temp/dummy_full_output.txt

================= Task and Asset Design!

>>> Prompt: 
You are an AI in robot simulation code and task design. I will provide you some example tasks, code implementation, and some guidelines for how to generate tasks and then you will help me generate a new task `TARGET_TASK_NAME`. My goal is to design creative and feasible tasks for tabletop manipulation. I will first ask you to describe the task in natural languages and then will let you write the code for it. 


=========
Here are all the assets. Use only these assets in the task and code design. 
"""
insertion/:
ell.urdf  fixture.urdf

bowl/:
bowl.urdf

box/:
box-template.urdf

stacking/:
block.urdf  stand.urdf

zone/:
zone.obj  zone.urdf

pallet/:
pallet.obj  pallet.urdf

ball/:
ball-template.urdf

cylinder/:
cylinder-template.urdf

bowl/:
bowl.urdf

# assets not for picking
corner/:
corner-template.urdf

line/:
single-green-line-template.urdf

container/:
container-template.urdf
"""


There are certain rules on the asset usage.
1. Sweeping piles task must have small blocks `block/small.urdf` and zones `zone.urdf`. Only the piles can be swept in all assets
2. Insertion tasks must have `insertion/ell.urdf` and `insertion/fixture.urdf`. Only the fixture can be inserted in all assets.


=========
Here are some examples of good tasks. Try to be creative and high standard, and avoid overlapping with these tasks. 

TASK_DESCRIPTION_PROMPT



=========
Now let's design the task `TARGET_TASK_NAME`. Please describe the new task `TARGET_TASK_NAME` in natural languages in a clear and detailed way. Format the answer in a python dictionary with keys "task-name" and value type string with lower-case and separated by hyphens, "task-description" (one sentence and do not mention urdf paths) and value type string, and "assets-used" and value type list of strings. 

Note:
- Do not use assets that are not in the list above. 
- Tasks that have more colors and shapes are interesting.
- Be as specific as possible about the number, shape, and color of each asset in the task descriptions. 
- The task need to obey physics and remain feasible.


=========
Now I will provide you some reference code and you can write the code for the task.


"""
import os

import numpy as np
from cliport.tasks.task import Task
from cliport.utils import utils


class PackingShapes(Task):
    """pick up randomly sized shapes and place them tightly into a container."""

    def __init__(self):
        super().__init__()
        self.max_steps = 1
        self.homogeneous = False

        self.lang_template = "pack the {obj} in the brown box"
        self.task_completed_desc = "done packing shapes."
        self.additional_reset()


    def reset(self, env):
        super().reset(env)

        # Shape Names:
        shapes = utils.assembling_kit_shapes

        n_objects = 5
        if self.mode == 'train':
            obj_shapes = np.random.choice(self.train_set, n_objects, replace=False)
        else:
            if self.homogeneous:
                obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects
            else:
                obj_shapes = np.random.choice(self.test_set, n_objects, replace=False)

        # Shuffle colors to avoid always picking an object of the same color
        colors, color_names = utils.get_colors(mode=self.mode)

        # Add container box.
        zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05)
        zone_pose = self.get_random_pose(env, zone_size)
        container_template = 'container/container-template.urdf'
        replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)}
        # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template`
        container_urdf = self.fill_template(container_template, replace)
        env.add_object(container_urdf, zone_pose, 'fixed')

        # Add objects.
        objects = []
        template = 'kitting/object-template.urdf'
        for i in range(n_objects):
            shape = obj_shapes[i]

            # x, y, z dimensions for the asset size
            size = (0.08, 0.08, 0.02)
            pose= self.get_random_pose(env, size)
            fname = f'{shape:02d}.obj'
            fname = os.path.join(self.assets_root, 'kitting', fname)
            scale = [0.003, 0.003, 0.001]  
            replace = {'FNAME': (fname,),
                       'SCALE': scale,
                       'COLOR': colors[i]}

            # IMPORTANT: REPLACE THE TEMPLATE URDF
            urdf = self.fill_template(template, replace)
            block_id = env.add_object(urdf, pose)
            objects.append(block_id)

        # Pick the first shape.
        num_objects_to_pick = 1
        for i in range(num_objects_to_pick):
            # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict
            self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False,
                rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick)

            self.lang_goals.append(self.lang_template.format(obj=shapes[obj_shapes[i]]))
"""
"""
import numpy as np
from cliport.tasks.task import Task
from cliport.utils import utils
import pybullet as p


class PlaceRedInGreen(Task):
    """pick up the red blocks and place them into the green bowls amidst other objects."""

    def __init__(self):
        super().__init__()
        self.max_steps = 10
        self.lang_template = "put the red blocks in a green bowl"
        self.task_completed_desc = "done placing blocks in bowls."
        self.additional_reset()

    def reset(self, env):
        super().reset(env)
        n_bowls = np.random.randint(1, 4)
        n_blocks = np.random.randint(1, n_bowls + 1)

        # Add bowls.
        # x, y, z dimensions for the asset size
        bowl_size = (0.12, 0.12, 0)
        bowl_urdf = 'bowl/bowl.urdf'
        bowl_poses = []
        for _ in range(n_bowls):
            bowl_pose = self.get_random_pose(env, obj_size=bowl_size)
            env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed')
            bowl_poses.append(bowl_pose)

        # Add blocks.
        # x, y, z dimensions for the asset size
        blocks = []
        block_size = (0.04, 0.04, 0.04)
        block_urdf = 'stacking/block.urdf'
        for _ in range(n_blocks):
            block_pose = self.get_random_pose(env, obj_size=block_size)
            block_id = env.add_object(block_urdf, block_pose)
            blocks.append(block_id)

        # Goal: each red block is in a different green bowl.
        self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False,
                rotations=True, metric='pose', params=None, step_max_reward=1)
        self.lang_goals.append(self.lang_template)

        # Colors of distractor objects.
        # IMPORTANT: RETRIEVE THE ACTUAL COLOR VALUES
        bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green']
        block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red']

        # Add distractors.
        n_distractors = 0
        while n_distractors < 6:
            is_block = np.random.rand() > 0.5
            urdf = block_urdf if is_block else bowl_urdf
            size = block_size if is_block else bowl_size
            colors = block_colors if is_block else bowl_colors
            pose = self.get_random_pose(env, obj_size=size)
            color = colors[n_distractors % len(colors)]

            obj_id = env.add_object(urdf, pose, color=color)
            n_distractors += 1
"""


=========
Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way.  Now write the code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python.  Reminder: TASK_STRING_TEMPLATE 




>>> Answer: 
The task you're asking for is "TARGET_TASK_NAME". This is a placeholder name, so I'll replace it with a concrete task name. Let's design a task called "insert-ell-into-green-fixture". In this task, the robot needs to pick up `insertion/ell.urdf` and `insertion/fixture.urdf` and insert ell into the green fixture.

```python
import pybullet as p
from cliport.tasks.task import Task
from cliport.utils import utils

class InsertEllIntoGreenFixture(Task):
    """Insert ell into the green fixture."""

    def __init__(self):
        super().__init__()
        self.max_steps = 5
        self.lang_template = "insert the ell into the green fixture"
        self.task_completed_desc = "done inserting ell into fixture."
        self.additional_reset()

    def reset(self, env):
        super().reset(env)

        # Add ell.
        ell_size = (0.02, 0.02, 0.02)  # x, y, z dimensions for the asset size
        ell_pose = self.get_random_pose(env, ell_size)
        ell_urdf = 'insertion/ell.urdf'
        ell_id = env.add_object(ell_urdf, ell_pose)
        ell_color = p.getVisualShapeData(ell_id)[0][7]  # retrieve the actual color values of ell

        # Add green fixture.
        fixture_size = (0.02, 0.02, 0.02)  # x, y, z dimensions for the asset size
        fixture_pose = self.get_random_pose(env, fixture_size)
        fixture_urdf = 'insertion/fixture.urdf'
        env.add_object(fixture_urdf, fixture_pose, fixed_base=1, color=utils.COLORS['green'])

        # Goal: insert ell into the fixture.
        self.add_goal(objs=[ell_id], matches=[[1]], targ_poses=[fixture_pose], 
                      replace=False, rotations=True, metric='pose', params=None, step_max_reward=1)
        self.lang_goals.append(self.lang_template.format(obj='ell', color=ell_color))

```
The task is designed to test the robot's ability to perform delicate manipulations. It must pick up the ell, move it to the fixture, and then insert it. The ell and fixture are positioned in random locations at the start of each task instance, ensuring a diverse range of scenarios.

================= Code Generation!

>>> Prompt: 
Now I will provide you some reference code and you can write the code for the task "dummy".


"""
import os

import numpy as np
from cliport.tasks.task import Task
from cliport.utils import utils


class PackingShapes(Task):
    """pick up randomly sized shapes and place them tightly into a container."""

    def __init__(self):
        super().__init__()
        self.max_steps = 1
        self.homogeneous = False

        self.lang_template = "pack the {obj} in the brown box"
        self.task_completed_desc = "done packing shapes."
        self.additional_reset()


    def reset(self, env):
        super().reset(env)

        # Shape Names:
        shapes = utils.assembling_kit_shapes

        n_objects = 5
        if self.mode == 'train':
            obj_shapes = np.random.choice(self.train_set, n_objects, replace=False)
        else:
            if self.homogeneous:
                obj_shapes = [np.random.choice(self.test_set, replace=False)] * n_objects
            else:
                obj_shapes = np.random.choice(self.test_set, n_objects, replace=False)

        # Shuffle colors to avoid always picking an object of the same color
        colors, color_names = utils.get_colors(mode=self.mode)

        # Add container box.
        zone_size = self.get_random_size(0.1, 0.15, 0.1, 0.15, 0.05, 0.05)
        zone_pose = self.get_random_pose(env, zone_size)
        container_template = 'container/container-template.urdf'
        replace = {'DIM': zone_size, 'HALF': (zone_size[0] / 2, zone_size[1] / 2, zone_size[2] / 2)}
        # IMPORTANT: REPLACE THE TEMPLATE URDF with `fill_template`
        container_urdf = self.fill_template(container_template, replace)
        env.add_object(container_urdf, zone_pose, 'fixed')

        # Add objects.
        objects = []
        template = 'kitting/object-template.urdf'
        for i in range(n_objects):
            shape = obj_shapes[i]

            # x, y, z dimensions for the asset size
            size = (0.08, 0.08, 0.02)
            pose= self.get_random_pose(env, size)
            fname = f'{shape:02d}.obj'
            fname = os.path.join(self.assets_root, 'kitting', fname)
            scale = [0.003, 0.003, 0.001]  # .0005
            replace = {'FNAME': (fname,),
                       'SCALE': scale,
                       'COLOR': colors[i]}

            # IMPORTANT: REPLACE THE TEMPLATE URDF
            urdf = self.fill_template(template, replace)
            block_id = env.add_object(urdf, pose)
            objects.append(block_id)

        # Pick the first shape.
        num_objects_to_pick = 1
        for i in range(num_objects_to_pick):
            # IMPORTANT: Specify (obj_pts, [(zone_pose, zone_size)]) for target `zone`. obj_pts is a dict
            language_goal = self.lang_template.format(obj=shapes[obj_shapes[i]])
            self.add_goal(objs=[objects[i]], matches=np.int32([[1]]), targ_poses=[zone_pose], replace=False,
                rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1 / num_objects_to_pick,
                language_goal=language_goal)
"""
"""
import numpy as np
from cliport.tasks.task import Task
from cliport.utils import utils
import pybullet as p


class PlaceRedInGreen(Task):
    """pick up the red blocks and place them into the green bowls amidst other objects."""

    def __init__(self):
        super().__init__()
        self.max_steps = 10
        self.lang_template = "put the red blocks in a green bowl"
        self.task_completed_desc = "done placing blocks in bowls."
        self.additional_reset()

    def reset(self, env):
        super().reset(env)
        n_bowls = np.random.randint(1, 4)
        n_blocks = np.random.randint(1, n_bowls + 1)

        # Add bowls.
        # x, y, z dimensions for the asset size
        bowl_size = (0.12, 0.12, 0)
        bowl_urdf = 'bowl/bowl.urdf'
        bowl_poses = []
        for _ in range(n_bowls):
            bowl_pose = self.get_random_pose(env, obj_size=bowl_size)
            env.add_object(urdf=bowl_urdf, pose=bowl_pose, category='fixed')
            bowl_poses.append(bowl_pose)

        # Add blocks.
        # x, y, z dimensions for the asset size
        blocks = []
        block_size = (0.04, 0.04, 0.04)
        block_urdf = 'stacking/block.urdf'
        for _ in range(n_blocks):
            block_pose = self.get_random_pose(env, obj_size=block_size)
            block_id = env.add_object(block_urdf, block_pose)
            blocks.append(block_id)

        # Goal: each red block is in a different green bowl.
        self.add_goal(objs=blocks, matches=np.ones((len(blocks), len(bowl_poses))), targ_poses=bowl_poses, replace=False,
                rotations=True, metric='pose', params=None, step_max_reward=1, language_goal=self.lang_template)

        # Colors of distractor objects.
        bowl_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'green']
        block_colors = [utils.COLORS[c] for c in utils.COLORS if c != 'red']

        # Add distractors.
        n_distractors = 0
        while n_distractors < 6:
            is_block = np.random.rand() > 0.5
            urdf = block_urdf if is_block else bowl_urdf
            size = block_size if is_block else bowl_size
            colors = block_colors if is_block else bowl_colors
            pose = self.get_random_pose(env, obj_size=size)
            color = colors[n_distractors % len(colors)]

            obj_id = env.add_object(urdf, pose, color=color)
            n_distractors += 1
"""
"""
import numpy as np
from cliport.tasks import primitives
from cliport.tasks.grippers import Spatula
from cliport.tasks.task import Task
from cliport.utils import utils


class SweepingPiles(Task):
    """Push piles of small objects into a target goal zone marked on the tabletop."""

    def __init__(self):
        super().__init__()
        self.max_steps = 20
        self.lang_template = "push the pile of blocks into the green square"
        self.task_completed_desc = "done sweeping."
        self.primitive = primitives.push
        self.ee = Spatula
        self.additional_reset()

    def reset(self, env):
        super().reset(env)

        # Add goal zone.
        zone_size = (0.12, 0.12, 0)
        zone_pose = self.get_random_pose(env, zone_size)
        env.add_object('zone/zone.urdf', zone_pose, 'fixed')

        # Add pile of small blocks with `make_piles` function
        obj_ids = self.make_piles(env)

        # Add goal
        self.add_goal(objs=obj_ids, matches=np.ones((50, 1)), targ_poses=[zone_pose], replace=True,
                rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template)
"""
"""
import numpy as np
from cliport.tasks.task import Task
from cliport.utils import utils
import pybullet as p

class StackBlockPyramid(Task):
    """Build a pyramid of colored blocks in a color sequence"""

    def __init__(self):
        super().__init__()
        self.max_steps = 12
        self.lang_template = "make the {row} row with {blocks}"
        self.task_completed_desc = "done stacking block pyramid."
        self.additional_reset()

    def reset(self, env):
        super().reset(env)

        # Add base.
        base_size = (0.05, 0.15, 0.005)
        base_urdf = 'stacking/stand.urdf'
        base_pose = self.get_random_pose(env, base_size)
        env.add_object(base_urdf, base_pose, category='fixed')

        # Block colors.
        colors = [
            utils.COLORS['purple'], utils.COLORS['blue'], utils.COLORS['green'],
            utils.COLORS['yellow'], utils.COLORS['orange'], utils.COLORS['red']
        ]

        # Add blocks.
        block_size = (0.04, 0.04, 0.04)
        block_urdf = 'stacking/block.urdf'

        objs = []
        for i in range(6):
            block_pose = self.get_random_pose(env, block_size)
            block_id = env.add_object(block_urdf, block_pose, color=colors[i])
            objs.append(block_id)

        # IMPORTANT Associate placement locations for goals.
        place_pos = [(0, -0.05, 0.03), (0, 0, 0.03),
                     (0, 0.05, 0.03), (0, -0.025, 0.08),
                     (0, 0.025, 0.08), (0, 0, 0.13)]
        targs = [(utils.apply(base_pose, i), base_pose[1]) for i in place_pos]

        # Goal: blocks are stacked in a pyramid (bottom row: green, blue, purple).
        language_goal = self.lang_template.format(blocks="the green, blue and purple blocks", row="bottom")
        self.add_goal(objs=objs[:3], matches=np.ones((3, 3)), targ_poses=targs[:3], replace=False,
                rotations=True, metric='pose', params=None, step_max_reward=1 / 2, symmetries=[np.pi/2]*3, language_goal=language_goal)

        # Goal: blocks are stacked in a pyramid (middle row: yellow, orange).
        language_goal = self.lang_template.format(blocks="the yellow and orange blocks", row="middle")      
        self.add_goal(objs=objs[3:5], matches=np.ones((2, 2)), targ_poses=targs[3:5], replace=False,
                rotations=True, metric='pose', params=None, step_max_reward=1 / 3, symmetries=[np.pi/2]*2, language_goal=language_goal)

        # Goal: blocks are stacked in a pyramid (top row: red).
        language_goal = self.lang_template.format(blocks="the red block", row="top")
        self.add_goal(objs=objs[5:], matches=np.ones((1, 1)), targ_poses=targs[5:], replace=False,
                rotations=True, metric='pose', params=None, step_max_reward=1 / 6, symmetries=[np.pi/2]*1, language_goal=language_goal)
"""



Do not use libraries, functions, and assets that you don't know. For each object, try to describe its color, size, category in the task first before you write the code. You do not need extra helper functions. Comment the code liberally to explain what each piece does and why it's written that way. If you have only one goal, `step_max_reward` in `add_goal` should be 1. Use functions `make_piles` and `make_ropes` for creating piles and cables. To use spatula together with the push primitives, import the libraries 
"""
from cliport.tasks import primitives;
from cliport.tasks.grippers import Spatula
"""
and then use `self.primitive = primitives.push` and `self.ee = Spatula`.
Note that the number of language goals usually match the number of motion goals, since they should correspond to each other.

Now write the code for the task "dummy" in python code block starting with ```python.  Reminder: {'task-name': 'dummy', 'assets-used': [], 'task_descriptions': ''} 


>>> Answer: 
The task "dummy" is a placeholder task. I'll replace it with a concrete task called "sweep-red-blocks-into-zone". In this task, the robot needs to sweep a pile of red blocks into a designated zone.

```python
import numpy as np
from cliport.tasks import primitives
from cliport.tasks.grippers import Spatula
from cliport.tasks.task import Task
from cliport.utils import utils

class SweepRedBlocksIntoZone(Task):
    """Sweep a pile of red blocks into a designated zone."""

    def __init__(self):
        super().__init__()
        self.max_steps = 20
        self.lang_template = "sweep the pile of red blocks into the green square"
        self.task_completed_desc = "done sweeping."
        self.primitive = primitives.push
        self.ee = Spatula
        self.additional_reset()

    def reset(self, env):
        super().__init__(env)

        # Add goal zone.
        zone_size = (0.12, 0.12, 0)
        zone_pose = self.get_random_pose(env, zone_size)
        env.add_object('zone/zone.urdf', zone_pose, 'fixed')

        # Add pile of red blocks with `make_piles` function
        block_urdf = 'stacking/block.urdf'
        block_size = (0.04, 0.04, 0.04)
        block_color = utils.COLORS['red']
        pile_pose = self.get_random_pose(env, block_size)
        pile_ids = self.make_piles(env, block_urdf, block_size, block_color, pile_pose, num_piles=1, num_objs=5)

        # Add goal
        self.add_goal(objs=pile_ids, matches=np.ones((5, 1)), targ_poses=[zone_pose], replace=True,
                rotations=False, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1, language_goal=self.lang_template)
```

In this task, the robot is required to sweep a pile of red blocks into a designated zone. The pile of blocks and the zone are positioned randomly at the start of each task instance, ensuring a diverse range of scenarios. The robot is equipped with a spatula and uses the push primitive to perform the task. The task is completed when all blocks are within the zone.

================= TRIAL: 1