def reset(self):
# reset simulation
del self.platform
# initialize simulation
initial_robot_position = TriFingerPlatform.spaces.robot_position.default
initial_object_pose=self.initializer.get_initial_state()
goal_object_pose = self.initializer.get_goal()
self.platform = TriFingerPlatform(
visualization=self.visualization,
initial_robot_position=initial_robot_position,
initial_object_pose=initial_object_pose,
)
self.goal = {
"position": goal_object_pose.position,
"orientation": goal_object_pose.orientation,
}
# visualize the goal
if self.visualization:
self.goal_marker = visual_objects.CubeMarker(
width=0.065,
position=goal_object_pose.position,
orientation=goal_object_pose.orientation,
)
self.info = {"difficulty": self.initializer.difficulty}
self.step_count = 0
return self._create_observation(0)
def reset(self):
# reset simulation
del self.platform
# initialize simulation
if self.initializer is None:
# if no initializer is given (which will be the case during training),
# we can initialize in any way desired. here, we initialize the cube always
# in the center of the arena, instead of randomly, as this appears to help
# training
initial_robot_position = TriFingerPlatform.spaces.robot_position.default
default_object_position = (
TriFingerPlatform.spaces.object_position.default)
default_object_orientation = (
TriFingerPlatform.spaces.object_orientation.default)
initial_object_pose = move_cube.Pose(
position=default_object_position,
orientation=default_object_orientation,
)
goal_object_pose = move_cube.sample_goal(difficulty=1)
else:
# if an initializer is given, i.e. during evaluation, we need to initialize
# according to it, to make sure we remain coherent with the standard CubeEnv.
# otherwise the trajectories produced during evaluation will be invalid.
initial_robot_position = TriFingerPlatform.spaces.robot_position.default
initial_object_pose = self.initializer.get_initial_state()
goal_object_pose = self.initializer.get_goal()
self.platform = TriFingerPlatform(
visualization=self.visualization,
initial_robot_position=initial_robot_position,
initial_object_pose=initial_object_pose,
)
self.goal = {
"position": goal_object_pose.position,
"orientation": goal_object_pose.orientation,
}
# visualize the goal
if self.visualization:
self.goal_marker = visual_objects.CubeMarker(
width=0.065,
position=goal_object_pose.position,
orientation=goal_object_pose.orientation,
physicsClientId=self.platform.simfinger._pybullet_client_id,
)
self.info = {"difficulty": 1}
self.step_count = 0
return self._create_observation(0)
def reset(self):
# reset simulation
del self.platform
# initialize simulation
initial_robot_position = (
TriFingerPlatform.spaces.robot_position.default
)
initial_object_pose = self.initializer.get_initial_state()
goal_object_pose = self.initializer.get_goal()
self.platform = TriFingerPlatform(
visualization=self.visualization,
initial_robot_position=initial_robot_position,
initial_object_pose=initial_object_pose,
)
self.goal = {
"position": goal_object_pose.position,
"orientation": goal_object_pose.orientation,
}
# updates smoothing parameters
self.update_smoothing()
self.episode_count += 1
self.smoothed_action = None
# visualize the goal
if self.visualization:
self.goal_marker = visual_objects.CubeMarker(
width=0.065,
position=goal_object_pose.position,
orientation=goal_object_pose.orientation,
physicsClientId=self.platform.simfinger._pybullet_client_id,
)
self.info = {"difficulty": self.initializer.difficulty}
self.step_count = 0
return self._create_observation(0)
def reset(self):
# By changing the `_reset_*` method below you can switch between using
# the platform frontend, which is needed for the submission system, and
# the direct simulation, which may be more convenient if you want to
# pre-train locally in simulation.
self._reset_platform_frontend()
if self.sim_platform:
del self.sim_platform
initial_robot_position = TriFingerPlatform.spaces.robot_position.default
default_object_position = (
TriFingerPlatform.spaces.object_position.default)
default_object_orientation = (
TriFingerPlatform.spaces.object_orientation.default)
dummy_initial_object_pose = move_cube.Pose(
position=default_object_position,
orientation=default_object_orientation,
)
self.sim_platform = TriFingerPlatform(
visualization=False,
initial_robot_position=initial_robot_position,
initial_object_pose=dummy_initial_object_pose,
)
# self.goal_marker = visual_objects.CubeMarker(
# width=0.065,
# position=self.goal["position"],
# orientation=self.goal["orientation"],
# physicsClientId=self.sim_platform.simfinger._pybullet_client_id,
# )
# self._reset_direct_simulation()
self.step_count = 0
# need to already do one step to get initial observation
# TODO disable frameskip here?
observation, _, _, _ = self.step(self._initial_action)
return observation
class ExamplePushingTrainingEnv(gym.Env):
"""Gym environment for moving cubes with simulated TriFingerPro."""
def __init__(
self,
initializer=None,
action_type=cube_env.ActionType.POSITION,
frameskip=1,
visualization=False,
):
"""Initialize.
Args:
initializer: Initializer class for providing initial cube pose and
goal pose. If no initializer is provided, we will initialize in a way
which is be helpful for learning.
action_type (ActionType): Specify which type of actions to use.
See :class:`ActionType` for details.
frameskip (int): Number of actual control steps to be performed in
one call of step().
visualization (bool): If true, the pyBullet GUI is run for
visualization.
"""
# Basic initialization
# ====================
self.initializer = initializer
self.action_type = action_type
self.visualization = visualization
if frameskip < 1:
raise ValueError("frameskip cannot be less than 1.")
self.frameskip = frameskip
# will be initialized in reset()
self.platform = None
# Create the action and observation spaces
# ========================================
spaces = TriFingerPlatform.spaces
if self.action_type == cube_env.ActionType.TORQUE:
self.action_space = spaces.robot_torque.gym
elif self.action_type == cube_env.ActionType.POSITION:
self.action_space = spaces.robot_position.gym
elif self.action_type == cube_env.ActionType.TORQUE_AND_POSITION:
self.action_space = gym.spaces.Dict({
"torque":
spaces.robot_torque.gym,
"position":
spaces.robot_position.gym,
})
else:
raise ValueError("Invalid action_type")
self.observation_names = [
"robot_position",
"robot_velocity",
"robot_tip_positions",
"object_position",
"object_orientation",
"goal_object_position",
]
self.observation_space = gym.spaces.Dict({
"robot_position":
spaces.robot_position.gym,
"robot_velocity":
spaces.robot_velocity.gym,
"robot_tip_positions":
gym.spaces.Box(
low=np.array([spaces.object_position.low] * 3),
high=np.array([spaces.object_position.high] * 3),
),
"object_position":
spaces.object_position.gym,
"object_orientation":
spaces.object_orientation.gym,
"goal_object_position":
spaces.object_position.gym,
})
def step(self, action):
if self.platform is None:
raise RuntimeError("Call `reset()` before starting to step.")
if not self.action_space.contains(action):
raise ValueError(
"Given action is not contained in the action space.")
num_steps = self.frameskip
# ensure episode length is not exceeded due to frameskip
step_count_after = self.step_count + num_steps
if step_count_after > move_cube.episode_length:
excess = step_count_after - move_cube.episode_length
num_steps = max(1, num_steps - excess)
reward = 0.0
for _ in range(num_steps):
self.step_count += 1
if self.step_count > move_cube.episode_length:
raise RuntimeError("Exceeded number of steps for one episode.")
# send action to robot
robot_action = self._gym_action_to_robot_action(action)
t = self.platform.append_desired_action(robot_action)
previous_observation = self._create_observation(t)
observation = self._create_observation(t + 1)
reward += self._compute_reward(
previous_observation=previous_observation,
observation=observation,
)
is_done = self.step_count == move_cube.episode_length
return observation, reward, is_done, self.info
def reset(self):
# reset simulation
del self.platform
# initialize simulation
if self.initializer is None:
# if no initializer is given (which will be the case during training),
# we can initialize in any way desired. here, we initialize the cube always
# in the center of the arena, instead of randomly, as this appears to help
# training
initial_robot_position = TriFingerPlatform.spaces.robot_position.default
default_object_position = (
TriFingerPlatform.spaces.object_position.default)
default_object_orientation = (
TriFingerPlatform.spaces.object_orientation.default)
initial_object_pose = move_cube.Pose(
position=default_object_position,
orientation=default_object_orientation,
)
goal_object_pose = move_cube.sample_goal(difficulty=1)
else:
# if an initializer is given, i.e. during evaluation, we need to initialize
# according to it, to make sure we remain coherent with the standard CubeEnv.
# otherwise the trajectories produced during evaluation will be invalid.
initial_robot_position = TriFingerPlatform.spaces.robot_position.default
initial_object_pose = self.initializer.get_initial_state()
goal_object_pose = self.initializer.get_goal()
self.platform = TriFingerPlatform(
visualization=self.visualization,
initial_robot_position=initial_robot_position,
initial_object_pose=initial_object_pose,
)
self.goal = {
"position": goal_object_pose.position,
"orientation": goal_object_pose.orientation,
}
# visualize the goal
if self.visualization:
self.goal_marker = visual_objects.CubeMarker(
width=0.065,
position=goal_object_pose.position,
orientation=goal_object_pose.orientation,
)
self.info = dict()
self.step_count = 0
return self._create_observation(0)
def seed(self, seed=None):
self.np_random, seed = gym.utils.seeding.np_random(seed)
move_cube.random = self.np_random
return [seed]
def _create_observation(self, t):
robot_observation = self.platform.get_robot_observation(t)
object_observation = self.platform.get_object_pose(t)
robot_tip_positions = self.platform.forward_kinematics(
robot_observation.position)
robot_tip_positions = np.array(robot_tip_positions)
observation = {
"robot_position": robot_observation.position,
"robot_velocity": robot_observation.velocity,
"robot_tip_positions": robot_tip_positions,
"object_position": object_observation.position,
"object_orientation": object_observation.orientation,
"goal_object_position": self.goal["position"],
}
return observation
@staticmethod
def _compute_reward(previous_observation, observation):
# calculate first reward term
current_distance_from_block = np.linalg.norm(
observation["robot_tip_positions"] -
observation["object_position"])
previous_distance_from_block = np.linalg.norm(
previous_observation["robot_tip_positions"] -
previous_observation["object_position"])
reward_term_1 = (previous_distance_from_block -
current_distance_from_block)
# calculate second reward term
current_dist_to_goal = np.linalg.norm(
observation["goal_object_position"] -
observation["object_position"])
previous_dist_to_goal = np.linalg.norm(
previous_observation["goal_object_position"] -
previous_observation["object_position"])
reward_term_2 = previous_dist_to_goal - current_dist_to_goal
reward = 750 * reward_term_1 + 250 * reward_term_2
return reward
def _gym_action_to_robot_action(self, gym_action):
# construct robot action depending on action type
if self.action_type == cube_env.ActionType.TORQUE:
robot_action = self.platform.Action(torque=gym_action)
elif self.action_type == cube_env.ActionType.POSITION:
robot_action = self.platform.Action(position=gym_action)
elif self.action_type == cube_env.ActionType.TORQUE_AND_POSITION:
robot_action = self.platform.Action(
torque=gym_action["torque"], position=gym_action["position"])
else:
raise ValueError("Invalid action_type")
return robot_action
class CubeEnv(gym.GoalEnv):
"""Gym environment for moving cubes with simulated TriFingerPro."""
def __init__(
self,
initializer,
action_type=ActionType.POSITION,
observation_type=None,
frameskip=1,
visualization=False,
testing=False
):
"""Initialize.
Args:
initializer: Initializer class for providing initial cube pose and
goal pose. See :class:`RandomInitializer` and
:class:`FixedInitializer`.
action_type (ActionType): Specify which type of actions to use.
See :class:`ActionType` for details.
frameskip (int): Number of actual control steps to be performed in
one call of step().
visualization (bool): If true, the pyBullet GUI is run for
visualization.
"""
# Basic initialization
# ====================
self.initializer = initializer
self.action_type = action_type
self.visualization = visualization
# TODO: The name "frameskip" makes sense for an atari environment but
# not really for our scenario. The name is also misleading as
# "frameskip = 1" suggests that one frame is skipped while it actually
# means "do one step per step" (i.e. no skip).
if frameskip < 1:
raise ValueError("frameskip cannot be less than 1.")
self.frameskip = frameskip
# will be initialized in reset()
self.platform = None
# Create the action and observation spaces
# ========================================
spaces = TriFingerPlatform.spaces
object_state_space = gym.spaces.Dict(
{
"position": spaces.object_position.gym,
"orientation": spaces.object_orientation.gym,
}
)
if self.action_type == ActionType.TORQUE:
self.unscaled_action_space = spaces.robot_torque.gym
self.action_space = gym.spaces.Box(low=-np.ones((9,)), high=np.ones((9,)))
elif self.action_type == ActionType.POSITION:
self.unscaled_action_space = spaces.robot_position.gym
self.action_space = gym.spaces.Box(low=-np.ones((9,)), high=np.ones((9,)))
elif self.action_type == ActionType.TORQUE_AND_POSITION:
self.unscaled_action_space = gym.spaces.Dict(
{
"torque": spaces.robot_torque.gym,
"position": spaces.robot_position.gym,
}
)
self.action_space = gym.spaces.Dict(
{
"torque": gym.spaces.Box(low=-np.ones((9,)), high=np.ones((9,))),
"position": gym.spaces.Box(low=-np.ones((9,)), high=np.ones((9,))),
}
)
else:
raise ValueError("Invalid action_type")
self.observation_names = [
"robot_position",
"robot_velocity",
"robot_tip_positions",
"object_position",
"object_orientation",
"goal_object_position",
]
self.observation_space = gym.spaces.Dict(
{
"observation": gym.spaces.Dict(
{
"robot_position": spaces.robot_position.gym,
"robot_velocity": spaces.robot_velocity.gym,
"robot_tip_positions": gym.spaces.Box(
low=np.array([spaces.object_position.low] * 3),
high=np.array([spaces.object_position.high] * 3),
),
"object_position": spaces.object_position.gym,
"object_orientation": spaces.object_orientation.gym,
"goal_object_position": spaces.object_position.gym,
}
),
"desired_goal": object_state_space,
"achieved_goal": object_state_space,
}
)
smoothing_params = {
"num_episodes": 1000,
"start_after": 3.0 / 7.0,
"final_alpha": 0.975,
"stop_after": 5.0 / 7.0,
}
if testing:
self.smoothing_start_episode = 0
self.smoothing_alpha = smoothing_params["final_alpha"]
self.smoothing_increase_step = 0
self.smoothing_stop_episode = math.inf
else:
self.smoothing_stop_episode = int(
smoothing_params["num_episodes"]
* smoothing_params["stop_after"]
)
self.smoothing_start_episode = int(
smoothing_params["num_episodes"]
* smoothing_params["start_after"]
)
num_smoothing_increase_steps = (
self.smoothing_stop_episode - self.smoothing_start_episode
)
self.smoothing_alpha = 0
self.smoothing_increase_step = (
smoothing_params["final_alpha"] / num_smoothing_increase_steps
)
self.smoothed_action = None
self.episode_count = 0
@staticmethod
def compute_reward(previous_observation, observation):
# calculate first reward term
current_distance_from_block = np.linalg.norm(
observation["robot_tip_positions"] - observation["object_position"]
)
previous_distance_from_block = np.linalg.norm(
previous_observation["robot_tip_positions"]
- previous_observation["object_position"]
)
reward_term_1 = (
previous_distance_from_block - current_distance_from_block
)
# calculate second reward term
current_dist_to_goal = np.linalg.norm(
observation["goal_object_position"]
- observation["object_position"]
)
previous_dist_to_goal = np.linalg.norm(
previous_observation["goal_object_position"]
- previous_observation["object_position"]
)
reward_term_2 = previous_dist_to_goal - current_dist_to_goal
reward = 500 * reward_term_1 + 500 * reward_term_2
return reward
def step(self, action):
"""Run one timestep of the environment's dynamics.
When end of episode is reached, you are responsible for calling
``reset()`` to reset this environment's state.
Args:
action: An action provided by the agent (depends on the selected
:class:`ActionType`).
Returns:
tuple:
- observation (dict): agent's observation of the current
environment.
- reward (float) : amount of reward returned after previous action.
- done (bool): whether the episode has ended, in which case further
step() calls will return undefined results.
- info (dict): info dictionary containing the difficulty level of
the goal.
"""
if self.platform is None:
raise RuntimeError("Call `reset()` before starting to step.")
if not self.action_space.contains(action):
raise ValueError(
"Given action is not contained in the action space."
)
num_steps = self.frameskip
# ensure episode length is not exceeded due to frameskip
step_count_after = self.step_count + num_steps
if step_count_after > move_cube.episode_length:
excess = step_count_after - move_cube.episode_length
num_steps = max(1, num_steps - excess)
unscaled_action = self.unscale_action(action, self.unscaled_action_space)
if self.smoothed_action is None:
# start with current position
# self.smoothed_action = self.finger.observation.position
self.smoothed_action = unscaled_action
self.smoothed_action = (
self.smoothing_alpha * self.smoothed_action
+ (1 - self.smoothing_alpha) * action
)
reward = 0.0
for _ in range(num_steps):
self.step_count += 1
if self.step_count > move_cube.episode_length:
raise RuntimeError("Exceeded number of steps for one episode.")
# send action to robot
robot_action = self._gym_action_to_robot_action(self.smoothed_action)
t = self.platform.append_desired_action(robot_action)
# Use observations of step t + 1 to follow what would be expected
# in a typical gym environment. Note that on the real robot, this
# will not be possible
previous_observation = self._create_observation(t)
observation = self._create_observation(t + 1)
reward += self.compute_reward(
previous_observation=previous_observation["observation"],
observation=observation["observation"],
)
is_done = self.step_count == move_cube.episode_length
return observation, reward, is_done, self.info
def reset(self):
# reset simulation
del self.platform
# initialize simulation
initial_robot_position = (
TriFingerPlatform.spaces.robot_position.default
)
initial_object_pose = self.initializer.get_initial_state()
goal_object_pose = self.initializer.get_goal()
self.platform = TriFingerPlatform(
visualization=self.visualization,
initial_robot_position=initial_robot_position,
initial_object_pose=initial_object_pose,
)
self.goal = {
"position": goal_object_pose.position,
"orientation": goal_object_pose.orientation,
}
# updates smoothing parameters
self.update_smoothing()
self.episode_count += 1
self.smoothed_action = None
# visualize the goal
if self.visualization:
self.goal_marker = visual_objects.CubeMarker(
width=0.065,
position=goal_object_pose.position,
orientation=goal_object_pose.orientation,
physicsClientId=self.platform.simfinger._pybullet_client_id,
)
self.info = {"difficulty": self.initializer.difficulty}
self.step_count = 0
return self._create_observation(0)
def seed(self, seed=None):
"""Sets the seed for this env’s random number generator.
.. note::
Spaces need to be seeded separately. E.g. if you want to sample
actions directly from the action space using
``env.action_space.sample()`` you can set a seed there using
``env.action_space.seed()``.
Returns:
List of seeds used by this environment. This environment only uses
a single seed, so the list contains only one element.
"""
self.np_random, seed = gym.utils.seeding.np_random(seed)
move_cube.random = self.np_random
return [seed]
def _create_observation(self, t):
robot_observation = self.platform.get_robot_observation(t)
object_observation = self.platform.get_object_pose(t)
robot_tip_positions = self.platform.forward_kinematics(
robot_observation.position
)
robot_tip_positions = np.array(robot_tip_positions)
observation = {
"observation": {
"robot_position": robot_observation.position,
"robot_velocity": robot_observation.velocity,
"robot_tip_positions": robot_tip_positions,
"object_position": object_observation.position,
"object_orientation": object_observation.orientation,
"goal_object_position": self.goal["position"],
},
"desired_goal": self.goal,
"achieved_goal": {
"position": object_observation.position,
"orientation": object_observation.orientation,
},
}
return observation
def _gym_action_to_robot_action(self, gym_action):
# construct robot action depending on action type
if self.action_type == ActionType.TORQUE:
robot_action = self.platform.Action(torque=gym_action)
elif self.action_type == ActionType.POSITION:
robot_action = self.platform.Action(position=gym_action)
elif self.action_type == ActionType.TORQUE_AND_POSITION:
robot_action = self.platform.Action(
torque=gym_action["torque"], position=gym_action["position"]
)
else:
raise ValueError("Invalid action_type")
return robot_action
def update_smoothing(self):
"""
Update the smoothing coefficient with which the action to be
applied is smoothed
"""
if (
self.smoothing_start_episode
<= self.episode_count
< self.smoothing_stop_episode
):
self.smoothing_alpha += self.smoothing_increase_step
print(
"episode: {}, smoothing: {}".format(
self.episode_count, self.smoothing_alpha
)
)
@staticmethod
def unscale_action(y, space):
"""
Unscale some input from [-1;1] to the range of another space
"""
return space.low + (y + 1.0) / 2.0 * (space.high - space.low)
class CubeEnv(gym.GoalEnv):
"""Gym environment for moving cubes with simulated TriFingerPro."""
def __init__(
self,
initializer,
action_type=ActionType.POSITION,
frameskip=1,
visualization=False,
):
"""Initialize.
Args:
initializer: Initializer class for providing initial cube pose and
goal pose. See :class:`RandomInitializer` and
:class:`FixedInitializer`.
action_type (ActionType): Specify which type of actions to use.
See :class:`ActionType` for details.
frameskip (int): Number of actual control steps to be performed in
one call of step().
visualization (bool): If true, the pyBullet GUI is run for
visualization.
"""
# Basic initialization
# ====================
self.initializer = initializer
self.action_type = action_type
self.visualization = visualization
# TODO: The name "frameskip" makes sense for an atari environment but
# not really for our scenario. The name is also misleading as
# "frameskip = 1" suggests that one frame is skipped while it actually
# means "do one step per step" (i.e. no skip).
if frameskip < 1:
raise ValueError("frameskip cannot be less than 1.")
self.frameskip = frameskip
# will be initialized in reset()
self.platform = None
# Create the action and observation spaces
# ========================================
spaces = TriFingerPlatform.spaces
object_state_space = gym.spaces.Dict(
{
"position": spaces.object_position.gym,
"orientation": spaces.object_orientation.gym,
}
)
if self.action_type == ActionType.TORQUE:
self.action_space = spaces.robot_torque.gym
elif self.action_type == ActionType.POSITION:
self.action_space = spaces.robot_position.gym
elif self.action_type == ActionType.TORQUE_AND_POSITION:
self.action_space = gym.spaces.Dict(
{
"torque": spaces.robot_torque.gym,
"position": spaces.robot_position.gym,
}
)
else:
raise ValueError("Invalid action_type")
self.observation_space = gym.spaces.Dict(
{
"observation": gym.spaces.Dict(
{
"position": spaces.robot_position.gym,
"velocity": spaces.robot_velocity.gym,
"torque": spaces.robot_torque.gym,
}
),
"desired_goal": object_state_space,
"achieved_goal": object_state_space,
}
)
def compute_reward(self, achieved_goal, desired_goal, info):
"""Compute the reward for the given achieved and desired goal.
Args:
achieved_goal (dict): Current pose of the object.
desired_goal (dict): Goal pose of the object.
info (dict): An info dictionary containing a field "difficulty"
which specifies the difficulty level.
Returns:
float: The reward that corresponds to the provided achieved goal
w.r.t. to the desired goal. Note that the following should always
hold true::
ob, reward, done, info = env.step()
assert reward == env.compute_reward(
ob['achieved_goal'],
ob['desired_goal'],
info,
)
"""
return -move_cube.evaluate_state(
move_cube.Pose.from_dict(desired_goal),
move_cube.Pose.from_dict(achieved_goal),
info["difficulty"],
)
def step(self, action):
"""Run one timestep of the environment's dynamics.
When end of episode is reached, you are responsible for calling
``reset()`` to reset this environment's state.
Args:
action: An action provided by the agent (depends on the selected
:class:`ActionType`).
Returns:
tuple:
- observation (dict): agent's observation of the current
environment.
- reward (float) : amount of reward returned after previous action.
- done (bool): whether the episode has ended, in which case further
step() calls will return undefined results.
- info (dict): info dictionary containing the difficulty level of
the goal.
"""
if self.platform is None:
raise RuntimeError("Call `reset()` before starting to step.")
if not self.action_space.contains(action):
raise ValueError(
"Given action is not contained in the action space."
)
num_steps = self.frameskip
# ensure episode length is not exceeded due to frameskip
step_count_after = self.step_count + num_steps
if step_count_after > move_cube.episode_length:
excess = step_count_after - move_cube.episode_length
num_steps = max(1, num_steps - excess)
reward = 0.0
for _ in range(num_steps):
self.step_count += 1
if self.step_count > move_cube.episode_length:
raise RuntimeError("Exceeded number of steps for one episode.")
# send action to robot
robot_action = self._gym_action_to_robot_action(action)
t = self.platform.append_desired_action(robot_action)
# Use observations of step t + 1 to follow what would be expected
# in a typical gym environment. Note that on the real robot, this
# will not be possible
observation = self._create_observation(t + 1)
reward += self.compute_reward(
observation["achieved_goal"],
observation["desired_goal"],
self.info,
)
is_done = self.step_count == move_cube.episode_length
return observation, reward, is_done, self.info
def reset(self):
# reset simulation
del self.platform
# initialize simulation
initial_robot_position = (
TriFingerPlatform.spaces.robot_position.default
)
initial_object_pose = self.initializer.get_initial_state()
goal_object_pose = self.initializer.get_goal()
self.platform = TriFingerPlatform(
visualization=self.visualization,
initial_robot_position=initial_robot_position,
initial_object_pose=initial_object_pose,
)
self.goal = {
"position": goal_object_pose.position,
"orientation": goal_object_pose.orientation,
}
# visualize the goal
if self.visualization:
self.goal_marker = visual_objects.CubeMarker(
width=0.065,
position=goal_object_pose.position,
orientation=goal_object_pose.orientation,
physicsClientId=self.platform.simfinger._pybullet_client_id,
)
self.info = {"difficulty": self.initializer.difficulty}
self.step_count = 0
return self._create_observation(0)
def seed(self, seed=None):
"""Sets the seed for this env’s random number generator.
.. note::
Spaces need to be seeded separately. E.g. if you want to sample
actions directly from the action space using
``env.action_space.sample()`` you can set a seed there using
``env.action_space.seed()``.
Returns:
List of seeds used by this environment. This environment only uses
a single seed, so the list contains only one element.
"""
self.np_random, seed = gym.utils.seeding.np_random(seed)
move_cube.random = self.np_random
return [seed]
def _create_observation(self, t):
robot_observation = self.platform.get_robot_observation(t)
object_observation = self.platform.get_object_pose(t)
observation = {
"observation": {
"position": robot_observation.position,
"velocity": robot_observation.velocity,
"torque": robot_observation.torque,
},
"desired_goal": self.goal,
"achieved_goal": {
"position": object_observation.position,
"orientation": object_observation.orientation,
},
}
return observation
def _gym_action_to_robot_action(self, gym_action):
# construct robot action depending on action type
if self.action_type == ActionType.TORQUE:
robot_action = self.platform.Action(torque=gym_action)
elif self.action_type == ActionType.POSITION:
robot_action = self.platform.Action(position=gym_action)
elif self.action_type == ActionType.TORQUE_AND_POSITION:
robot_action = self.platform.Action(
torque=gym_action["torque"], position=gym_action["position"]
)
else:
raise ValueError("Invalid action_type")
return robot_action
def test_get_camera_observation_timeindex(self):
platform = TriFingerPlatform(enable_cameras=True)
# negative time index needs to be rejected
with self.assertRaises(ValueError):
platform.get_camera_observation(-1)
t = platform.append_desired_action(platform.Action())
try:
platform.get_camera_observation(t)
platform.get_camera_observation(t + 1)
except Exception:
self.fail()
with self.assertRaises(ValueError):
platform.get_camera_observation(t + 2)
def test_get_object_pose_timeindex(self):
platform = TriFingerPlatform()
# negative time index needs to be rejected
with self.assertRaises(ValueError):
platform.get_object_pose(-1)
t = platform.append_desired_action(platform.Action())
try:
platform.get_object_pose(t)
platform.get_object_pose(t + 1)
except Exception:
self.fail()
with self.assertRaises(ValueError):
platform.get_object_pose(t + 2)
def test_timestamps(self):
platform = TriFingerPlatform(visualization=False, enable_cameras=True)
action = platform.Action()
# compute camera update step interval based on configured rates
camera_update_step_interval = (
1 / platform._camera_rate_fps) / platform._time_step
# robot time step in milliseconds
time_step_ms = platform._time_step * 1000
# First time step
t = platform.append_desired_action(action)
first_stamp_ms = platform.get_timestamp_ms(t)
first_stamp_s = first_stamp_ms / 1000
object_pose = platform.get_object_pose(t)
camera_obs = platform.get_camera_observation(t)
self.assertEqual(first_stamp_ms, object_pose.timestamp)
self.assertEqual(first_stamp_ms, camera_obs.cameras[0].timestamp)
self.assertEqual(first_stamp_ms, camera_obs.cameras[1].timestamp)
self.assertEqual(first_stamp_ms, camera_obs.cameras[2].timestamp)
# Test time stamps of observations t+1
object_pose_next = platform.get_object_pose(t + 1)
camera_obs_next = platform.get_camera_observation(t + 1)
next_stamp_ms = first_stamp_ms + time_step_ms
self.assertEqual(next_stamp_ms, object_pose_next.timestamp)
self.assertEqual(next_stamp_ms, camera_obs_next.cameras[0].timestamp)
self.assertEqual(next_stamp_ms, camera_obs_next.cameras[1].timestamp)
self.assertEqual(next_stamp_ms, camera_obs_next.cameras[2].timestamp)
# XXX ===============================================================
# The following part of the test is disabled as currently everything is
# updated in each step (i.e. no lower update rate for camera and
# object).
return
# Second time step
t = platform.append_desired_action(action)
second_stamp_ms = platform.get_timestamp_ms(t)
self.assertEqual(second_stamp_ms, first_stamp_ms + time_step_ms)
# there should not be a new camera observation yet
object_pose = platform.get_object_pose(t)
camera_obs = platform.get_camera_observation(t)
self.assertEqual(first_stamp_s, object_pose.timestamp)
self.assertEqual(first_stamp_s, camera_obs.cameras[0].timestamp)
self.assertEqual(first_stamp_s, camera_obs.cameras[1].timestamp)
self.assertEqual(first_stamp_s, camera_obs.cameras[2].timestamp)
# do several steps until a new camera/object update is expected
for _ in range(int(camera_update_step_interval)):
t = platform.append_desired_action(action)
nth_stamp_ms = platform.get_timestamp_ms(t)
nth_stamp_s = nth_stamp_ms / 1000
self.assertGreater(nth_stamp_ms, second_stamp_ms)
object_pose = platform.get_object_pose(t)
camera_obs = platform.get_camera_observation(t)
self.assertEqual(nth_stamp_s, object_pose.timestamp)
self.assertEqual(nth_stamp_s, camera_obs.cameras[0].timestamp)
self.assertEqual(nth_stamp_s, camera_obs.cameras[1].timestamp)
self.assertEqual(nth_stamp_s, camera_obs.cameras[2].timestamp)