| 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. |
|
|
| ========= |
| 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 |
| """ |
|
|
| ========= |
| 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. |
|
|
| ========= |
| Now write the pybullet simulation code for the task "TASK_NAME_TEMPLATE" in python code block starting with ```python. |
|
|
