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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) | |