import RigidPrimView, XFormPrim
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.stage import add_reference_to_stage
from omniisaacgymenvs.tasks.base.rl_task import RLTask
from omni.physx.scripts import physicsUtils, utils
from omniisaacgymenvs.robots.articulations.views.factory_franka_view import (
FactoryFrankaView,
)
import omniisaacgymenvs.tasks.factory.factory_control as fc
from omniisaacgymenvs.tasks.factory.factory_base import FactoryBase
from omniisaacgymenvs.tasks.factory.factory_schema_class_env import FactoryABCEnv
from omniisaacgymenvs.tasks.factory.factory_schema_config_env import (
FactorySchemaConfigEnv,
)
class FactoryEnvNutBolt(FactoryBase, FactoryABCEnv):
def __init__(self, name, sim_config, env) -> None:
"""Initialize base superclass. Initialize instance variables."""
super().__init__(name, sim_config, env)
self._get_env_yaml_params()
def _get_env_yaml_params(self):
"""Initialize instance variables from YAML files."""
cs = hydra.core.config_store.ConfigStore.instance()
cs.store(name="factory_schema_config_env", node=FactorySchemaConfigEnv)
config_path = (
"task/FactoryEnvNutBolt.yaml" # relative to Hydra search path (cfg dir)
)
self.cfg_env = hydra.compose(config_name=config_path)
self.cfg_env = self.cfg_env["task"] # strip superfluous nesting
asset_info_path = "../tasks/factory/yaml/factory_asset_info_nut_bolt.yaml"
self.asset_info_nut_bolt = hydra.compose(config_name=asset_info_path)
self.asset_info_nut_bolt = self.asset_info_nut_bolt[""][""][""]["tasks"][
"factory"
][
"yaml"
] # strip superfluous nesting
def update_config(self, sim_config):
self._sim_config = sim_config
self._cfg = sim_config.config
self._task_cfg = sim_config.task_config
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._num_observations = self._task_cfg["env"]["numObservations"]
self._num_actions = self._task_cfg["env"]["numActions"]
self._env_spacing = self.cfg_base["env"]["env_spacing"]
self._get_env_yaml_params()
def set_up_scene(self, scene) -> None:
"""Import assets. Add to scene."""
# Increase buffer size to prevent overflow for Place and Screw tasks
physxSceneAPI = self.world.get_physics_context()._physx_scene_api
physxSceneAPI.CreateGpuCollisionStackSizeAttr().Set(256 * 1024 * 1024)
self.import_franka_assets(add_to_stage=True)
self.create_nut_bolt_material()
RLTask.set_up_scene(self, scene, replicate_physics=False)
self._import_env_assets(add_to_stage=True)
self.frankas = FactoryFrankaView(
prim_paths_expr="/World/envs/.*/franka", name="frankas_view"
)
self.nuts = RigidPrimView(
prim_paths_expr="/World/envs/.*/nut/factory_nut.*",
name="nuts_view",
track_contact_forces=True,
)
self.bolts = RigidPrimView(
prim_paths_expr="/World/envs/.*/bolt/factory_bolt.*",
name="bolts_view",
track_contact_forces=True,
)
scene.add(self.nuts)
scene.add(self.bolts)
scene.add(self.frankas)
scene.add(self.frankas._hands)
scene.add(self.frankas._lfingers)
scene.add(self.frankas._rfingers)
scene.add(self.frankas._fingertip_centered)
return
def initialize_views(self, scene) -> None:
"""Initialize views for extension workflow."""
super().initialize_views(scene)
self.import_franka_assets(add_to_stage=False)
self._import_env_assets(add_to_stage=False)
if scene.object_exists("frankas_view"):
scene.remove_object("frankas_view", registry_only=True)
if scene.object_exists("nuts_view"):
scene.remove_object("nuts_view", registry_only=True)
if scene.object_exists("bolts_view"):
scene.remove_object("bolts_view", registry_only=True)
if scene.object_exists("hands_view"):
scene.remove_object("hands_view", registry_only=True)
if scene.object_exists("lfingers_view"):
scene.remove_object("lfingers_view", registry_only=True)
if scene.object_exists("rfingers_view"):
scene.remove_object("rfingers_view", registry_only=True)
if scene.object_exists("fingertips_view"):
scene.remove_object("fingertips_view", registry_only=True)
self.frankas = FactoryFrankaView(
prim_paths_expr="/World/envs/.*/franka", name="frankas_view"
)
self.nuts = RigidPrimView(
prim_paths_expr="/World/envs/.*/nut/factory_nut.*", name="nuts_view"
)
self.bolts = RigidPrimView(
prim_paths_expr="/World/envs/.*/bolt/factory_bolt.*", name="bolts_view"
)
scene.add(self.nuts)
scene.add(self.bolts)
scene.add(self.frankas)
scene.add(self.frankas._hands)
scene.add(self.frankas._lfingers)
scene.add(self.frankas._rfingers)
scene.add(self.frankas._fingertip_centered)
def create_nut_bolt_material(self):
"""Define nut and bolt material."""
self.nutboltPhysicsMaterialPath = "/World/Physics_Materials/NutBoltMaterial"
utils.addRigidBodyMaterial(
self._stage,
self.nutboltPhysicsMaterialPath,
density=self.cfg_env.env.nut_bolt_density,
staticFriction=self.cfg_env.env.nut_bolt_friction,
dynamicFriction=self.cfg_env.env.nut_bolt_friction,
restitution=0.0,
)
def _import_env_assets(self, add_to_stage=True):
"""Set nut and bolt asset options. Import assets."""
self.nut_heights = []
self.nut_widths_max = []
self.bolt_widths = []
self.bolt_head_heights = []
self.bolt_shank_lengths = []
self.thread_pitches = []
assets_root_path = get_assets_root_path()
for i in range(0, self._num_envs):
j = np.random.randint(0, len(self.cfg_env.env.desired_subassemblies))
subassembly = self.cfg_env.env.desired_subassemblies[j]
components = list(self.asset_info_nut_bolt[subassembly])
nut_translation = torch.tensor(
[
0.0,
self.cfg_env.env.nut_lateral_offset,
self.cfg_base.env.table_height,
],
device=self._device,
)
nut_orientation = torch.tensor([1.0, 0.0, 0.0, 0.0], device=self._device)
nut_height = self.asset_info_nut_bolt[subassembly][components[0]]["height"]
nut_width_max = self.asset_info_nut_bolt[subassembly][components[0]][
"width_max"
]
self.nut_heights.append(nut_height)
self.nut_widths_max.append(nut_width_max)
nut_file = (
assets_root_path
+ self.asset_info_nut_bolt[subassembly][components[0]]["usd_path"]
)
if add_to_stage:
add_reference_to_stage(nut_file, f"/World/envs/env_{i}" + "/nut")
XFormPrim(
prim_path=f"/World/envs/env_{i}" + "/nut",
translation=nut_translation,
orientation=nut_orientation,
)
self._stage.GetPrimAtPath(
f"/World/envs/env_{i}" + f"/nut/factory_{components[0]}/collisions"
).SetInstanceable(
False
) # This is required to be able to edit physics material
physicsUtils.add_physics_material_to_prim(
self._stage,
self._stage.GetPrimAtPath(
f"/World/envs/env_{i}"
+ f"/nut/factory_{components[0]}/collisions/mesh_0"
),
self.nutboltPhysicsMaterialPath,
)
# applies articulation settings from the task configuration yaml file
self._sim_config.apply_articulation_settings(
"nut",
self._stage.GetPrimAtPath(f"/World/envs/env_{i}" + "/nut"),
self._sim_config.parse_actor_config("nut"),
)
bolt_translation = torch.tensor(
[0.0, 0.0, self.cfg_base.env.table_height], device=self._device
)
bolt_orientation = torch.tensor([1.0, 0.0, 0.0, 0.0], device=self._device)
bolt_width = self.asset_info_nut_bolt[subassembly][components[1]]["width"]
bolt_head_height = self.asset_info_nut_bolt[subassembly][components[1]][
"head_height"
]
bolt_shank_length = self.asset_info_nut_bolt[subassembly][components[1]][
"shank_length"
]
self.bolt_widths.append(bolt_width)
self.bolt_head_heights.append(bolt_head_height)
self.bolt_shank_lengths.append(bolt_shank_length)
if add_to_stage:
bolt_file = (
assets_root_path
+ self.asset_info_nut_bolt[subassembly][components[1]]["usd_path"]
)
add_reference_to_stage(bolt_file, f"/World/envs/env_{i}" + "/bolt")
XFormPrim(
prim_path=f"/World/envs/env_{i}" + "/bolt",
translation=bolt_translation,
orientation=bolt_orientation,
)
self._stage.GetPrimAtPath(
f"/World/envs/env_{i}" + f"/bolt/factory_{components[1]}/collisions"
).SetInstanceable(
False
) # This is required to be able to edit physics material
physicsUtils.add_physics_material_to_prim(
self._stage,
self._stage.GetPrimAtPath(
f"/World/envs/env_{i}"
+ f"/bolt/factory_{components[1]}/collisions/mesh_0"
),
self.nutboltPhysicsMaterialPath,
)
# applies articulation settings from the task configuration yaml file
self._sim_config.apply_articulation_settings(
"bolt",
self._stage.GetPrimAtPath(f"/World/envs/env_{i}" + "/bolt"),
self._sim_config.parse_actor_config("bolt"),
)
thread_pitch = self.asset_info_nut_bolt[subassembly]["thread_pitch"]
self.thread_pitches.append(thread_pitch)
# For computing body COM pos
self.nut_heights = torch.tensor(
self.nut_heights, device=self._device
).unsqueeze(-1)
self.bolt_head_heights = torch.tensor(
self.bolt_head_heights, device=self._device
).unsqueeze(-1)
# For setting initial state
self.nut_widths_max = torch.tensor(
self.nut_widths_max, device=self._device
).unsqueeze(-1)
self.bolt_shank_lengths = torch.tensor(
self.bolt_shank_lengths, device=self._device
).unsqueeze(-1)
# For defining success or failure
self.bolt_widths = torch.tensor(
self.bolt_widths, device=self._device
).unsqueeze(-1)
self.thread_pitches = torch.tensor(
self.thread_pitches, device=self._device
).unsqueeze(-1)
def refresh_env_tensors(self):
"""Refresh tensors."""
# Nut tensors
self.nut_pos, self.nut_quat = self.nuts.get_world_poses(clone=False)
self.nut_pos -= self.env_pos
self.nut_com_pos = fc.translate_along_local_z(
pos=self.nut_pos,
quat=self.nut_quat,
offset=self.bolt_head_heights + self.nut_heights * 0.5,
device=self.device,
)
self.nut_com_quat = self.nut_quat # always equal
nut_velocities = self.nuts.get_velocities(clone=False)
self.nut_linvel = nut_velocities[:, 0:3]
self.nut_angvel = nut_velocities[:, 3:6]
self.nut_com_linvel = self.nut_linvel + torch.cross(
self.nut_angvel, (self.nut_com_pos - self.nut_pos), dim=1
)
self.nut_com_angvel = self.nut_angvel # always equal
self.nut_force = self.nuts.get_net_contact_forces(clone=False)
# Bolt tensors
self.bolt_pos, self.bolt_quat = self.bolts.get_world_poses(clone=False)
self.bolt_pos -= self.env_pos
self.bolt_force = self.bolts.get_net_contact_forces(clone=False)
| 14,703 | Python | 39.284931 | 110 | 0.603414 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/factory_control.py | # Copyright (c) 2018-2023, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""Factory: control module.
Imported by base, environment, and task classes. Not directly executed.
"""
import math
import omni.isaac.core.utils.torch as torch_utils
import torch
def compute_dof_pos_target(
cfg_ctrl,
arm_dof_pos,
fingertip_midpoint_pos,
fingertip_midpoint_quat,
jacobian,
ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat,
ctrl_target_gripper_dof_pos,
device,
):
"""Compute Franka DOF position target to move fingertips towards target pose."""
ctrl_target_dof_pos = torch.zeros((cfg_ctrl["num_envs"], 9), device=device)
pos_error, axis_angle_error = get_pose_error(
fingertip_midpoint_pos=fingertip_midpoint_pos,
fingertip_midpoint_quat=fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat,
jacobian_type=cfg_ctrl["jacobian_type"],
rot_error_type="axis_angle",
)
delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1)
delta_arm_dof_pos = _get_delta_dof_pos(
delta_pose=delta_fingertip_pose,
ik_method=cfg_ctrl["ik_method"],
jacobian=jacobian,
device=device,
)
ctrl_target_dof_pos[:, 0:7] = arm_dof_pos + delta_arm_dof_pos
ctrl_target_dof_pos[:, 7:9] = ctrl_target_gripper_dof_pos # gripper finger joints
return ctrl_target_dof_pos
def compute_dof_torque(
cfg_ctrl,
dof_pos,
dof_vel,
fingertip_midpoint_pos,
fingertip_midpoint_quat,
fingertip_midpoint_linvel,
fingertip_midpoint_angvel,
left_finger_force,
right_finger_force,
jacobian,
arm_mass_matrix,
ctrl_target_gripper_dof_pos,
ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat,
ctrl_target_fingertip_contact_wrench,
device,
):
"""Compute Franka DOF torque to move fingertips towards target pose."""
# References:
# 1) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf
# 2) Modern Robotics
dof_torque = torch.zeros((cfg_ctrl["num_envs"], 9), device=device)
if cfg_ctrl["gain_space"] == "joint":
pos_error, axis_angle_error = get_pose_error(
fingertip_midpoint_pos=fingertip_midpoint_pos,
fingertip_midpoint_quat=fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat,
jacobian_type=cfg_ctrl["jacobian_type"],
rot_error_type="axis_angle",
)
delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1)
# Set tau = k_p * joint_pos_error - k_d * joint_vel_error (ETH eq. 3.72)
delta_arm_dof_pos = _get_delta_dof_pos(
delta_pose=delta_fingertip_pose,
ik_method=cfg_ctrl["ik_method"],
jacobian=jacobian,
device=device,
)
dof_torque[:, 0:7] = cfg_ctrl[
"joint_prop_gains"
] * delta_arm_dof_pos + cfg_ctrl["joint_deriv_gains"] * (0.0 - dof_vel[:, 0:7])
if cfg_ctrl["do_inertial_comp"]:
# Set tau = M * tau, where M is the joint-space mass matrix
arm_mass_matrix_joint = arm_mass_matrix
dof_torque[:, 0:7] = (
arm_mass_matrix_joint @ dof_torque[:, 0:7].unsqueeze(-1)
).squeeze(-1)
elif cfg_ctrl["gain_space"] == "task":
task_wrench = torch.zeros((cfg_ctrl["num_envs"], 6), device=device)
if cfg_ctrl["do_motion_ctrl"]:
pos_error, axis_angle_error = get_pose_error(
fingertip_midpoint_pos=fingertip_midpoint_pos,
fingertip_midpoint_quat=fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos=ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat=ctrl_target_fingertip_midpoint_quat,
jacobian_type=cfg_ctrl["jacobian_type"],
rot_error_type="axis_angle",
)
delta_fingertip_pose = torch.cat((pos_error, axis_angle_error), dim=1)
# Set tau = k_p * task_pos_error - k_d * task_vel_error (building towards eq. 3.96-3.98)
task_wrench_motion = _apply_task_space_gains(
delta_fingertip_pose=delta_fingertip_pose,
fingertip_midpoint_linvel=fingertip_midpoint_linvel,
fingertip_midpoint_angvel=fingertip_midpoint_angvel,
task_prop_gains=cfg_ctrl["task_prop_gains"],
task_deriv_gains=cfg_ctrl["task_deriv_gains"],
)
if cfg_ctrl["do_inertial_comp"]:
# Set tau = Lambda * tau, where Lambda is the task-space mass matrix
jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2)
arm_mass_matrix_task = torch.inverse(
jacobian @ torch.inverse(arm_mass_matrix) @ jacobian_T
) # ETH eq. 3.86; geometric Jacobian is assumed
task_wrench_motion = (
arm_mass_matrix_task @ task_wrench_motion.unsqueeze(-1)
).squeeze(-1)
task_wrench = (
task_wrench + cfg_ctrl["motion_ctrl_axes"] * task_wrench_motion
)
if cfg_ctrl["do_force_ctrl"]:
# Set tau = tau + F_t, where F_t is the target contact wrench
task_wrench_force = torch.zeros((cfg_ctrl["num_envs"], 6), device=device)
task_wrench_force = (
task_wrench_force + ctrl_target_fingertip_contact_wrench
) # open-loop force control (building towards ETH eq. 3.96-3.98)
if cfg_ctrl["force_ctrl_method"] == "closed":
force_error, torque_error = _get_wrench_error(
left_finger_force=left_finger_force,
right_finger_force=right_finger_force,
ctrl_target_fingertip_contact_wrench=ctrl_target_fingertip_contact_wrench,
num_envs=cfg_ctrl["num_envs"],
device=device,
)
# Set tau = tau + k_p * contact_wrench_error
task_wrench_force = task_wrench_force + cfg_ctrl[
"wrench_prop_gains"
] * torch.cat(
(force_error, torque_error), dim=1
) # part of Modern Robotics eq. 11.61
task_wrench = (
task_wrench
+ torch.tensor(cfg_ctrl["force_ctrl_axes"], device=device).unsqueeze(0)
* task_wrench_force
)
# Set tau = J^T * tau, i.e., map tau into joint space as desired
jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2)
dof_torque[:, 0:7] = (jacobian_T @ task_wrench.unsqueeze(-1)).squeeze(-1)
dof_torque[:, 7:9] = cfg_ctrl["gripper_prop_gains"] * (
ctrl_target_gripper_dof_pos - dof_pos[:, 7:9]
) + cfg_ctrl["gripper_deriv_gains"] * (
0.0 - dof_vel[:, 7:9]
) # gripper finger joints
dof_torque = torch.clamp(dof_torque, min=-100.0, max=100.0)
return dof_torque
def get_pose_error(
fingertip_midpoint_pos,
fingertip_midpoint_quat,
ctrl_target_fingertip_midpoint_pos,
ctrl_target_fingertip_midpoint_quat,
jacobian_type,
rot_error_type,
):
"""Compute task-space error between target Franka fingertip pose and current pose."""
# Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf
# Compute pos error
pos_error = ctrl_target_fingertip_midpoint_pos - fingertip_midpoint_pos
# Compute rot error
if (
jacobian_type == "geometric"
): # See example 2.9.8; note use of J_g and transformation between rotation vectors
# Compute quat error (i.e., difference quat)
# Reference: https://personal.utdallas.edu/~sxb027100/dock/quat.html
fingertip_midpoint_quat_norm = torch_utils.quat_mul(
fingertip_midpoint_quat, torch_utils.quat_conjugate(fingertip_midpoint_quat)
)[
:, 0
] # scalar component
fingertip_midpoint_quat_inv = torch_utils.quat_conjugate(
fingertip_midpoint_quat
) / fingertip_midpoint_quat_norm.unsqueeze(-1)
quat_error = torch_utils.quat_mul(
ctrl_target_fingertip_midpoint_quat, fingertip_midpoint_quat_inv
)
# Convert to axis-angle error
axis_angle_error = axis_angle_from_quat(quat_error)
elif (
jacobian_type == "analytic"
): # See example 2.9.7; note use of J_a and difference of rotation vectors
# Compute axis-angle error
axis_angle_error = axis_angle_from_quat(
ctrl_target_fingertip_midpoint_quat
) - axis_angle_from_quat(fingertip_midpoint_quat)
if rot_error_type == "quat":
return pos_error, quat_error
elif rot_error_type == "axis_angle":
return pos_error, axis_angle_error
def _get_wrench_error(
left_finger_force,
right_finger_force,
ctrl_target_fingertip_contact_wrench,
num_envs,
device,
):
"""Compute task-space error between target Franka fingertip contact wrench and current wrench."""
fingertip_contact_wrench = torch.zeros((num_envs, 6), device=device)
fingertip_contact_wrench[:, 0:3] = (
left_finger_force + right_finger_force
) # net contact force on fingers
# Cols 3 to 6 are all zeros, as we do not have enough information
force_error = ctrl_target_fingertip_contact_wrench[:, 0:3] - (
-fingertip_contact_wrench[:, 0:3]
)
torque_error = ctrl_target_fingertip_contact_wrench[:, 3:6] - (
-fingertip_contact_wrench[:, 3:6]
)
return force_error, torque_error
def _get_delta_dof_pos(delta_pose, ik_method, jacobian, device):
"""Get delta Franka DOF position from delta pose using specified IK method."""
# References:
# 1) https://www.cs.cmu.edu/~15464-s13/lectures/lecture6/iksurvey.pdf
# 2) https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf (p. 47)
if ik_method == "pinv": # Jacobian pseudoinverse
k_val = 1.0
jacobian_pinv = torch.linalg.pinv(jacobian)
delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1)
delta_dof_pos = delta_dof_pos.squeeze(-1)
elif ik_method == "trans": # Jacobian transpose
k_val = 1.0
jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2)
delta_dof_pos = k_val * jacobian_T @ delta_pose.unsqueeze(-1)
delta_dof_pos = delta_dof_pos.squeeze(-1)
elif ik_method == "dls": # damped least squares (Levenberg-Marquardt)
lambda_val = 0.1
jacobian_T = torch.transpose(jacobian, dim0=1, dim1=2)
lambda_matrix = (lambda_val**2) * torch.eye(
n=jacobian.shape[1], device=device
)
delta_dof_pos = (
jacobian_T
@ torch.inverse(jacobian @ jacobian_T + lambda_matrix)
@ delta_pose.unsqueeze(-1)
)
delta_dof_pos = delta_dof_pos.squeeze(-1)
elif ik_method == "svd": # adaptive SVD
k_val = 1.0
U, S, Vh = torch.linalg.svd(jacobian)
S_inv = 1.0 / S
min_singular_value = 1.0e-5
S_inv = torch.where(S > min_singular_value, S_inv, torch.zeros_like(S_inv))
jacobian_pinv = (
torch.transpose(Vh, dim0=1, dim1=2)[:, :, :6]
@ torch.diag_embed(S_inv)
@ torch.transpose(U, dim0=1, dim1=2)
)
delta_dof_pos = k_val * jacobian_pinv @ delta_pose.unsqueeze(-1)
delta_dof_pos = delta_dof_pos.squeeze(-1)
return delta_dof_pos
def _apply_task_space_gains(
delta_fingertip_pose,
fingertip_midpoint_linvel,
fingertip_midpoint_angvel,
task_prop_gains,
task_deriv_gains,
):
"""Interpret PD gains as task-space gains. Apply to task-space error."""
task_wrench = torch.zeros_like(delta_fingertip_pose)
# Apply gains to lin error components
lin_error = delta_fingertip_pose[:, 0:3]
task_wrench[:, 0:3] = task_prop_gains[:, 0:3] * lin_error + task_deriv_gains[
:, 0:3
] * (0.0 - fingertip_midpoint_linvel)
# Apply gains to rot error components
rot_error = delta_fingertip_pose[:, 3:6]
task_wrench[:, 3:6] = task_prop_gains[:, 3:6] * rot_error + task_deriv_gains[
:, 3:6
] * (0.0 - fingertip_midpoint_angvel)
return task_wrench
def get_analytic_jacobian(fingertip_quat, fingertip_jacobian, num_envs, device):
"""Convert geometric Jacobian to analytic Jacobian."""
# Reference: https://ethz.ch/content/dam/ethz/special-interest/mavt/robotics-n-intelligent-systems/rsl-dam/documents/RobotDynamics2018/RD_HS2018script.pdf
# NOTE: Gym returns world-space geometric Jacobians by default
batch = num_envs
# Overview:
# x = [x_p; x_r]
# From eq. 2.189 and 2.192, x_dot = J_a @ q_dot = (E_inv @ J_g) @ q_dot
# From eq. 2.191, E = block(E_p, E_r); thus, E_inv = block(E_p_inv, E_r_inv)
# Eq. 2.12 gives an expression for E_p_inv
# Eq. 2.107 gives an expression for E_r_inv
# Compute E_inv_top (i.e., [E_p_inv, 0])
I = torch.eye(3, device=device)
E_p_inv = I.repeat((batch, 1)).reshape(batch, 3, 3)
E_inv_top = torch.cat((E_p_inv, torch.zeros((batch, 3, 3), device=device)), dim=2)
# Compute E_inv_bottom (i.e., [0, E_r_inv])
fingertip_axis_angle = axis_angle_from_quat(fingertip_quat)
fingertip_axis_angle_cross = get_skew_symm_matrix(
fingertip_axis_angle, device=device
)
fingertip_angle = torch.linalg.vector_norm(fingertip_axis_angle, dim=1)
factor_1 = 1 / (fingertip_angle**2)
factor_2 = 1 - fingertip_angle * 0.5 * torch.sin(fingertip_angle) / (
1 - torch.cos(fingertip_angle)
)
factor_3 = factor_1 * factor_2
E_r_inv = (
I
- 1 * 0.5 * fingertip_axis_angle_cross
+ (fingertip_axis_angle_cross @ fingertip_axis_angle_cross)
* factor_3.unsqueeze(-1).repeat((1, 3 * 3)).reshape((batch, 3, 3))
)
E_inv_bottom = torch.cat(
(torch.zeros((batch, 3, 3), device=device), E_r_inv), dim=2
)
E_inv = torch.cat(
(E_inv_top.reshape((batch, 3 * 6)), E_inv_bottom.reshape((batch, 3 * 6))), dim=1
).reshape((batch, 6, 6))
J_a = E_inv @ fingertip_jacobian
return J_a
def get_skew_symm_matrix(vec, device):
"""Convert vector to skew-symmetric matrix."""
# Reference: https://en.wikipedia.org/wiki/Cross_product#Conversion_to_matrix_multiplication
batch = vec.shape[0]
I = torch.eye(3, device=device)
skew_symm = torch.transpose(
torch.cross(
vec.repeat((1, 3)).reshape((batch * 3, 3)), I.repeat((batch, 1))
).reshape(batch, 3, 3),
dim0=1,
dim1=2,
)
return skew_symm
def translate_along_local_z(pos, quat, offset, device):
"""Translate global body position along local Z-axis and express in global coordinates."""
num_vecs = pos.shape[0]
offset_vec = offset * torch.tensor([0.0, 0.0, 1.0], device=device).repeat(
(num_vecs, 1)
)
_, translated_pos = torch_utils.tf_combine(
q1=quat,
t1=pos,
q2=torch.tensor([1.0, 0.0, 0.0, 0.0], device=device).repeat((num_vecs, 1)),
t2=offset_vec,
)
return translated_pos
def axis_angle_from_euler(euler):
"""Convert tensor of Euler angles to tensor of axis-angles."""
quat = torch_utils.quat_from_euler_xyz(
roll=euler[:, 0], pitch=euler[:, 1], yaw=euler[:, 2]
)
quat = quat * torch.sign(quat[:, 0]).unsqueeze(-1) # smaller rotation
axis_angle = axis_angle_from_quat(quat)
return axis_angle
def axis_angle_from_quat(quat, eps=1.0e-6):
"""Convert tensor of quaternions to tensor of axis-angles."""
# Reference: https://github.com/facebookresearch/pytorch3d/blob/bee31c48d3d36a8ea268f9835663c52ff4a476ec/pytorch3d/transforms/rotation_conversions.py#L516-L544
mag = torch.linalg.norm(quat[:, 1:4], dim=1)
half_angle = torch.atan2(mag, quat[:, 0])
angle = 2.0 * half_angle
sin_half_angle_over_angle = torch.where(
torch.abs(angle) > eps, torch.sin(half_angle) / angle, 1 / 2 - angle**2.0 / 48
)
axis_angle = quat[:, 1:4] / sin_half_angle_over_angle.unsqueeze(-1)
return axis_angle
def axis_angle_from_quat_naive(quat):
"""Convert tensor of quaternions to tensor of axis-angles."""
# Reference: https://en.wikipedia.org/wiki/quats_and_spatial_rotation#Recovering_the_axis-angle_representation
# NOTE: Susceptible to undesirable behavior due to divide-by-zero
mag = torch.linalg.vector_norm(quat[:, 1:4], dim=1) # zero when quat = [1, 0, 0, 0]
axis = quat[:, 1:4] / mag.unsqueeze(-1)
angle = 2.0 * torch.atan2(mag, quat[:, 0])
axis_angle = axis * angle.unsqueeze(-1)
return axis_angle
def get_rand_quat(num_quats, device):
"""Generate tensor of random quaternions."""
# Reference: http://planning.cs.uiuc.edu/node198.html
u = torch.rand((num_quats, 3), device=device)
quat = torch.zeros((num_quats, 4), device=device)
quat[:, 0] = torch.sqrt(u[:, 0]) * torch.cos(2 * math.pi * u[:, 2])
quat[:, 1] = torch.sqrt(1 - u[:, 0]) * torch.sin(2 * math.pi * u[:, 1])
quat[:, 2] = torch.sqrt(1 - u[:, 0]) * torch.cos(2 * math.pi * u[:, 1])
quat[:, 3] = torch.sqrt(u[:, 0]) * torch.sin(2 * math.pi * u[:, 2])
return quat
def get_nonrand_quat(num_quats, rot_perturbation, device):
"""Generate tensor of non-random quaternions by composing random Euler rotations."""
quat = torch_utils.quat_from_euler_xyz(
torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0
- rot_perturbation,
torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0
- rot_perturbation,
torch.rand((num_quats, 1), device=device).squeeze() * rot_perturbation * 2.0
- rot_perturbation,
)
return quat
| 19,859 | Python | 37.864971 | 163 | 0.627574 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/yaml/factory_asset_info_nut_bolt.yaml | nut_bolt_m4:
nut:
usd_path: '/Isaac/Props/Factory/factory_nut_m4_tight/factory_nut_m4_tight.usd'
width_min: 0.007 # distance from flat surface to flat surface
width_max: 0.0080829 # distance from edge to edge
height: 0.0032 # height of nut
flat_length: 0.00404145 # length of flat surface
bolt:
usd_path: '/Isaac/Props/Factory/factory_bolt_m4_tight/factory_bolt_m4_tight.usd'
width: 0.004 # major diameter of bolt
head_height: 0.004 # height of bolt head
shank_length: 0.016 # length of bolt shank
thread_pitch: 0.0007 # distance between threads
nut_bolt_m8:
nut:
usd_path: '/Isaac/Props/Factory/factory_nut_m8_tight/factory_nut_m8_tight.usd'
width_min: 0.013
width_max: 0.01501111
height: 0.0065
flat_length: 0.00750555
bolt:
usd_path: '/Isaac/Props/Factory/factory_bolt_m8_tight/factory_bolt_m8_tight.usd'
width: 0.008
head_height: 0.008
shank_length: 0.018
thread_pitch: 0.00125
nut_bolt_m12:
nut:
usd_path: '/Isaac/Props/Factory/factory_nut_m12_tight/factory_nut_m12_tight.usd'
width_min: 0.019
width_max: 0.02193931
height: 0.010
flat_length: 0.01096966
bolt:
usd_path: '/Isaac/Props/Factory/factory_bolt_m12_tight/factory_bolt_m12_tight.usd'
width: 0.012
head_height: 0.012
shank_length: 0.020
thread_pitch: 0.00175
nut_bolt_m16:
nut:
usd_path: '/Isaac/Props/Factory/factory_nut_m16_tight/factory_nut_m16_tight.usd'
width_min: 0.024
width_max: 0.02771281
height: 0.013
flat_length: 0.01385641
bolt:
usd_path: '/Isaac/Props/Factory/factory_bolt_m16_tight/factory_bolt_m16_tight.usd'
width: 0.016
head_height: 0.016
shank_length: 0.025
thread_pitch: 0.002
nut_bolt_m20:
nut:
usd_path: '/Isaac/Props/Factory/factory_nut_m20_tight/factory_nut_m20_tight.usd'
width_min: 0.030
width_max: 0.03464102
height: 0.016
flat_length: 0.01732051
bolt:
usd_path: '/Isaac/Props/Factory/factory_bolt_m20_tight/factory_bolt_m20_tight.usd'
width: 0.020
head_height: 0.020
shank_length: 0.045
thread_pitch: 0.0025
| 2,331 | YAML | 32.314285 | 90 | 0.617332 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/factory/yaml/factory_asset_info_franka_table.yaml | franka_hand_length: 0.0584 # distance from origin of hand to origin of finger
franka_finger_length: 0.053671 # distance from origin of finger to bottom of fingerpad
franka_fingerpad_length: 0.017608 # distance from top of inner surface of fingerpad to bottom of inner surface of fingerpad
franka_gripper_width_max: 0.080 # maximum opening width of gripper
table_depth: 0.6 # depth of table
table_width: 1.0 # width of table
| 431 | YAML | 52.999993 | 124 | 0.772622 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/utils/anymal_terrain_generator.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
import numpy as np
import torch
from omniisaacgymenvs.utils.terrain_utils.terrain_utils import *
# terrain generator
class Terrain:
def __init__(self, cfg, num_robots) -> None:
self.horizontal_scale = 0.1
self.vertical_scale = 0.005
self.border_size = 20
self.num_per_env = 2
self.env_length = cfg["mapLength"]
self.env_width = cfg["mapWidth"]
self.proportions = [np.sum(cfg["terrainProportions"][: i + 1]) for i in range(len(cfg["terrainProportions"]))]
self.env_rows = cfg["numLevels"]
self.env_cols = cfg["numTerrains"]
self.num_maps = self.env_rows * self.env_cols
self.num_per_env = int(num_robots / self.num_maps)
self.env_origins = np.zeros((self.env_rows, self.env_cols, 3))
self.width_per_env_pixels = int(self.env_width / self.horizontal_scale)
self.length_per_env_pixels = int(self.env_length / self.horizontal_scale)
self.border = int(self.border_size / self.horizontal_scale)
self.tot_cols = int(self.env_cols * self.width_per_env_pixels) + 2 * self.border
self.tot_rows = int(self.env_rows * self.length_per_env_pixels) + 2 * self.border
self.height_field_raw = np.zeros((self.tot_rows, self.tot_cols), dtype=np.int16)
if cfg["curriculum"]:
self.curiculum(num_robots, num_terrains=self.env_cols, num_levels=self.env_rows)
else:
self.randomized_terrain()
self.heightsamples = self.height_field_raw
self.vertices, self.triangles = convert_heightfield_to_trimesh(
self.height_field_raw, self.horizontal_scale, self.vertical_scale, cfg["slopeTreshold"]
)
def randomized_terrain(self):
for k in range(self.num_maps):
# Env coordinates in the world
(i, j) = np.unravel_index(k, (self.env_rows, self.env_cols))
# Heightfield coordinate system from now on
start_x = self.border + i * self.length_per_env_pixels
end_x = self.border + (i + 1) * self.length_per_env_pixels
start_y = self.border + j * self.width_per_env_pixels
end_y = self.border + (j + 1) * self.width_per_env_pixels
terrain = SubTerrain(
"terrain",
width=self.width_per_env_pixels,
length=self.width_per_env_pixels,
vertical_scale=self.vertical_scale,
horizontal_scale=self.horizontal_scale,
)
choice = np.random.uniform(0, 1)
if choice < 0.1:
if np.random.choice([0, 1]):
pyramid_sloped_terrain(terrain, np.random.choice([-0.3, -0.2, 0, 0.2, 0.3]))
random_uniform_terrain(terrain, min_height=-0.1, max_height=0.1, step=0.05, downsampled_scale=0.2)
else:
pyramid_sloped_terrain(terrain, np.random.choice([-0.3, -0.2, 0, 0.2, 0.3]))
elif choice < 0.6:
# step_height = np.random.choice([-0.18, -0.15, -0.1, -0.05, 0.05, 0.1, 0.15, 0.18])
step_height = np.random.choice([-0.15, 0.15])
pyramid_stairs_terrain(terrain, step_width=0.31, step_height=step_height, platform_size=3.0)
elif choice < 1.0:
discrete_obstacles_terrain(terrain, 0.15, 1.0, 2.0, 40, platform_size=3.0)
self.height_field_raw[start_x:end_x, start_y:end_y] = terrain.height_field_raw
env_origin_x = (i + 0.5) * self.env_length
env_origin_y = (j + 0.5) * self.env_width
x1 = int((self.env_length / 2.0 - 1) / self.horizontal_scale)
x2 = int((self.env_length / 2.0 + 1) / self.horizontal_scale)
y1 = int((self.env_width / 2.0 - 1) / self.horizontal_scale)
y2 = int((self.env_width / 2.0 + 1) / self.horizontal_scale)
env_origin_z = np.max(terrain.height_field_raw[x1:x2, y1:y2]) * self.vertical_scale
self.env_origins[i, j] = [env_origin_x, env_origin_y, env_origin_z]
def curiculum(self, num_robots, num_terrains, num_levels):
num_robots_per_map = int(num_robots / num_terrains)
left_over = num_robots % num_terrains
idx = 0
for j in range(num_terrains):
for i in range(num_levels):
terrain = SubTerrain(
"terrain",
width=self.width_per_env_pixels,
length=self.width_per_env_pixels,
vertical_scale=self.vertical_scale,
horizontal_scale=self.horizontal_scale,
)
difficulty = i / num_levels
choice = j / num_terrains
slope = difficulty * 0.4
step_height = 0.05 + 0.175 * difficulty
discrete_obstacles_height = 0.025 + difficulty * 0.15
stepping_stones_size = 2 - 1.8 * difficulty
if choice < self.proportions[0]:
if choice < 0.05:
slope *= -1
pyramid_sloped_terrain(terrain, slope=slope, platform_size=3.0)
elif choice < self.proportions[1]:
if choice < 0.15:
slope *= -1
pyramid_sloped_terrain(terrain, slope=slope, platform_size=3.0)
random_uniform_terrain(terrain, min_height=-0.1, max_height=0.1, step=0.025, downsampled_scale=0.2)
elif choice < self.proportions[3]:
if choice < self.proportions[2]:
step_height *= -1
pyramid_stairs_terrain(terrain, step_width=0.31, step_height=step_height, platform_size=3.0)
elif choice < self.proportions[4]:
discrete_obstacles_terrain(terrain, discrete_obstacles_height, 1.0, 2.0, 40, platform_size=3.0)
else:
stepping_stones_terrain(
terrain, stone_size=stepping_stones_size, stone_distance=0.1, max_height=0.0, platform_size=3.0
)
# Heightfield coordinate system
start_x = self.border + i * self.length_per_env_pixels
end_x = self.border + (i + 1) * self.length_per_env_pixels
start_y = self.border + j * self.width_per_env_pixels
end_y = self.border + (j + 1) * self.width_per_env_pixels
self.height_field_raw[start_x:end_x, start_y:end_y] = terrain.height_field_raw
robots_in_map = num_robots_per_map
if j < left_over:
robots_in_map += 1
env_origin_x = (i + 0.5) * self.env_length
env_origin_y = (j + 0.5) * self.env_width
x1 = int((self.env_length / 2.0 - 1) / self.horizontal_scale)
x2 = int((self.env_length / 2.0 + 1) / self.horizontal_scale)
y1 = int((self.env_width / 2.0 - 1) / self.horizontal_scale)
y2 = int((self.env_width / 2.0 + 1) / self.horizontal_scale)
env_origin_z = np.max(terrain.height_field_raw[x1:x2, y1:y2]) * self.vertical_scale
self.env_origins[i, j] = [env_origin_x, env_origin_y, env_origin_z]
| 8,852 | Python | 50.47093 | 119 | 0.591618 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/utils/usd_utils.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.stage import get_current_stage
from pxr import UsdLux, UsdPhysics
def set_drive_type(prim_path, drive_type):
joint_prim = get_prim_at_path(prim_path)
# set drive type ("angular" or "linear")
drive = UsdPhysics.DriveAPI.Apply(joint_prim, drive_type)
return drive
def set_drive_target_position(drive, target_value):
if not drive.GetTargetPositionAttr():
drive.CreateTargetPositionAttr(target_value)
else:
drive.GetTargetPositionAttr().Set(target_value)
def set_drive_target_velocity(drive, target_value):
if not drive.GetTargetVelocityAttr():
drive.CreateTargetVelocityAttr(target_value)
else:
drive.GetTargetVelocityAttr().Set(target_value)
def set_drive_stiffness(drive, stiffness):
if not drive.GetStiffnessAttr():
drive.CreateStiffnessAttr(stiffness)
else:
drive.GetStiffnessAttr().Set(stiffness)
def set_drive_damping(drive, damping):
if not drive.GetDampingAttr():
drive.CreateDampingAttr(damping)
else:
drive.GetDampingAttr().Set(damping)
def set_drive_max_force(drive, max_force):
if not drive.GetMaxForceAttr():
drive.CreateMaxForceAttr(max_force)
else:
drive.GetMaxForceAttr().Set(max_force)
def set_drive(prim_path, drive_type, target_type, target_value, stiffness, damping, max_force) -> None:
drive = set_drive_type(prim_path, drive_type)
# set target type ("position" or "velocity")
if target_type == "position":
set_drive_target_position(drive, target_value)
elif target_type == "velocity":
set_drive_target_velocity(drive, target_value)
set_drive_stiffness(drive, stiffness)
set_drive_damping(drive, damping)
set_drive_max_force(drive, max_force)
| 3,403 | Python | 36.406593 | 103 | 0.740229 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/shared/in_hand_manipulation.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
from abc import abstractmethod
import numpy as np
import torch
from omni.isaac.core.prims import RigidPrimView, XFormPrim
from omni.isaac.core.utils.nucleus import get_assets_root_path
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.stage import add_reference_to_stage, get_current_stage
from omni.isaac.core.utils.torch import *
from omniisaacgymenvs.tasks.base.rl_task import RLTask
class InHandManipulationTask(RLTask):
def __init__(self, name, env, offset=None) -> None:
InHandManipulationTask.update_config(self)
RLTask.__init__(self, name, env)
self.x_unit_tensor = torch.tensor([1, 0, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.y_unit_tensor = torch.tensor([0, 1, 0], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.z_unit_tensor = torch.tensor([0, 0, 1], dtype=torch.float, device=self.device).repeat((self.num_envs, 1))
self.reset_goal_buf = self.reset_buf.clone()
self.successes = torch.zeros(self.num_envs, dtype=torch.float, device=self.device)
self.consecutive_successes = torch.zeros(1, dtype=torch.float, device=self.device)
self.randomization_buf = torch.zeros(self.num_envs, dtype=torch.long, device=self.device)
self.av_factor = torch.tensor(self.av_factor, dtype=torch.float, device=self.device)
self.total_successes = 0
self.total_resets = 0
def update_config(self):
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self.dist_reward_scale = self._task_cfg["env"]["distRewardScale"]
self.rot_reward_scale = self._task_cfg["env"]["rotRewardScale"]
self.action_penalty_scale = self._task_cfg["env"]["actionPenaltyScale"]
self.success_tolerance = self._task_cfg["env"]["successTolerance"]
self.reach_goal_bonus = self._task_cfg["env"]["reachGoalBonus"]
self.fall_dist = self._task_cfg["env"]["fallDistance"]
self.fall_penalty = self._task_cfg["env"]["fallPenalty"]
self.rot_eps = self._task_cfg["env"]["rotEps"]
self.vel_obs_scale = self._task_cfg["env"]["velObsScale"]
self.reset_position_noise = self._task_cfg["env"]["resetPositionNoise"]
self.reset_rotation_noise = self._task_cfg["env"]["resetRotationNoise"]
self.reset_dof_pos_noise = self._task_cfg["env"]["resetDofPosRandomInterval"]
self.reset_dof_vel_noise = self._task_cfg["env"]["resetDofVelRandomInterval"]
self.hand_dof_speed_scale = self._task_cfg["env"]["dofSpeedScale"]
self.use_relative_control = self._task_cfg["env"]["useRelativeControl"]
self.act_moving_average = self._task_cfg["env"]["actionsMovingAverage"]
self.max_episode_length = self._task_cfg["env"]["episodeLength"]
self.reset_time = self._task_cfg["env"].get("resetTime", -1.0)
self.print_success_stat = self._task_cfg["env"]["printNumSuccesses"]
self.max_consecutive_successes = self._task_cfg["env"]["maxConsecutiveSuccesses"]
self.av_factor = self._task_cfg["env"].get("averFactor", 0.1)
self.dt = 1.0 / 60
control_freq_inv = self._task_cfg["env"].get("controlFrequencyInv", 1)
if self.reset_time > 0.0:
self.max_episode_length = int(round(self.reset_time / (control_freq_inv * self.dt)))
print("Reset time: ", self.reset_time)
print("New episode length: ", self.max_episode_length)
def set_up_scene(self, scene) -> None:
self._stage = get_current_stage()
self._assets_root_path = get_assets_root_path()
self.get_starting_positions()
self.get_hand()
self.object_start_translation = self.hand_start_translation.clone()
self.object_start_translation[1] += self.pose_dy
self.object_start_translation[2] += self.pose_dz
self.object_start_orientation = torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device)
self.goal_displacement_tensor = torch.tensor([-0.2, -0.06, 0.12], device=self.device)
self.goal_start_translation = self.object_start_translation + self.goal_displacement_tensor
self.goal_start_translation[2] -= 0.04
self.goal_start_orientation = torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device)
self.get_object(self.hand_start_translation, self.pose_dy, self.pose_dz)
self.get_goal()
super().set_up_scene(scene, filter_collisions=False)
self._hands = self.get_hand_view(scene)
scene.add(self._hands)
self._objects = RigidPrimView(
prim_paths_expr="/World/envs/env_.*/object/object",
name="object_view",
reset_xform_properties=False,
masses=torch.tensor([0.07087] * self._num_envs, device=self.device),
)
scene.add(self._objects)
self._goals = RigidPrimView(
prim_paths_expr="/World/envs/env_.*/goal/object", name="goal_view", reset_xform_properties=False
)
self._goals._non_root_link = True # hack to ignore kinematics
scene.add(self._goals)
if self._dr_randomizer.randomize:
self._dr_randomizer.apply_on_startup_domain_randomization(self)
def initialize_views(self, scene):
RLTask.initialize_views(self, scene)
if scene.object_exists("shadow_hand_view"):
scene.remove_object("shadow_hand_view", registry_only=True)
if scene.object_exists("finger_view"):
scene.remove_object("finger_view", registry_only=True)
if scene.object_exists("allegro_hand_view"):
scene.remove_object("allegro_hand_view", registry_only=True)
if scene.object_exists("goal_view"):
scene.remove_object("goal_view", registry_only=True)
if scene.object_exists("object_view"):
scene.remove_object("object_view", registry_only=True)
self.get_starting_positions()
self.object_start_translation = self.hand_start_translation.clone()
self.object_start_translation[1] += self.pose_dy
self.object_start_translation[2] += self.pose_dz
self.object_start_orientation = torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device)
self.goal_displacement_tensor = torch.tensor([-0.2, -0.06, 0.12], device=self.device)
self.goal_start_translation = self.object_start_translation + self.goal_displacement_tensor
self.goal_start_translation[2] -= 0.04
self.goal_start_orientation = torch.tensor([1.0, 0.0, 0.0, 0.0], device=self.device)
self._hands = self.get_hand_view(scene)
scene.add(self._hands)
self._objects = RigidPrimView(
prim_paths_expr="/World/envs/env_.*/object/object",
name="object_view",
reset_xform_properties=False,
masses=torch.tensor([0.07087] * self._num_envs, device=self.device),
)
scene.add(self._objects)
self._goals = RigidPrimView(
prim_paths_expr="/World/envs/env_.*/goal/object", name="goal_view", reset_xform_properties=False
)
self._goals._non_root_link = True # hack to ignore kinematics
scene.add(self._goals)
if self._dr_randomizer.randomize:
self._dr_randomizer.apply_on_startup_domain_randomization(self)
@abstractmethod
def get_hand(self):
pass
@abstractmethod
def get_hand_view(self):
pass
@abstractmethod
def get_observations(self):
pass
def get_object(self, hand_start_translation, pose_dy, pose_dz):
self.object_usd_path = f"{self._assets_root_path}/Isaac/Props/Blocks/block_instanceable.usd"
add_reference_to_stage(self.object_usd_path, self.default_zero_env_path + "/object")
obj = XFormPrim(
prim_path=self.default_zero_env_path + "/object/object",
name="object",
translation=self.object_start_translation,
orientation=self.object_start_orientation,
scale=self.object_scale,
)
self._sim_config.apply_articulation_settings(
"object", get_prim_at_path(obj.prim_path), self._sim_config.parse_actor_config("object")
)
def get_goal(self):
add_reference_to_stage(self.object_usd_path, self.default_zero_env_path + "/goal")
goal = XFormPrim(
prim_path=self.default_zero_env_path + "/goal",
name="goal",
translation=self.goal_start_translation,
orientation=self.goal_start_orientation,
scale=self.object_scale,
)
self._sim_config.apply_articulation_settings(
"goal", get_prim_at_path(goal.prim_path), self._sim_config.parse_actor_config("goal_object")
)
def post_reset(self):
self.num_hand_dofs = self._hands.num_dof
self.actuated_dof_indices = self._hands.actuated_dof_indices
self.hand_dof_targets = torch.zeros((self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device)
self.prev_targets = torch.zeros((self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device)
self.cur_targets = torch.zeros((self.num_envs, self.num_hand_dofs), dtype=torch.float, device=self.device)
dof_limits = self._hands.get_dof_limits()
self.hand_dof_lower_limits, self.hand_dof_upper_limits = torch.t(dof_limits[0].to(self.device))
self.hand_dof_default_pos = torch.zeros(self.num_hand_dofs, dtype=torch.float, device=self.device)
self.hand_dof_default_vel = torch.zeros(self.num_hand_dofs, dtype=torch.float, device=self.device)
self.object_init_pos, self.object_init_rot = self._objects.get_world_poses()
self.object_init_pos -= self._env_pos
self.object_init_velocities = torch.zeros_like(
self._objects.get_velocities(), dtype=torch.float, device=self.device
)
self.goal_pos = self.object_init_pos.clone()
self.goal_pos[:, 2] -= 0.04
self.goal_rot = self.object_init_rot.clone()
self.goal_init_pos = self.goal_pos.clone()
self.goal_init_rot = self.goal_rot.clone()
# randomize all envs
indices = torch.arange(self._num_envs, dtype=torch.int64, device=self._device)
self.reset_idx(indices)
if self._dr_randomizer.randomize:
self._dr_randomizer.set_up_domain_randomization(self)
def get_object_goal_observations(self):
self.object_pos, self.object_rot = self._objects.get_world_poses(clone=False)
self.object_pos -= self._env_pos
self.object_velocities = self._objects.get_velocities(clone=False)
self.object_linvel = self.object_velocities[:, 0:3]
self.object_angvel = self.object_velocities[:, 3:6]
def calculate_metrics(self):
(
self.rew_buf[:],
self.reset_buf[:],
self.reset_goal_buf[:],
self.progress_buf[:],
self.successes[:],
self.consecutive_successes[:],
) = compute_hand_reward(
self.rew_buf,
self.reset_buf,
self.reset_goal_buf,
self.progress_buf,
self.successes,
self.consecutive_successes,
self.max_episode_length,
self.object_pos,
self.object_rot,
self.goal_pos,
self.goal_rot,
self.dist_reward_scale,
self.rot_reward_scale,
self.rot_eps,
self.actions,
self.action_penalty_scale,
self.success_tolerance,
self.reach_goal_bonus,
self.fall_dist,
self.fall_penalty,
self.max_consecutive_successes,
self.av_factor,
)
self.extras["consecutive_successes"] = self.consecutive_successes.mean()
self.randomization_buf += 1
if self.print_success_stat:
self.total_resets = self.total_resets + self.reset_buf.sum()
direct_average_successes = self.total_successes + self.successes.sum()
self.total_successes = self.total_successes + (self.successes * self.reset_buf).sum()
# The direct average shows the overall result more quickly, but slightly undershoots long term policy performance.
print(
"Direct average consecutive successes = {:.1f}".format(
direct_average_successes / (self.total_resets + self.num_envs)
)
)
if self.total_resets > 0:
print(
"Post-Reset average consecutive successes = {:.1f}".format(self.total_successes / self.total_resets)
)
def pre_physics_step(self, actions):
if not self.world.is_playing():
return
env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
goal_env_ids = self.reset_goal_buf.nonzero(as_tuple=False).squeeze(-1)
reset_buf = self.reset_buf.clone()
# if only goals need reset, then call set API
if len(goal_env_ids) > 0 and len(env_ids) == 0:
self.reset_target_pose(goal_env_ids)
elif len(goal_env_ids) > 0:
self.reset_target_pose(goal_env_ids)
if len(env_ids) > 0:
self.reset_idx(env_ids)
self.actions = actions.clone().to(self.device)
if self.use_relative_control:
targets = (
self.prev_targets[:, self.actuated_dof_indices] + self.hand_dof_speed_scale * self.dt * self.actions
)
self.cur_targets[:, self.actuated_dof_indices] = tensor_clamp(
targets,
self.hand_dof_lower_limits[self.actuated_dof_indices],
self.hand_dof_upper_limits[self.actuated_dof_indices],
)
else:
self.cur_targets[:, self.actuated_dof_indices] = scale(
self.actions,
self.hand_dof_lower_limits[self.actuated_dof_indices],
self.hand_dof_upper_limits[self.actuated_dof_indices],
)
self.cur_targets[:, self.actuated_dof_indices] = (
self.act_moving_average * self.cur_targets[:, self.actuated_dof_indices]
+ (1.0 - self.act_moving_average) * self.prev_targets[:, self.actuated_dof_indices]
)
self.cur_targets[:, self.actuated_dof_indices] = tensor_clamp(
self.cur_targets[:, self.actuated_dof_indices],
self.hand_dof_lower_limits[self.actuated_dof_indices],
self.hand_dof_upper_limits[self.actuated_dof_indices],
)
self.prev_targets[:, self.actuated_dof_indices] = self.cur_targets[:, self.actuated_dof_indices]
self._hands.set_joint_position_targets(
self.cur_targets[:, self.actuated_dof_indices], indices=None, joint_indices=self.actuated_dof_indices
)
if self._dr_randomizer.randomize:
rand_envs = torch.where(
self.randomization_buf >= self._dr_randomizer.min_frequency,
torch.ones_like(self.randomization_buf),
torch.zeros_like(self.randomization_buf),
)
rand_env_ids = torch.nonzero(torch.logical_and(rand_envs, reset_buf))
self.dr.physics_view.step_randomization(rand_env_ids)
self.randomization_buf[rand_env_ids] = 0
def is_done(self):
pass
def reset_target_pose(self, env_ids):
# reset goal
indices = env_ids.to(dtype=torch.int32)
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), 4), device=self.device)
new_rot = randomize_rotation(
rand_floats[:, 0], rand_floats[:, 1], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids]
)
self.goal_pos[env_ids] = self.goal_init_pos[env_ids, 0:3]
self.goal_rot[env_ids] = new_rot
goal_pos, goal_rot = self.goal_pos.clone(), self.goal_rot.clone()
goal_pos[env_ids] = (
self.goal_pos[env_ids] + self.goal_displacement_tensor + self._env_pos[env_ids]
) # add world env pos
self._goals.set_world_poses(goal_pos[env_ids], goal_rot[env_ids], indices)
self.reset_goal_buf[env_ids] = 0
def reset_idx(self, env_ids):
indices = env_ids.to(dtype=torch.int32)
rand_floats = torch_rand_float(-1.0, 1.0, (len(env_ids), self.num_hand_dofs * 2 + 5), device=self.device)
self.reset_target_pose(env_ids)
# reset object
new_object_pos = (
self.object_init_pos[env_ids] + self.reset_position_noise * rand_floats[:, 0:3] + self._env_pos[env_ids]
) # add world env pos
new_object_rot = randomize_rotation(
rand_floats[:, 3], rand_floats[:, 4], self.x_unit_tensor[env_ids], self.y_unit_tensor[env_ids]
)
object_velocities = torch.zeros_like(self.object_init_velocities, dtype=torch.float, device=self.device)
self._objects.set_velocities(object_velocities[env_ids], indices)
self._objects.set_world_poses(new_object_pos, new_object_rot, indices)
# reset hand
delta_max = self.hand_dof_upper_limits - self.hand_dof_default_pos
delta_min = self.hand_dof_lower_limits - self.hand_dof_default_pos
rand_delta = delta_min + (delta_max - delta_min) * 0.5 * (rand_floats[:, 5 : 5 + self.num_hand_dofs] + 1.0)
pos = self.hand_dof_default_pos + self.reset_dof_pos_noise * rand_delta
dof_pos = torch.zeros((self.num_envs, self.num_hand_dofs), device=self.device)
dof_pos[env_ids, :] = pos
dof_vel = torch.zeros((self.num_envs, self.num_hand_dofs), device=self.device)
dof_vel[env_ids, :] = (
self.hand_dof_default_vel
+ self.reset_dof_vel_noise * rand_floats[:, 5 + self.num_hand_dofs : 5 + self.num_hand_dofs * 2]
)
self.prev_targets[env_ids, : self.num_hand_dofs] = pos
self.cur_targets[env_ids, : self.num_hand_dofs] = pos
self.hand_dof_targets[env_ids, :] = pos
self._hands.set_joint_position_targets(self.hand_dof_targets[env_ids], indices)
self._hands.set_joint_positions(dof_pos[env_ids], indices)
self._hands.set_joint_velocities(dof_vel[env_ids], indices)
self.progress_buf[env_ids] = 0
self.reset_buf[env_ids] = 0
self.successes[env_ids] = 0
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def randomize_rotation(rand0, rand1, x_unit_tensor, y_unit_tensor):
return quat_mul(
quat_from_angle_axis(rand0 * np.pi, x_unit_tensor), quat_from_angle_axis(rand1 * np.pi, y_unit_tensor)
)
@torch.jit.script
def compute_hand_reward(
rew_buf,
reset_buf,
reset_goal_buf,
progress_buf,
successes,
consecutive_successes,
max_episode_length: float,
object_pos,
object_rot,
target_pos,
target_rot,
dist_reward_scale: float,
rot_reward_scale: float,
rot_eps: float,
actions,
action_penalty_scale: float,
success_tolerance: float,
reach_goal_bonus: float,
fall_dist: float,
fall_penalty: float,
max_consecutive_successes: int,
av_factor: float,
):
goal_dist = torch.norm(object_pos - target_pos, p=2, dim=-1)
# Orientation alignment for the cube in hand and goal cube
quat_diff = quat_mul(object_rot, quat_conjugate(target_rot))
rot_dist = 2.0 * torch.asin(
torch.clamp(torch.norm(quat_diff[:, 1:4], p=2, dim=-1), max=1.0)
) # changed quat convention
dist_rew = goal_dist * dist_reward_scale
rot_rew = 1.0 / (torch.abs(rot_dist) + rot_eps) * rot_reward_scale
action_penalty = torch.sum(actions**2, dim=-1)
# Total reward is: position distance + orientation alignment + action regularization + success bonus + fall penalty
reward = dist_rew + rot_rew + action_penalty * action_penalty_scale
# Find out which envs hit the goal and update successes count
goal_resets = torch.where(torch.abs(rot_dist) <= success_tolerance, torch.ones_like(reset_goal_buf), reset_goal_buf)
successes = successes + goal_resets
# Success bonus: orientation is within `success_tolerance` of goal orientation
reward = torch.where(goal_resets == 1, reward + reach_goal_bonus, reward)
# Fall penalty: distance to the goal is larger than a threashold
reward = torch.where(goal_dist >= fall_dist, reward + fall_penalty, reward)
# Check env termination conditions, including maximum success number
resets = torch.where(goal_dist >= fall_dist, torch.ones_like(reset_buf), reset_buf)
if max_consecutive_successes > 0:
# Reset progress buffer on goal envs if max_consecutive_successes > 0
progress_buf = torch.where(
torch.abs(rot_dist) <= success_tolerance, torch.zeros_like(progress_buf), progress_buf
)
resets = torch.where(successes >= max_consecutive_successes, torch.ones_like(resets), resets)
resets = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(resets), resets)
# Apply penalty for not reaching the goal
if max_consecutive_successes > 0:
reward = torch.where(progress_buf >= max_episode_length - 1, reward + 0.5 * fall_penalty, reward)
num_resets = torch.sum(resets)
finished_cons_successes = torch.sum(successes * resets.float())
cons_successes = torch.where(
num_resets > 0,
av_factor * finished_cons_successes / num_resets + (1.0 - av_factor) * consecutive_successes,
consecutive_successes,
)
return reward, resets, goal_resets, progress_buf, successes, cons_successes
| 23,466 | Python | 43.110902 | 126 | 0.630657 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/tasks/shared/locomotion.py | # Copyright (c) 2018-2022, NVIDIA Corporation
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its
# contributors may be used to endorse or promote products derived from
# this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
import math
from abc import abstractmethod
import numpy as np
import torch
from omni.isaac.core.articulations import ArticulationView
from omni.isaac.core.utils.prims import get_prim_at_path
from omni.isaac.core.utils.torch.maths import tensor_clamp, torch_rand_float, unscale
from omni.isaac.core.utils.torch.rotations import compute_heading_and_up, compute_rot, quat_conjugate
from omniisaacgymenvs.tasks.base.rl_task import RLTask
class LocomotionTask(RLTask):
def __init__(self, name, env, offset=None) -> None:
LocomotionTask.update_config(self)
RLTask.__init__(self, name, env)
return
def update_config(self):
self._num_envs = self._task_cfg["env"]["numEnvs"]
self._env_spacing = self._task_cfg["env"]["envSpacing"]
self._max_episode_length = self._task_cfg["env"]["episodeLength"]
self.dof_vel_scale = self._task_cfg["env"]["dofVelocityScale"]
self.angular_velocity_scale = self._task_cfg["env"]["angularVelocityScale"]
self.contact_force_scale = self._task_cfg["env"]["contactForceScale"]
self.power_scale = self._task_cfg["env"]["powerScale"]
self.heading_weight = self._task_cfg["env"]["headingWeight"]
self.up_weight = self._task_cfg["env"]["upWeight"]
self.actions_cost_scale = self._task_cfg["env"]["actionsCost"]
self.energy_cost_scale = self._task_cfg["env"]["energyCost"]
self.joints_at_limit_cost_scale = self._task_cfg["env"]["jointsAtLimitCost"]
self.death_cost = self._task_cfg["env"]["deathCost"]
self.termination_height = self._task_cfg["env"]["terminationHeight"]
self.alive_reward_scale = self._task_cfg["env"]["alive_reward_scale"]
@abstractmethod
def set_up_scene(self, scene) -> None:
pass
@abstractmethod
def get_robot(self):
pass
def get_observations(self) -> dict:
torso_position, torso_rotation = self._robots.get_world_poses(clone=False)
velocities = self._robots.get_velocities(clone=False)
velocity = velocities[:, 0:3]
ang_velocity = velocities[:, 3:6]
dof_pos = self._robots.get_joint_positions(clone=False)
dof_vel = self._robots.get_joint_velocities(clone=False)
# force sensors attached to the feet
sensor_force_torques = self._robots.get_measured_joint_forces(joint_indices=self._sensor_indices)
(
self.obs_buf[:],
self.potentials[:],
self.prev_potentials[:],
self.up_vec[:],
self.heading_vec[:],
) = get_observations(
torso_position,
torso_rotation,
velocity,
ang_velocity,
dof_pos,
dof_vel,
self.targets,
self.potentials,
self.dt,
self.inv_start_rot,
self.basis_vec0,
self.basis_vec1,
self.dof_limits_lower,
self.dof_limits_upper,
self.dof_vel_scale,
sensor_force_torques,
self._num_envs,
self.contact_force_scale,
self.actions,
self.angular_velocity_scale,
)
observations = {self._robots.name: {"obs_buf": self.obs_buf}}
return observations
def pre_physics_step(self, actions) -> None:
if not self.world.is_playing():
return
reset_env_ids = self.reset_buf.nonzero(as_tuple=False).squeeze(-1)
if len(reset_env_ids) > 0:
self.reset_idx(reset_env_ids)
self.actions = actions.clone().to(self._device)
forces = self.actions * self.joint_gears * self.power_scale
indices = torch.arange(self._robots.count, dtype=torch.int32, device=self._device)
# applies joint torques
self._robots.set_joint_efforts(forces, indices=indices)
def reset_idx(self, env_ids):
num_resets = len(env_ids)
# randomize DOF positions and velocities
dof_pos = torch_rand_float(-0.2, 0.2, (num_resets, self._robots.num_dof), device=self._device)
dof_pos[:] = tensor_clamp(self.initial_dof_pos[env_ids] + dof_pos, self.dof_limits_lower, self.dof_limits_upper)
dof_vel = torch_rand_float(-0.1, 0.1, (num_resets, self._robots.num_dof), device=self._device)
root_pos, root_rot = self.initial_root_pos[env_ids], self.initial_root_rot[env_ids]
root_vel = torch.zeros((num_resets, 6), device=self._device)
# apply resets
self._robots.set_joint_positions(dof_pos, indices=env_ids)
self._robots.set_joint_velocities(dof_vel, indices=env_ids)
self._robots.set_world_poses(root_pos, root_rot, indices=env_ids)
self._robots.set_velocities(root_vel, indices=env_ids)
to_target = self.targets[env_ids] - self.initial_root_pos[env_ids]
to_target[:, 2] = 0.0
self.prev_potentials[env_ids] = -torch.norm(to_target, p=2, dim=-1) / self.dt
self.potentials[env_ids] = self.prev_potentials[env_ids].clone()
# bookkeeping
self.reset_buf[env_ids] = 0
self.progress_buf[env_ids] = 0
num_resets = len(env_ids)
def post_reset(self):
self._robots = self.get_robot()
self.initial_root_pos, self.initial_root_rot = self._robots.get_world_poses()
self.initial_dof_pos = self._robots.get_joint_positions()
# initialize some data used later on
self.start_rotation = torch.tensor([1, 0, 0, 0], device=self._device, dtype=torch.float32)
self.up_vec = torch.tensor([0, 0, 1], dtype=torch.float32, device=self._device).repeat((self.num_envs, 1))
self.heading_vec = torch.tensor([1, 0, 0], dtype=torch.float32, device=self._device).repeat((self.num_envs, 1))
self.inv_start_rot = quat_conjugate(self.start_rotation).repeat((self.num_envs, 1))
self.basis_vec0 = self.heading_vec.clone()
self.basis_vec1 = self.up_vec.clone()
self.targets = torch.tensor([1000, 0, 0], dtype=torch.float32, device=self._device).repeat((self.num_envs, 1))
self.target_dirs = torch.tensor([1, 0, 0], dtype=torch.float32, device=self._device).repeat((self.num_envs, 1))
self.dt = 1.0 / 60.0
self.potentials = torch.tensor([-1000.0 / self.dt], dtype=torch.float32, device=self._device).repeat(
self.num_envs
)
self.prev_potentials = self.potentials.clone()
self.actions = torch.zeros((self.num_envs, self.num_actions), device=self._device)
# randomize all envs
indices = torch.arange(self._robots.count, dtype=torch.int64, device=self._device)
self.reset_idx(indices)
def calculate_metrics(self) -> None:
self.rew_buf[:] = calculate_metrics(
self.obs_buf,
self.actions,
self.up_weight,
self.heading_weight,
self.potentials,
self.prev_potentials,
self.actions_cost_scale,
self.energy_cost_scale,
self.termination_height,
self.death_cost,
self._robots.num_dof,
self.get_dof_at_limit_cost(),
self.alive_reward_scale,
self.motor_effort_ratio,
)
def is_done(self) -> None:
self.reset_buf[:] = is_done(
self.obs_buf, self.termination_height, self.reset_buf, self.progress_buf, self._max_episode_length
)
#####################################################################
###=========================jit functions=========================###
#####################################################################
@torch.jit.script
def normalize_angle(x):
return torch.atan2(torch.sin(x), torch.cos(x))
@torch.jit.script
def get_observations(
torso_position,
torso_rotation,
velocity,
ang_velocity,
dof_pos,
dof_vel,
targets,
potentials,
dt,
inv_start_rot,
basis_vec0,
basis_vec1,
dof_limits_lower,
dof_limits_upper,
dof_vel_scale,
sensor_force_torques,
num_envs,
contact_force_scale,
actions,
angular_velocity_scale,
):
# type: (Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, Tensor, float, Tensor, Tensor, Tensor, Tensor, Tensor, float, Tensor, int, float, Tensor, float) -> Tuple[Tensor, Tensor, Tensor, Tensor, Tensor]
to_target = targets - torso_position
to_target[:, 2] = 0.0
prev_potentials = potentials.clone()
potentials = -torch.norm(to_target, p=2, dim=-1) / dt
torso_quat, up_proj, heading_proj, up_vec, heading_vec = compute_heading_and_up(
torso_rotation, inv_start_rot, to_target, basis_vec0, basis_vec1, 2
)
vel_loc, angvel_loc, roll, pitch, yaw, angle_to_target = compute_rot(
torso_quat, velocity, ang_velocity, targets, torso_position
)
dof_pos_scaled = unscale(dof_pos, dof_limits_lower, dof_limits_upper)
# obs_buf shapes: 1, 3, 3, 1, 1, 1, 1, 1, num_dofs, num_dofs, num_sensors * 6, num_dofs
obs = torch.cat(
(
torso_position[:, 2].view(-1, 1),
vel_loc,
angvel_loc * angular_velocity_scale,
normalize_angle(yaw).unsqueeze(-1),
normalize_angle(roll).unsqueeze(-1),
normalize_angle(angle_to_target).unsqueeze(-1),
up_proj.unsqueeze(-1),
heading_proj.unsqueeze(-1),
dof_pos_scaled,
dof_vel * dof_vel_scale,
sensor_force_torques.reshape(num_envs, -1) * contact_force_scale,
actions,
),
dim=-1,
)
return obs, potentials, prev_potentials, up_vec, heading_vec
@torch.jit.script
def is_done(obs_buf, termination_height, reset_buf, progress_buf, max_episode_length):
# type: (Tensor, float, Tensor, Tensor, float) -> Tensor
reset = torch.where(obs_buf[:, 0] < termination_height, torch.ones_like(reset_buf), reset_buf)
reset = torch.where(progress_buf >= max_episode_length - 1, torch.ones_like(reset_buf), reset)
return reset
@torch.jit.script
def calculate_metrics(
obs_buf,
actions,
up_weight,
heading_weight,
potentials,
prev_potentials,
actions_cost_scale,
energy_cost_scale,
termination_height,
death_cost,
num_dof,
dof_at_limit_cost,
alive_reward_scale,
motor_effort_ratio,
):
# type: (Tensor, Tensor, float, float, Tensor, Tensor, float, float, float, float, int, Tensor, float, Tensor) -> Tensor
heading_weight_tensor = torch.ones_like(obs_buf[:, 11]) * heading_weight
heading_reward = torch.where(obs_buf[:, 11] > 0.8, heading_weight_tensor, heading_weight * obs_buf[:, 11] / 0.8)
# aligning up axis of robot and environment
up_reward = torch.zeros_like(heading_reward)
up_reward = torch.where(obs_buf[:, 10] > 0.93, up_reward + up_weight, up_reward)
# energy penalty for movement
actions_cost = torch.sum(actions**2, dim=-1)
electricity_cost = torch.sum(
torch.abs(actions * obs_buf[:, 12 + num_dof : 12 + num_dof * 2]) * motor_effort_ratio.unsqueeze(0), dim=-1
)
# reward for duration of staying alive
alive_reward = torch.ones_like(potentials) * alive_reward_scale
progress_reward = potentials - prev_potentials
total_reward = (
progress_reward
+ alive_reward
+ up_reward
+ heading_reward
- actions_cost_scale * actions_cost
- energy_cost_scale * electricity_cost
- dof_at_limit_cost
)
# adjust reward for fallen agents
total_reward = torch.where(
obs_buf[:, 0] < termination_height, torch.ones_like(total_reward) * death_cost, total_reward
)
return total_reward
| 13,243 | Python | 37.277457 | 214 | 0.628861 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/config.yaml |
# Task name - used to pick the class to load
task_name: ${task.name}
# experiment name. defaults to name of training config
experiment: ''
# if set to positive integer, overrides the default number of environments
num_envs: ''
# seed - set to -1 to choose random seed
seed: 42
# set to True for deterministic performance
torch_deterministic: False
# set the maximum number of learning iterations to train for. overrides default per-environment setting
max_iterations: ''
## Device config
physics_engine: 'physx'
# whether to use cpu or gpu pipeline
pipeline: 'gpu'
# whether to use cpu or gpu physx
sim_device: 'gpu'
# used for gpu simulation only - device id for running sim and task if pipeline=gpu
device_id: 0
# device to run RL
rl_device: 'cuda:0'
# multi-GPU training
multi_gpu: False
## PhysX arguments
num_threads: 4 # Number of worker threads used by PhysX - for CPU PhysX only.
solver_type: 1 # 0: pgs, 1: tgs
# RLGames Arguments
# test - if set, run policy in inference mode (requires setting checkpoint to load)
test: False
# used to set checkpoint path
checkpoint: ''
# evaluate checkpoint
evaluation: False
# disables rendering
headless: False
# enables native livestream
enable_livestream: False
# timeout for MT script
mt_timeout: 300
# enables viewport recording
enable_recording: False
# interval between video recordings (in steps)
recording_interval: 2000
# length of the recorded video (in steps)
recording_length: 100
# fps for writing recorded video
recording_fps: 30
# directory to save recordings in
recording_dir: ''
wandb_activate: False
wandb_group: ''
wandb_name: ${train.params.config.name}
wandb_entity: ''
wandb_project: 'omniisaacgymenvs'
# path to a kit app file
kit_app: ''
# Warp
warp: False
# set default task and default training config based on task
defaults:
- _self_
- task: Cartpole
- train: ${task}PPO
- override hydra/job_logging: disabled
# set the directory where the output files get saved
hydra:
output_subdir: null
run:
dir: .
| 2,007 | YAML | 22.348837 | 103 | 0.744893 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/CartpoleCamera.yaml | defaults:
- Cartpole
- _self_
name: CartpoleCamera
env:
numEnvs: ${resolve_default:32,${...num_envs}}
envSpacing: 20.0
cameraWidth: 240
cameraHeight: 160
exportImages: False
sim:
rendering_dt: 0.0166 # 1/60
# set to True if you use camera sensors in the environment
enable_cameras: True
add_ground_plane: False
add_distant_light: True
| 363 | YAML | 16.333333 | 60 | 0.69697 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/FrankaDeformable.yaml | # used to create the object
name: FrankaDeformable
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:1024,${...num_envs}} # 2048#4096
envSpacing: 3.0
episodeLength: 100 # 150 #350 #500
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
controlFrequencyInv: 2 # 60 Hz
startPositionNoise: 0.0
startRotationNoise: 0.0
numProps: 4
aggregateMode: 3
actionScale: 7.5
dofVelocityScale: 0.1
distRewardScale: 2.0
rotRewardScale: 0.5
aroundHandleRewardScale: 10.0
openRewardScale: 7.5
fingerDistRewardScale: 100.0
actionPenaltyScale: 0.01
fingerCloseRewardScale: 10.0
sim:
dt: 0.016 # 1/60s
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 8 # 12
solver_velocity_iteration_count: 0 # 1
contact_offset: 0.02 #0.005
rest_offset: 0.001
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 1000.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 33554432
gpu_found_lost_pairs_capacity: 524288 #20965884
gpu_found_lost_aggregate_pairs_capacity: 262144
gpu_total_aggregate_pairs_capacity: 1048576
gpu_max_soft_body_contacts: 4194304 #2097152 #16777216 #8388608 #2097152 #1048576
gpu_max_particle_contacts: 1048576 #2097152 #1048576
gpu_heap_capacity: 33554432
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
franka:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: True
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 12
solver_velocity_iteration_count: 1
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
beaker:
# -1 to use default values
override_usd_defaults: False
make_kinematic: True
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 12
solver_velocity_iteration_count: 1
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
cube:
# -1 to use default values
override_usd_defaults: False
make_kinematic: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 12
solver_velocity_iteration_count: 1
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
# # per-shape
# contact_offset: 0.02
# rest_offset: 0.001
| 3,418 | YAML | 25.92126 | 85 | 0.691925 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/FrankaCabinet.yaml | # used to create the object
name: FrankaCabinet
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 3.0
episodeLength: 500
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
controlFrequencyInv: 2 # 60 Hz
startPositionNoise: 0.0
startRotationNoise: 0.0
numProps: 4
aggregateMode: 3
actionScale: 7.5
dofVelocityScale: 0.1
distRewardScale: 2.0
rotRewardScale: 0.5
aroundHandleRewardScale: 10.0
openRewardScale: 7.5
fingerDistRewardScale: 100.0
actionPenaltyScale: 0.01
fingerCloseRewardScale: 10.0
sim:
dt: 0.0083 # 1/120 s
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 12
solver_velocity_iteration_count: 1
contact_offset: 0.005
rest_offset: 0.0
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 1000.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 33554432
gpu_found_lost_pairs_capacity: 524288
gpu_found_lost_aggregate_pairs_capacity: 262144
gpu_total_aggregate_pairs_capacity: 1048576
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 33554432
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
franka:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 12
solver_velocity_iteration_count: 1
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
cabinet:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 12
solver_velocity_iteration_count: 1
sleep_threshold: 0.0
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
prop:
# -1 to use default values
override_usd_defaults: False
make_kinematic: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 12
solver_velocity_iteration_count: 1
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: 100
max_depenetration_velocity: 1000.0
# per-shape
contact_offset: 0.005
rest_offset: 0.0
| 3,287 | YAML | 25.304 | 71 | 0.695467 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/Guarddog.yaml | # used to create the object
name: Guarddog
physics_engine: ${..physics_engine}
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 4. # [m]
clipObservations: 5.0
clipActions: 1.0
controlFrequencyInv: 2
baseInitState:
pos: [0.0, 0.0, 0.62] # x,y,z [m]
rot: [0.0, 0.0, 0.0, 1.0] # x,y,z,w [quat]
vLinear: [0.0, 0.0, 0.0] # x,y,z [m/s]
vAngular: [0.0, 0.0, 0.0] # x,y,z [rad/s]
randomCommandVelocityRanges:
linear_x: [-2., 2.] # min max [m/s]
linear_y: [-1.0, 1.0] # min max [m/s]
yaw: [-0.5, 0.5] # min max [rad/s]
control:
# PD Drive parameters:
stiffness: 85.0 # [N*m/rad]
damping: 2.0 # [N*m*s/rad]
actionScale: 13.5
defaultJointAngles: # = target angles when action = 0.0
FR_J1: 0.0 # [rad]
FL_J1: 0.0 # [rad]
BR_J1: 0.0 # [rad]
BL_J1: 0.0 # [rad]
FR_J2: 1.5 # [rad]
FL_J2: -1.5 # [rad]
BR_J2: -1.5 # [rad]
BL_J2: 1.5 # [rad]
FR_J3: -0.0 # [rad]
FL_J3: 0.0 # [rad]
BR_J3: 0.0 # [rad]
BL_J3: -0.0 # [rad]
learn:
# rewards
linearVelocityXYRewardScale: 1.0
angularVelocityZRewardScale: 0.5
linearVelocityZRewardScale: -0.03
jointAccRewardScale: -0.0003
actionRateRewardScale: -0.006
cosmeticRewardScale: -0.06
# normalization
linearVelocityScale: 2.0
angularVelocityScale: 0.25
dofPositionScale: 1.0
dofVelocityScale: 0.05
# episode length in seconds
episodeLength_s: 50
sim:
dt: 0.01
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 4
solver_velocity_iteration_count: 1
contact_offset: 0.02
rest_offset: 0.0
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 100.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 163840
gpu_found_lost_pairs_capacity: 4194304
gpu_found_lost_aggregate_pairs_capacity: 33554432
gpu_total_aggregate_pairs_capacity: 4194304
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 134217728
gpu_temp_buffer_capacity: 33554432
gpu_max_num_partitions: 8
Guarddog:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 4
solver_velocity_iteration_count: 1
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 100.0
| 3,260 | YAML | 24.880952 | 71 | 0.62638 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/Ant.yaml | # used to create the object
name: Ant
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
# numEnvs: ${...num_envs}
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 5
episodeLength: 1000
enableDebugVis: False
clipActions: 1.0
powerScale: 0.5
controlFrequencyInv: 2 # 60 Hz
# reward parameters
headingWeight: 0.5
upWeight: 0.1
# cost parameters
actionsCost: 0.005
energyCost: 0.05
dofVelocityScale: 0.2
angularVelocityScale: 1.0
contactForceScale: 0.1
jointsAtLimitCost: 0.1
deathCost: -2.0
terminationHeight: 0.31
alive_reward_scale: 0.5
sim:
dt: 0.0083 # 1/120 s
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
contact_offset: 0.02
rest_offset: 0.0
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 10.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 81920
gpu_found_lost_pairs_capacity: 8192
gpu_found_lost_aggregate_pairs_capacity: 262144
gpu_total_aggregate_pairs_capacity: 8192
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
Ant:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 10.0 | 2,370 | YAML | 24.771739 | 71 | 0.690717 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/AnymalTerrain.yaml | name: AnymalTerrain
physics_engine: ${..physics_engine}
env:
numEnvs: ${resolve_default:2048,${...num_envs}}
numObservations: 188
numActions: 12
envSpacing: 3. # [m]
terrain:
staticFriction: 1.0 # [-]
dynamicFriction: 1.0 # [-]
restitution: 0. # [-]
# rough terrain only:
curriculum: true
maxInitMapLevel: 0
mapLength: 8.
mapWidth: 8.
numLevels: 10
numTerrains: 20
# terrain types: [smooth slope, rough slope, stairs up, stairs down, discrete]
terrainProportions: [0.1, 0.1, 0.35, 0.25, 0.2]
# tri mesh only:
slopeTreshold: 0.5
baseInitState:
pos: [0.0, 0.0, 0.62] # x,y,z [m]
rot: [1.0, 0.0, 0.0, 0.0] # w,x,y,z [quat]
vLinear: [0.0, 0.0, 0.0] # x,y,z [m/s]
vAngular: [0.0, 0.0, 0.0] # x,y,z [rad/s]
randomCommandVelocityRanges:
# train
linear_x: [-1., 1.] # min max [m/s]
linear_y: [-1., 1.] # min max [m/s]
yaw: [-3.14, 3.14] # min max [rad/s]
control:
# PD Drive parameters:
stiffness: 80.0 # [N*m/rad]
damping: 2.0 # [N*m*s/rad]
# action scale: target angle = actionScale * action + defaultAngle
actionScale: 0.5
# decimation: Number of control action updates @ sim DT per policy DT
decimation: 4
defaultJointAngles: # = target angles when action = 0.0
LF_HAA: 0.03 # [rad]
LH_HAA: 0.03 # [rad]
RF_HAA: -0.03 # [rad]
RH_HAA: -0.03 # [rad]
LF_HFE: 0.4 # [rad]
LH_HFE: -0.4 # [rad]
RF_HFE: 0.4 # [rad]
RH_HFE: -0.4 # [rad]
LF_KFE: -0.8 # [rad]
LH_KFE: 0.8 # [rad]
RF_KFE: -0.8 # [rad]
RH_KFE: 0.8 # [rad]
learn:
# rewards
terminalReward: 0.0
linearVelocityXYRewardScale: 1.0
linearVelocityZRewardScale: -4.0
angularVelocityXYRewardScale: -0.05
angularVelocityZRewardScale: 0.5
orientationRewardScale: -0.
torqueRewardScale: -0.00002
jointAccRewardScale: -0.0005
baseHeightRewardScale: -0.0
actionRateRewardScale: -0.01
fallenOverRewardScale: -1.0
# cosmetics
hipRewardScale: -0. #25
# normalization
linearVelocityScale: 2.0
angularVelocityScale: 0.25
dofPositionScale: 1.0
dofVelocityScale: 0.05
heightMeasurementScale: 5.0
# noise
addNoise: true
noiseLevel: 1.0 # scales other values
dofPositionNoise: 0.01
dofVelocityNoise: 1.5
linearVelocityNoise: 0.1
angularVelocityNoise: 0.2
gravityNoise: 0.05
heightMeasurementNoise: 0.06
#randomization
pushInterval_s: 15
# episode length in seconds
episodeLength_s: 20
sim:
dt: 0.005
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: False
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: True
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
contact_offset: 0.02
rest_offset: 0.0
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 100.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 163840
gpu_found_lost_pairs_capacity: 4194304
gpu_found_lost_aggregate_pairs_capacity: 33554432
gpu_total_aggregate_pairs_capacity: 4194304
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 134217728
gpu_temp_buffer_capacity: 33554432
gpu_max_num_partitions: 8
anymal:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: True
enable_gyroscopic_forces: False
# also in stage params
# per-actor
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 100.0
| 4,346 | YAML | 25.345454 | 82 | 0.633916 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/BallBalance.yaml | # used to create the object
name: BallBalance
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 2.0
maxEpisodeLength: 600
actionSpeedScale: 20
clipObservations: 5.0
clipActions: 1.0
sim:
dt: 0.01
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 8
solver_velocity_iteration_count: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 1000.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 81920
gpu_found_lost_pairs_capacity: 262144
gpu_found_lost_aggregate_pairs_capacity: 262144
gpu_total_aggregate_pairs_capacity: 262144
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
table:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: True
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 8
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
ball:
# -1 to use default values
override_usd_defaults: False
make_kinematic: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 8
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: 200
max_depenetration_velocity: 1000.0
| 2,458 | YAML | 25.728261 | 71 | 0.690806 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/FactoryBase.yaml | # See schema in factory_schema_config_base.py for descriptions of parameters.
defaults:
- _self_
- /factory_schema_config_base
sim:
add_damping: True
disable_contact_processing: False
env:
env_spacing: 1.5
franka_depth: 0.5
table_height: 0.4
franka_friction: 1.0
table_friction: 0.3
| 309 | YAML | 16.222221 | 77 | 0.699029 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/Humanoid.yaml | # used to create the object
name: Humanoid
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
# numEnvs: ${...num_envs}
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 5
episodeLength: 1000
enableDebugVis: False
clipActions: 1.0
powerScale: 1.0
controlFrequencyInv: 2 # 60 Hz
# reward parameters
headingWeight: 0.5
upWeight: 0.1
# cost parameters
actionsCost: 0.01
energyCost: 0.05
dofVelocityScale: 0.1
angularVelocityScale: 0.25
contactForceScale: 0.01
jointsAtLimitCost: 0.25
deathCost: -1.0
terminationHeight: 0.8
alive_reward_scale: 2.0
sim:
dt: 0.0083 # 1/120 s
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 10.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 81920
gpu_found_lost_pairs_capacity: 8192
gpu_found_lost_aggregate_pairs_capacity: 262144
gpu_total_aggregate_pairs_capacity: 8192
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
Humanoid:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: True
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 10.0
| 2,335 | YAML | 24.670329 | 71 | 0.693362 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/AllegroHand.yaml | # used to create the object
name: AllegroHand
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:8192,${...num_envs}}
envSpacing: 0.75
episodeLength: 600
clipObservations: 5.0
clipActions: 1.0
useRelativeControl: False
dofSpeedScale: 20.0
actionsMovingAverage: 1.0
controlFrequencyInv: 4 # 30 Hz
startPositionNoise: 0.01
startRotationNoise: 0.0
resetPositionNoise: 0.01
resetRotationNoise: 0.0
resetDofPosRandomInterval: 0.2
resetDofVelRandomInterval: 0.0
# reward -> dictionary
distRewardScale: -10.0
rotRewardScale: 1.0
rotEps: 0.1
actionPenaltyScale: -0.0002
reachGoalBonus: 250
fallDistance: 0.24
fallPenalty: 0.0
velObsScale: 0.2
objectType: "block"
observationType: "full" # can be "full_no_vel", "full"
successTolerance: 0.1
printNumSuccesses: False
maxConsecutiveSuccesses: 0
sim:
dt: 0.0083 # 1/120 s
add_ground_plane: True
add_distant_light: False
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
# per-scene
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type} # 0: PGS, 1: TGS
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 33554432
gpu_found_lost_pairs_capacity: 819200
gpu_found_lost_aggregate_pairs_capacity: 819200
gpu_total_aggregate_pairs_capacity: 1048576
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 33554432
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
allegro_hand:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: True
enable_gyroscopic_forces: False
# also in stage params
# per-actor
solver_position_iteration_count: 8
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.0005
# per-body
density: -1
max_depenetration_velocity: 1000.0
object:
# -1 to use default values
override_usd_defaults: False
make_kinematic: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 8
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.0025
# per-body
density: 400.0
max_depenetration_velocity: 1000.0
goal_object:
# -1 to use default values
override_usd_defaults: False
make_kinematic: True
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 8
solver_velocity_iteration_count: 0
sleep_threshold: 0.000
stabilization_threshold: 0.0025
# per-body
density: -1
max_depenetration_velocity: 1000.0
| 3,360 | YAML | 25.464567 | 71 | 0.69881 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/HumanoidSAC.yaml | # used to create the object
defaults:
- Humanoid
- _self_
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:64,${...num_envs}} | 168 | YAML | 20.124997 | 52 | 0.678571 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/Ingenuity.yaml | # used to create the object
name: Ingenuity
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 2.5
maxEpisodeLength: 2000
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
sim:
dt: 0.01
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -3.721]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
# set to True if you use camera sensors in the environment
enable_cameras: False
disable_contact_processing: False
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 6
solver_velocity_iteration_count: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 1000.0
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: False
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 81920
gpu_found_lost_pairs_capacity: 4194304
gpu_found_lost_aggregate_pairs_capacity: 33554432
gpu_total_aggregate_pairs_capacity: 4194304
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
ingenuity:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: True
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 6
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
ball:
# -1 to use default values
override_usd_defaults: False
make_kinematic: True
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 6
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0 | 2,351 | YAML | 27 | 71 | 0.693322 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/Quadcopter.yaml | # used to create the object
name: Quadcopter
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 1.25
maxEpisodeLength: 500
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
sim:
dt: 0.01
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 1000.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 81920
gpu_found_lost_pairs_capacity: 8192
gpu_found_lost_aggregate_pairs_capacity: 262144
gpu_total_aggregate_pairs_capacity: 8192
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
copter:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: True
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
ball:
# -1 to use default values
override_usd_defaults: False
make_kinematic: True
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 6
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
| 2,452 | YAML | 25.663043 | 71 | 0.690457 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/Crazyflie.yaml | # used to create the object
name: Crazyflie
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 2.5
maxEpisodeLength: 700
enableDebugVis: False
clipObservations: 5.0
clipActions: 1.0
sim:
dt: 0.01
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
# set to True if you use camera sensors in the environment
enable_cameras: False
disable_contact_processing: False
physx:
num_threads: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 6
solver_velocity_iteration_count: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
max_depenetration_velocity: 1000.0
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: False
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 81920
gpu_found_lost_pairs_capacity: 4194304
gpu_found_lost_aggregate_pairs_capacity: 33554432
gpu_total_aggregate_pairs_capacity: 4194304
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
crazyflie:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: True
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 6
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
ball:
# -1 to use default values
override_usd_defaults: False
make_kinematic: True
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 6
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 1000.0
| 2,350 | YAML | 26.658823 | 71 | 0.692766 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/FactoryEnvNutBolt.yaml | # See schema in factory_schema_config_env.py for descriptions of common parameters.
defaults:
- _self_
- /factory_schema_config_env
sim:
disable_franka_collisions: False
disable_nut_collisions: False
disable_bolt_collisions: False
disable_contact_processing: False
env:
env_name: 'FactoryEnvNutBolt'
desired_subassemblies: ['nut_bolt_m16', 'nut_bolt_m16']
nut_lateral_offset: 0.1 # Y-axis offset of nut before initial reset to prevent initial interpenetration with bolt
nut_bolt_density: 7850.0
nut_bolt_friction: 0.3
# Subassembly options:
# {nut_bolt_m4, nut_bolt_m8, nut_bolt_m12, nut_bolt_m16, nut_bolt_m20}
| 643 | YAML | 28.272726 | 116 | 0.73717 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/AntSAC.yaml | # used to create the object
defaults:
- Ant
- _self_
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:64,${...num_envs}} | 163 | YAML | 19.499998 | 52 | 0.668712 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/Cartpole.yaml | # used to create the object
name: Cartpole
physics_engine: ${..physics_engine}
# if given, will override the device setting in gym.
env:
numEnvs: ${resolve_default:512,${...num_envs}}
envSpacing: 4.0
resetDist: 3.0
maxEffort: 400.0
clipObservations: 5.0
clipActions: 1.0
controlFrequencyInv: 2 # 60 Hz
sim:
dt: 0.0083 # 1/120 s
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
contact_offset: 0.02
rest_offset: 0.001
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 100.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 81920
gpu_found_lost_pairs_capacity: 1024
gpu_found_lost_aggregate_pairs_capacity: 262144
gpu_total_aggregate_pairs_capacity: 1024
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 67108864
gpu_temp_buffer_capacity: 16777216
gpu_max_num_partitions: 8
Cartpole:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 4
solver_velocity_iteration_count: 0
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 100.0
# per-shape
contact_offset: 0.02
rest_offset: 0.001 | 2,124 | YAML | 26.243589 | 71 | 0.686911 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/Anymal.yaml | # used to create the object
name: Anymal
physics_engine: ${..physics_engine}
env:
numEnvs: ${resolve_default:4096,${...num_envs}}
envSpacing: 4. # [m]
clipObservations: 5.0
clipActions: 1.0
controlFrequencyInv: 2
baseInitState:
pos: [0.0, 0.0, 0.62] # x,y,z [m]
rot: [0.0, 0.0, 0.0, 1.0] # x,y,z,w [quat]
vLinear: [0.0, 0.0, 0.0] # x,y,z [m/s]
vAngular: [0.0, 0.0, 0.0] # x,y,z [rad/s]
randomCommandVelocityRanges:
linear_x: [-2., 2.] # min max [m/s]
linear_y: [-1., 1.] # min max [m/s]
yaw: [-1., 1.] # min max [rad/s]
control:
# PD Drive parameters:
stiffness: 85.0 # [N*m/rad]
damping: 2.0 # [N*m*s/rad]
actionScale: 13.5
defaultJointAngles: # = target angles when action = 0.0
LF_HAA: 0.03 # [rad]
LH_HAA: 0.03 # [rad]
RF_HAA: -0.03 # [rad]
RH_HAA: -0.03 # [rad]
LF_HFE: 0.4 # [rad]
LH_HFE: -0.4 # [rad]
RF_HFE: 0.4 # [rad]
RH_HFE: -0.4 # [rad]
LF_KFE: -0.8 # [rad]
LH_KFE: 0.8 # [rad]
RF_KFE: -0.8 # [rad]
RH_KFE: 0.8 # [rad]
learn:
# rewards
linearVelocityXYRewardScale: 1.0
angularVelocityZRewardScale: 0.5
linearVelocityZRewardScale: -0.03
jointAccRewardScale: -0.0003
actionRateRewardScale: -0.006
cosmeticRewardScale: -0.06
# normalization
linearVelocityScale: 2.0
angularVelocityScale: 0.25
dofPositionScale: 1.0
dofVelocityScale: 0.05
# episode length in seconds
episodeLength_s: 50
sim:
dt: 0.01
use_gpu_pipeline: ${eq:${...pipeline},"gpu"}
gravity: [0.0, 0.0, -9.81]
add_ground_plane: True
add_distant_light: False
use_fabric: True
enable_scene_query_support: False
disable_contact_processing: False
# set to True if you use camera sensors in the environment
enable_cameras: False
default_physics_material:
static_friction: 1.0
dynamic_friction: 1.0
restitution: 0.0
physx:
worker_thread_count: ${....num_threads}
solver_type: ${....solver_type}
use_gpu: ${eq:${....sim_device},"gpu"} # set to False to run on CPU
solver_position_iteration_count: 4
solver_velocity_iteration_count: 1
contact_offset: 0.02
rest_offset: 0.0
bounce_threshold_velocity: 0.2
friction_offset_threshold: 0.04
friction_correlation_distance: 0.025
enable_sleeping: True
enable_stabilization: True
max_depenetration_velocity: 100.0
# GPU buffers
gpu_max_rigid_contact_count: 524288
gpu_max_rigid_patch_count: 163840
gpu_found_lost_pairs_capacity: 4194304
gpu_found_lost_aggregate_pairs_capacity: 33554432
gpu_total_aggregate_pairs_capacity: 4194304
gpu_max_soft_body_contacts: 1048576
gpu_max_particle_contacts: 1048576
gpu_heap_capacity: 134217728
gpu_temp_buffer_capacity: 33554432
gpu_max_num_partitions: 8
Anymal:
# -1 to use default values
override_usd_defaults: False
enable_self_collisions: False
enable_gyroscopic_forces: True
# also in stage params
# per-actor
solver_position_iteration_count: 4
solver_velocity_iteration_count: 1
sleep_threshold: 0.005
stabilization_threshold: 0.001
# per-body
density: -1
max_depenetration_velocity: 100.0
| 3,270 | YAML | 24.960317 | 71 | 0.626911 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/task/ShadowHandOpenAI_LSTM.yaml | # specifies what the config is when running `ShadowHandOpenAI` in LSTM mode
defaults:
- ShadowHandOpenAI_FF
- _self_
env:
numEnvs: ${resolve_default:8192,${...num_envs}}
| 178 | YAML | 18.888887 | 75 | 0.707865 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/ShadowHandOpenAI_FFPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [400, 400, 200, 100]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:ShadowHandOpenAI_FF,${....experiment}}
full_experiment_name: ${.name}
device: ${....rl_device}
device_name: ${....rl_device}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.998
tau: 0.95
learning_rate: 5e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
max_epochs: ${resolve_default:10000,${....max_iterations}}
save_best_after: 100
save_frequency: 200
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 4
critic_coef: 4
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
central_value_config:
minibatch_size: 32864
mini_epochs: 4
learning_rate: 5e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
network:
name: actor_critic
central_value: True
mlp:
units: [512, 512, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
player:
deterministic: True
games_num: 100000
print_stats: True
| 2,215 | YAML | 20.940594 | 66 | 0.577427 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/AnymalTerrainPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: True
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0. # std = 1.
fixed_sigma: True
mlp:
units: [512, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
# rnn:
# name: lstm
# units: 128
# layers: 1
# before_mlp: True
# concat_input: True
# layer_norm: False
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:AnymalTerrain,${....experiment}}
full_experiment_name: ${.name}
device: ${....rl_device}
device_name: ${....rl_device}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False # True
normalize_input: True
normalize_value: True
normalize_advantage: True
value_bootstrap: True
clip_actions: False
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
gamma: 0.99
tau: 0.95
e_clip: 0.2
entropy_coef: 0.001
learning_rate: 3.e-4 # overwritten by adaptive lr_schedule
lr_schedule: adaptive
kl_threshold: 0.008 # target kl for adaptive lr
truncate_grads: True
grad_norm: 1.
horizon_length: 48
minibatch_size: 16384
mini_epochs: 5
critic_coef: 2
clip_value: True
seq_length: 4 # only for rnn
bounds_loss_coef: 0.
max_epochs: ${resolve_default:2000,${....max_iterations}}
save_best_after: 100
score_to_win: 20000
save_frequency: 50
print_stats: True
| 1,928 | YAML | 21.694117 | 101 | 0.592842 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/HumanoidPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [400, 200, 100]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:Humanoid,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
device: ${....rl_device}
device_name: ${....rl_device}
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 5e-4
lr_schedule: adaptive
kl_threshold: 0.008
score_to_win: 20000
max_epochs: ${resolve_default:1000,${....max_iterations}}
save_best_after: 100
save_frequency: 100
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 32
minibatch_size: 32768
mini_epochs: 5
critic_coef: 4
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
| 1,639 | YAML | 21.465753 | 101 | 0.594875 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/CrazyfliePPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 256, 128]
activation: tanh
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:Crazyflie,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
device: ${....rl_device}
device_name: ${....rl_device}
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1e-4
lr_schedule: adaptive
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:1000,${....max_iterations}}
save_best_after: 50
save_frequency: 50
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
| 1,614 | YAML | 21.430555 | 101 | 0.593556 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/ShadowHandPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [512, 512, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:ShadowHand,${....experiment}}
full_experiment_name: ${.name}
device: ${....rl_device}
device_name: ${....rl_device}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 5e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
max_epochs: ${resolve_default:10000,${....max_iterations}}
save_best_after: 100
save_frequency: 200
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 32768
mini_epochs: 5
critic_coef: 4
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
player:
deterministic: True
games_num: 100000
print_stats: True
| 1,703 | YAML | 20.56962 | 62 | 0.589548 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/GuarddogPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0. # std = 1.
fixed_sigma: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:Guarddog,${....experiment}}
full_experiment_name: ${.name}
device: ${....rl_device}
device_name: ${....rl_device}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.99
tau: 0.95
e_clip: 0.2
entropy_coef: 0.0
learning_rate: 3.e-4 # overwritten by adaptive lr_schedule
lr_schedule: adaptive
kl_threshold: 0.008 # target kl for adaptive lr
truncate_grads: True
grad_norm: 1.
horizon_length: 24
minibatch_size: 32768
mini_epochs: 5
critic_coef: 2
clip_value: True
seq_length: 4 # only for rnn
bounds_loss_coef: 0.001
max_epochs: ${resolve_default:1000,${....max_iterations}}
save_best_after: 200
score_to_win: 20000
save_frequency: 50
print_stats: True
| 1,746 | YAML | 21.986842 | 101 | 0.601375 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/HumanoidSAC.yaml | params:
seed: ${...seed}
algo:
name: sac
model:
name: soft_actor_critic
network:
name: soft_actor_critic
separate: True
space:
continuous:
mlp:
units: [512, 256]
activation: relu
initializer:
name: default
log_std_bounds: [-5, 2]
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:HumanoidSAC,${....experiment}}
env_name: rlgpu
device: ${....rl_device}
device_name: ${....rl_device}
multi_gpu: ${....multi_gpu}
normalize_input: True
reward_shaper:
scale_value: 1.0
max_epochs: ${resolve_default:50000,${....max_iterations}}
num_steps_per_episode: 8
save_best_after: 100
save_frequency: 1000
gamma: 0.99
init_alpha: 1.0
alpha_lr: 0.005
actor_lr: 0.0005
critic_lr: 0.0005
critic_tau: 0.005
batch_size: 4096
learnable_temperature: true
num_seed_steps: 5
num_warmup_steps: 10
replay_buffer_size: 1000000
num_actors: ${....task.env.numEnvs}
| 1,165 | YAML | 21.423077 | 101 | 0.603433 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/ShadowHandOpenAI_LSTMPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [512]
activation: relu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 1024
layers: 1
before_mlp: True
layer_norm: True
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:ShadowHandOpenAI_LSTM,${....experiment}}
full_experiment_name: ${.name}
device: ${....rl_device}
device_name: ${....rl_device}
env_name: rlgpu
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.998
tau: 0.95
learning_rate: 1e-4
lr_schedule: adaptive
schedule_type: standard
kl_threshold: 0.016
score_to_win: 100000
max_epochs: ${resolve_default:10000,${....max_iterations}}
save_best_after: 100
save_frequency: 200
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 4
critic_coef: 4
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
central_value_config:
minibatch_size: 32768
mini_epochs: 4
learning_rate: 1e-4
kl_threshold: 0.016
clip_value: True
normalize_input: True
truncate_grads: True
network:
name: actor_critic
central_value: True
mlp:
units: [512]
activation: relu
d2rl: False
initializer:
name: default
regularizer:
name: None
rnn:
name: lstm
units: 1024
layers: 1
before_mlp: True
layer_norm: True
zero_rnn_on_done: False
player:
deterministic: True
games_num: 100000
print_stats: True
| 2,402 | YAML | 20.265487 | 68 | 0.562448 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/IngenuityPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:Ingenuity,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
device: ${....rl_device}
device_name: ${....rl_device}
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1e-3
lr_schedule: adaptive
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:400,${....max_iterations}}
save_best_after: 50
save_frequency: 50
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
| 1,612 | YAML | 21.402777 | 101 | 0.593052 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/QuadcopterPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 256, 128]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:Quadcopter,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
device: ${....rl_device}
device_name: ${....rl_device}
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.1
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1e-3
lr_schedule: adaptive
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:1000,${....max_iterations}}
save_best_after: 50
save_frequency: 50
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
| 1,613 | YAML | 21.416666 | 101 | 0.593304 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/FactoryTaskNutBoltScrewPPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False}
load_path: ${...checkpoint}
config:
name: ${resolve_default:FactoryTaskNutBoltScrew,${....experiment}}
full_experiment_name: ${.name}
device: ${....rl_device}
device_name: ${....rl_device}
env_name: rlgpu
multi_gpu: False
ppo: True
mixed_precision: True
normalize_input: True
normalize_value: True
value_bootstrap: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 1.0
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 1e-4
lr_schedule: fixed
schedule_type: standard
kl_threshold: 0.016
score_to_win: 20000
max_epochs: ${resolve_default:400,${....max_iterations}}
save_best_after: 50
save_frequency: 100
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: False
e_clip: 0.2
horizon_length: 512
minibatch_size: 512
mini_epochs: 8
critic_coef: 2
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
| 1,597 | YAML | 20.594594 | 70 | 0.594865 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/BallBalancePPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [128, 64, 32]
activation: elu
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:BallBalance,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
device: ${....rl_device}
device_name: ${....rl_device}
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.1
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 3e-4
lr_schedule: adaptive
kl_threshold: 0.008
score_to_win: 20000
max_epochs: ${resolve_default:250,${....max_iterations}}
save_best_after: 50
save_frequency: 100
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 8192
mini_epochs: 8
critic_coef: 4
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
| 1,593 | YAML | 21.450704 | 101 | 0.593848 |
Tbarkin121/GuardDog/OmniIsaacGymEnvs/omniisaacgymenvs/cfg/train/FrankaDeformablePPO.yaml | params:
seed: ${...seed}
algo:
name: a2c_continuous
model:
name: continuous_a2c_logstd
network:
name: actor_critic
separate: False
space:
continuous:
mu_activation: None
sigma_activation: None
mu_init:
name: default
sigma_init:
name: const_initializer
val: 0
fixed_sigma: True
mlp:
units: [256, 128, 64]
activation: elu
d2rl: False
initializer:
name: default
regularizer:
name: None
load_checkpoint: ${if:${...checkpoint},True,False} # flag which sets whether to load the checkpoint
load_path: ${...checkpoint} # path to the checkpoint to load
config:
name: ${resolve_default:FrankaDeformable,${....experiment}}
full_experiment_name: ${.name}
env_name: rlgpu
device: ${....rl_device}
device_name: ${....rl_device}
multi_gpu: ${....multi_gpu}
ppo: True
mixed_precision: False
normalize_input: True
normalize_value: True
num_actors: ${....task.env.numEnvs}
reward_shaper:
scale_value: 0.01
normalize_advantage: True
gamma: 0.99
tau: 0.95
learning_rate: 5e-4
lr_schedule: adaptive
kl_threshold: 0.008
score_to_win: 100000000
max_epochs: ${resolve_default:6000,${....max_iterations}}
save_best_after: 500
save_frequency: 500
print_stats: True
grad_norm: 1.0
entropy_coef: 0.0
truncate_grads: True
e_clip: 0.2
horizon_length: 16
minibatch_size: 16384 #2048 #4096 #8192 #16384
mini_epochs: 8
critic_coef: 4
clip_value: True
seq_length: 4
bounds_loss_coef: 0.0001
| 1,665 | YAML | 22.138889 | 101 | 0.600601 |
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