Train RL in Simulation

This guide explains how to use the gym_hil simulation environments as an alternative to real robots when working with the LeRobot framework for Human-In-the-Loop (HIL) reinforcement learning.

gym_hil is a package that provides Gymnasium-compatible simulation environments specifically designed for Human-In-the-Loop reinforcement learning. These environments allow you to:

• Train policies in simulation to test the RL stack before training on real robots

• Collect demonstrations in sim using external devices like gamepads or keyboards

• Perform human interventions during policy learning

Currently, the main environment is a Franka Panda robot simulation based on MuJoCo, with tasks like picking up a cube.

Installation

First, install the gym_hil package within the LeRobot environment:

pip install -e ".[hilserl]"

What do I need?

• A gamepad or keyboard to control the robot
• A Nvidia GPU

Configuration

To use gym_hil with LeRobot, you need to create a configuration file. An example is provided here. Key configuration sections include:

Environment Type and Task

{
    "type": "hil",
    "name": "franka_sim",
    "task": "PandaPickCubeGamepad-v0",
    "device": "cuda"
}

Available tasks:

• PandaPickCubeBase-v0: Basic environment
• PandaPickCubeGamepad-v0: With gamepad control
• PandaPickCubeKeyboard-v0: With keyboard control

Gym Wrappers Configuration

"wrapper": {
    "gripper_penalty": -0.02,
    "control_time_s": 15.0,
    "use_gripper": true,
    "fixed_reset_joint_positions": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785],
    "end_effector_step_sizes": {
        "x": 0.025,
        "y": 0.025,
        "z": 0.025
    },
    "control_mode": "gamepad"
    }

Important parameters:

• gripper_penalty: Penalty for excessive gripper movement
• use_gripper: Whether to enable gripper control
• end_effector_step_sizes: Size of the steps in the x,y,z axes of the end-effector
• control_mode: Set to "gamepad" to use a gamepad controller

Running with HIL RL of LeRobot

Basic Usage

To run the environment, set mode to null:

python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json

Recording a Dataset

To collect a dataset, set the mode to record whilst defining the repo_id and number of episodes to record:

python lerobot/scripts/rl/gym_manipulator.py --config_path path/to/gym_hil_env.json

Training a Policy

To train a policy, checkout the configuration example available here and run the actor and learner servers:

python lerobot/scripts/rl/actor.py --config_path path/to/train_gym_hil_env.json

In a different terminal, run the learner server:

python lerobot/scripts/rl/learner.py --config_path path/to/train_gym_hil_env.json

The simulation environment provides a safe and repeatable way to develop and test your Human-In-the-Loop reinforcement learning components before deploying to real robots.

Congrats 🎉, you have finished this tutorial!

Paper citation:

@article{luo2024precise,
  title={Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning},
  author={Luo, Jianlan and Xu, Charles and Wu, Jeffrey and Levine, Sergey},
  journal={arXiv preprint arXiv:2410.21845},
  year={2024}
}