def __init__(self,
asset_path: str = DCLAW3_ASSET_PATH,
observation_keys: Sequence[str] = DEFAULT_OBSERVATION_KEYS,
frame_skip: int = 40,
**kwargs):
"""Initializes the environment.
Args:
asset_path: The XML model file to load.
observation_keys: The keys in `get_obs_dict` to concatenate as the
observations returned by `step` and `reset`.
frame_skip: The number of simulation steps per environment step.
interactive: If True, allows the hardware guide motor to freely
rotate and its current angle is used as the goal.
success_threshold: The difference threshold (in radians) of the
object position and the goal position within which we consider
as a sucesss.
"""
super().__init__(sim_model=get_asset_path(asset_path),
observation_keys=observation_keys,
frame_skip=frame_skip,
**kwargs)
self._desired_claw_pos = RESET_POSE
# The following are modified (possibly every reset) by subclasses.
self._initial_object_pos = 0
self._initial_object_vel = 0
def __init__(self,
asset_path: str = DCLAW3_ASSET_PATH,
observation_keys: Sequence[str] = DEFAULT_OBSERVATION_KEYS,
frame_skip: int = 40,
interactive: bool = False,
**kwargs):
"""Initializes the environment.
Args:
asset_path: The XML model file to load.
observation_keys: The keys in `get_obs_dict` to concatenate as the
observations returned by `step` and `reset`.
frame_skip: The number of simulation steps per environment step.
interactive: If True, allows the hardware guide motor to freely
rotate and its current angle is used as the goal.
"""
super().__init__(
sim_model=get_asset_path(asset_path),
observation_keys=observation_keys,
frame_skip=frame_skip,
**kwargs)
self._interactive = interactive
self._desired_claw_pos = RESET_POSE
self._target_bid = self.model.body_name2id('target')
# The following are modified (possibly every reset) by subclasses.
self._initial_object_pos = 0
self._initial_object_vel = 0
self._set_target_object_pos(0)
def __init__(self,
asset_path: str = DKITTY_ASSET_PATH,
observation_keys: Sequence[str] = DEFAULT_OBSERVATION_KEYS,
target_tracker_id: Optional[Union[str, int]] = None,
heading_tracker_id: Optional[Union[str, int]] = None,
frame_skip: int = 40,
upright_threshold: float = 0.9,
upright_reward: float = 1,
falling_reward: float = -500,
**kwargs):
"""Initializes the environment.
Args:
asset_path: The XML model file to load.
observation_keys: The keys in `get_obs_dict` to concatenate as the
observations returned by `step` and `reset`.
target_tracker_id: The device index or serial of the tracking device
for the target location.
heading_tracker_id: The device index or serial of the tracking
device for the heading direction. This defaults to the target
tracker.
frame_skip: The number of simulation steps per environment step.
upright_threshold: The threshold (in [0, 1]) above which the D'Kitty
is considered to be upright. If the cosine similarity of the
D'Kitty's z-axis with the global z-axis is below this threshold,
the D'Kitty is considered to have fallen.
upright_reward: The reward multiplier for uprightedness.
falling_reward: The reward multipler for falling.
"""
self._target_tracker_id = target_tracker_id
self._heading_tracker_id = heading_tracker_id
if self._heading_tracker_id is None:
self._heading_tracker_id = self._target_tracker_id
super().__init__(
sim_model=get_asset_path(asset_path),
observation_keys=observation_keys,
frame_skip=frame_skip,
upright_threshold=upright_threshold,
upright_reward=upright_reward,
falling_reward=falling_reward,
**kwargs)
self._initial_target_pos = np.zeros(3)
self._initial_heading_pos = None
def __init__(self,
asset_path: str = DCLAW3_ASSET_PATH,
observation_keys: Sequence[str] = DEFAULT_OBSERVATION_KEYS,
frame_skip: int = 20,
**kwargs):
"""Initializes the environment.
Args:
asset_path: The XML model file to load.
observation_keys: The keys in `get_obs_dict` to concatenate as the
observations returned by `step` and `reset`.
frame_skip: The number of simulation steps per environment step.
"""
super().__init__(sim_model=get_asset_path(asset_path),
observation_keys=observation_keys,
frame_skip=frame_skip,
**kwargs)
self._initial_pos = np.zeros(9)
self._desired_pos = np.zeros(9)
def __init__(
self,
asset_path: str = DKITTY_ASSET_PATH,
observation_keys: Sequence[str] = DEFAULT_OBSERVATION_KEYS,
frame_skip: int = 40,
upright_threshold: float = 0, # cos(90deg)
upright_reward: float = 2,
falling_reward: float = -100,
**kwargs):
"""Initializes the environment.
Args:
asset_path: The XML model file to load.
observation_keys: The keys in `get_obs_dict` to concatenate as the
observations returned by `step` and `reset`.
device_path: The device path to Dynamixel hardware.
torso_tracker_id: The device index or serial of the tracking device
for the D'Kitty torso.
frame_skip: The number of simulation steps per environment step.
upright_threshold: The threshold (in [0, 1]) above which the D'Kitty
is considered to be upright. If the cosine similarity of the
D'Kitty's z-axis with the global z-axis is below this threshold,
the D'Kitty is considered to have fallen.
upright_reward: The reward multiplier for uprightedness.
falling_reward: The reward multipler for falling.
"""
super().__init__(
sim_model=get_asset_path(asset_path),
observation_keys=observation_keys,
frame_skip=frame_skip,
upright_threshold=upright_threshold,
upright_reward=upright_reward,
falling_reward=falling_reward,
**kwargs)
self._desired_pose = np.zeros(12)
self._initial_pose = np.zeros(12)
def __init__(self,
asset_path: str = DKITTY_ASSET_PATH,
observation_keys: Sequence[str] = DEFAULT_OBSERVATION_KEYS,
device_path: Optional[str] = None,
torso_tracker_id: Optional[Union[str, int]] = None,
frame_skip: int = 40,
sticky_action_probability: float = 0.0,
upright_threshold: float = 0.9,
upright_reward: float = 1,
falling_reward: float = -500,
expose_last_action: bool = True,
expose_upright: bool = True,
robot_noise_ratio: float = 0.05,
**kwargs):
"""Initializes the environment.
Args:
asset_path: The XML model file to load.
observation_keys: The keys in `get_obs_dict` to concatenate as the
observations returned by `step` and `reset`.
device_path: The device path to Dynamixel hardware.
torso_tracker_id: The device index or serial of the tracking device
for the D'Kitty torso.
frame_skip: The number of simulation steps per environment step.
sticky_action_probability: Repeat previous action with this
probability. Default 0 (no sticky actions).
upright_threshold: The threshold (in [0, 1]) above which the D'Kitty
is considered to be upright. If the cosine similarity of the
D'Kitty's z-axis with the global z-axis is below this threshold,
the D'Kitty is considered to have fallen.
upright_reward: The reward multiplier for uprightedness.
falling_reward: The reward multipler for falling.
"""
self._expose_last_action = expose_last_action
self._expose_upright = expose_upright
observation_keys = observation_keys[:-2]
if self._expose_last_action:
observation_keys += ("last_action", )
if self._expose_upright:
observation_keys += ("upright", )
# robot_config = self.get_robot_config(device_path)
# if 'sim_observation_noise' in robot_config.keys():
# robot_config['sim_observation_noise'] = robot_noise_ratio
super().__init__(
sim_model=get_asset_path(asset_path),
# robot_config=robot_config,
# tracker_config=self.get_tracker_config(
# torso=torso_tracker_id,
# ),
observation_keys=observation_keys,
frame_skip=frame_skip,
upright_threshold=upright_threshold,
upright_reward=upright_reward,
falling_reward=falling_reward,
**kwargs)
self._last_action = np.zeros(12)
self._sticky_action_probability = sticky_action_probability
self._time_step = 0
