def __init__(self,
agent_rate: float,
robot_model: str,
restrict_position_goal_to_workspace: bool,
sparse_reward: bool,
act_quick_reward: float,
required_accuracy: float,
verbose: bool,
**kwargs):
# Initialize the Task base class
Reach.__init__(self,
agent_rate=agent_rate,
robot_model=robot_model,
restrict_position_goal_to_workspace=restrict_position_goal_to_workspace,
sparse_reward=sparse_reward,
act_quick_reward=act_quick_reward,
required_accuracy=required_accuracy,
verbose=verbose,
**kwargs)
# Perception (RGB camera)
self.camera_sub = CameraSubscriber(topic=f'/{self._camera_type}',
is_point_cloud=False,
node_name=f'drl_grasping_rgb_camera_sub_{self.id}')
def __init__(self, agent_rate: float, robot_model: str,
restrict_position_goal_to_workspace: bool,
sparse_reward: bool, act_quick_reward: float,
required_accuracy: float, octree_depth: int,
octree_full_depth: int, octree_include_color: bool,
octree_n_stacked: int, octree_max_size: int, verbose: bool,
**kwargs):
# Initialize the Task base class
Reach.__init__(self,
agent_rate=agent_rate,
robot_model=robot_model,
restrict_position_goal_to_workspace=
restrict_position_goal_to_workspace,
sparse_reward=sparse_reward,
act_quick_reward=act_quick_reward,
required_accuracy=required_accuracy,
verbose=verbose,
**kwargs)
if octree_include_color:
self._camera_type = 'rgbd_camera'
else:
self._camera_type = 'depth_camera'
# Perception (RGB-D camera - point cloud)
self.camera_sub = CameraSubscriber(
topic=f'/{self._camera_type}/points',
is_point_cloud=True,
node_name=f'drl_grasping_point_cloud_sub_{self.id}')
self.octree_creator = OctreeCreator(
min_bound=self._octree_min_bound,
max_bound=self._octree_max_bound,
depth=octree_depth,
full_depth=octree_full_depth,
include_color=octree_include_color,
use_sim_time=True,
debug_draw=False,
debug_write_octree=False,
robot_frame_id='panda_link0'
if 'panda' == robot_model else 'base_link',
node_name=f'drl_grasping_octree_creator_{self.id}')
# Additional parameters
self._octree_n_stacked = octree_n_stacked
self._octree_max_size = octree_max_size
# List of all octrees
self.__stacked_octrees = deque([], maxlen=self._octree_n_stacked)
def __init__(self,
agent_rate: float,
robot_model: str,
restrict_position_goal_to_workspace: bool,
gripper_dead_zone: float,
full_3d_orientation: bool,
sparse_reward: bool,
normalize_reward: bool,
required_reach_distance: float,
required_lift_height: float,
reach_dense_reward_multiplier: float,
lift_dense_reward_multiplier: float,
act_quick_reward: float,
outside_workspace_reward: float,
ground_collision_reward: float,
n_ground_collisions_till_termination: int,
curriculum_enable_workspace_scale: bool,
curriculum_min_workspace_scale: float,
curriculum_enable_object_count_increase: bool,
curriculum_max_object_count: int,
curriculum_enable_stages: bool,
curriculum_stage_reward_multiplier: float,
curriculum_stage_increase_rewards: bool,
curriculum_success_rate_threshold: float,
curriculum_success_rate_rolling_average_n: int,
curriculum_restart_every_n_steps: int,
curriculum_skip_reach_stage: bool,
curriculum_skip_grasp_stage: bool,
curriculum_restart_exploration_at_start: bool,
max_episode_length: int,
octree_depth: int,
octree_full_depth: int,
octree_include_color: bool,
octree_n_stacked: int,
octree_max_size: int,
proprieceptive_observations: bool,
verbose: bool,
preload_replay_buffer: bool = False,
**kwargs):
# Initialize the Task base class
Grasp.__init__(
self,
agent_rate=agent_rate,
robot_model=robot_model,
restrict_position_goal_to_workspace=
restrict_position_goal_to_workspace,
gripper_dead_zone=gripper_dead_zone,
full_3d_orientation=full_3d_orientation,
sparse_reward=sparse_reward,
normalize_reward=normalize_reward,
required_reach_distance=required_reach_distance,
required_lift_height=required_lift_height,
reach_dense_reward_multiplier=reach_dense_reward_multiplier,
lift_dense_reward_multiplier=lift_dense_reward_multiplier,
act_quick_reward=act_quick_reward,
outside_workspace_reward=outside_workspace_reward,
ground_collision_reward=ground_collision_reward,
n_ground_collisions_till_termination=
n_ground_collisions_till_termination,
curriculum_enable_workspace_scale=curriculum_enable_workspace_scale,
curriculum_min_workspace_scale=curriculum_min_workspace_scale,
curriculum_enable_object_count_increase=
curriculum_enable_object_count_increase,
curriculum_max_object_count=curriculum_max_object_count,
curriculum_enable_stages=curriculum_enable_stages,
curriculum_stage_reward_multiplier=
curriculum_stage_reward_multiplier,
curriculum_stage_increase_rewards=curriculum_stage_increase_rewards,
curriculum_success_rate_threshold=curriculum_success_rate_threshold,
curriculum_success_rate_rolling_average_n=
curriculum_success_rate_rolling_average_n,
curriculum_restart_every_n_steps=curriculum_restart_every_n_steps,
curriculum_skip_reach_stage=curriculum_skip_reach_stage,
curriculum_skip_grasp_stage=curriculum_skip_grasp_stage,
curriculum_restart_exploration_at_start=
curriculum_restart_exploration_at_start,
max_episode_length=max_episode_length,
verbose=verbose,
preload_replay_buffer=preload_replay_buffer,
**kwargs)
if octree_include_color:
self._camera_type = 'rgbd_camera'
else:
self._camera_type = 'depth_camera'
# Perception (RGB-D camera - point cloud)
self.camera_sub = CameraSubscriber(
topic=f'/{self._camera_type}/points',
is_point_cloud=True,
node_name=f'drl_grasping_point_cloud_sub_{self.id}')
self.octree_creator = OctreeCreator(
min_bound=self._octree_min_bound,
max_bound=self._octree_max_bound,
depth=octree_depth,
full_depth=octree_full_depth,
include_color=octree_include_color,
use_sim_time=True,
debug_draw=False,
debug_write_octree=False,
robot_frame_id='panda_link0'
if 'panda' == robot_model else 'base_link',
node_name=f'drl_grasping_octree_creator_{self.id}')
# Additional parameters
self._octree_n_stacked = octree_n_stacked
self._octree_max_size = octree_max_size
self._proprieceptive_observations = proprieceptive_observations
# List of all octrees
self.__stacked_octrees = deque([], maxlen=self._octree_n_stacked)