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import os
import numpy as np
from cliport.tasks.task import Task
from cliport.utils import utils
import pybullet as p
class PackingBoxes(Task):
"""pick up randomly sized boxes and place them tightly into a container."""
def __init__(self):
super().__init__()
self.max_steps = 20
self.lang_template = "pack all the boxes inside the brown box"
self.task_completed_desc = "done packing boxes."
self.zone_bounds = np.array([[0.25, 0.75], [-0.5, 0.5], [0, 0.08]])
self.additional_reset()
def reset(self, env):
super().reset(env)
# Add container box.
zone_size = self.get_random_size(0.05, 0.3, 0.05, 0.3, 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
container_urdf = self.fill_template(container_template, replace)
env.add_object(container_urdf, zone_pose, 'fixed')
margin = 0.01
min_object_dim = 0.05
bboxes = []
# Split container space with KD trees.
stack_size = np.array(zone_size)
stack_size[0] -= 0.01
stack_size[1] -= 0.01
root_size = (0.01, 0.01, 0) + tuple(stack_size)
root = utils.TreeNode(None, [], bbox=np.array(root_size))
utils.KDTree(root, min_object_dim, margin, bboxes)
colors = [utils.COLORS[c] for c in utils.COLORS if c != 'brown']
# Add objects in container.
object_ids = []
bboxes = np.array(bboxes)
object_template = 'box/box-template.urdf'
# Compute object points that are needed for zone
for bbox in bboxes:
size = bbox[3:] - bbox[:3]
position = size / 2. + bbox[:3]
position[0] += -zone_size[0] / 2
position[1] += -zone_size[1] / 2
pose = (position, (0, 0, 0, 1))
pose = utils.multiply(zone_pose, pose)
# IMPORTANT: REPLACE THE TEMPLATE URDF
urdf = self.fill_template(object_template, {'DIM': size})
icolor = np.random.choice(range(len(colors)), 1).squeeze()
box_id = env.add_object(urdf, pose, color=colors[icolor])
object_ids.append(box_id)
# Randomly select object in box and save ground truth pose.
object_volumes = []
true_poses = []
for object_id in object_ids:
true_pose = p.getBasePositionAndOrientation(object_id)
object_size = p.getVisualShapeData(object_id)[0][3]
object_volumes.append(np.prod(np.array(object_size) * 100))
pose = self.get_random_pose(env, object_size)
p.resetBasePositionAndOrientation(object_id, pose[0], pose[1])
true_poses.append(true_pose)
self.add_goal(objs=object_ids, matches=np.eye(len(object_ids)), targ_poses=true_poses, replace=False,
rotations=True, metric='zone', params=[(zone_pose, zone_size)], step_max_reward=1)
self.lang_goals.append(self.lang_template)
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