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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. My goal is to design diverse 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.
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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
"""
"""
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
"""
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Please describe the task "TASK_NAME_TEMPLATE" in natural languages and format the answer in a python dictionary with keys "task-name" and value type string, "task-description" (one specific sentence) and value type string, and "assets-used" and value type list of strings.
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Now write the pybullet simulation code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python.
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