def step(self, action, action_dot):
self._last_base_position = self.minitaur.GetTrueBasePosition()
if self._is_render:
time_spent = time.time() - self._last_frame_time
self._last_frame_time = time.time()
time_to_sleep = self.control_time_step - time_spent
if time_to_sleep > 0:
time.sleep(time_to_sleep)
base_pos = self.minitaur.GetTrueBasePosition()
[yaw, pitch,
dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]
self._pybullet_client.resetDebugVisualizerCamera(
dist, yaw, pitch, base_pos)
for env_randomizer in self._env_randomizers:
env_randomizer.randomize_step(self)
self.minitaur.Step(action, action_dot)
self._get_true_observation()
done = self._termination()
if self._log_path is not None:
minitaur_logging.update_episode_proto(self._episode_proto,
self.minitaur, action,
self._env_step_counter)
self._env_step_counter += 1
if done:
self.minitaur.Terminate()
return np.array(self._get_true_observation())
def _step_old_robot_class(self, action):
self._last_base_position = self._robot.GetBasePosition()
self._last_action = action
if self._is_render:
self._wait_for_rendering()
for env_randomizer in self._env_randomizers:
env_randomizer.randomize_step(self)
self._robot.Step(action)
if self._profiling_slot >= 0:
self._profiling_counter -= 1
if self._profiling_counter == 0:
self._pybullet_client.stopStateLogging(self._profiling_slot)
if self._log_path is not None:
minitaur_logging.update_episode_proto(self._episode_proto,
self._robot, action,
self._env_step_counter)
reward = self._reward()
for s in self.all_sensors():
s.on_step(self)
if self._task and hasattr(self._task, 'update'):
self._task.update(self) # TODO(b/154635313): resolve API mismatch
done = self._termination()
self._env_step_counter += 1
# TODO(b/161941829): terminate removed for now, change terminate to other
# names.
return self._get_observation(), reward, done, {}
def _step_new_robot_class(self, action):
self._last_base_position = self._robot.base_position
self._last_action = action
if self._is_render:
self._wait_for_rendering()
for env_randomizer in self._env_randomizers:
env_randomizer.randomize_step(self)
action = self._robot.pre_control_step(action, self._env_time_step)
for obj in self._dynamic_objects():
obj.pre_control_step(autonomous_object.AUTONOMOUS_ACTION)
for _ in range(self._num_action_repeat):
for env_randomizer in self._env_randomizers:
env_randomizer.randomize_sub_step(self, i,
self._num_action_repeat)
self._robot.apply_action(action)
for obj in self._dynamic_objects():
obj.update(self.get_time_since_reset(), self._observation_dict)
obj.apply_action(autonomous_object.AUTONOMOUS_ACTION)
self._pybullet_client.stepSimulation()
self._sim_step_counter += 1
self._robot.receive_observation()
for obj in self._dynamic_objects():
obj.receive_observation()
for s in self.all_sensors():
s.on_new_observation()
self._robot.post_control_step()
for obj in self._dynamic_objects():
obj.post_control_step()
if self._profiling_slot >= 0:
self._profiling_counter -= 1
if self._profiling_counter == 0:
self._pybullet_client.stopStateLogging(self._profiling_slot)
if self._log_path is not None:
minitaur_logging.update_episode_proto(self._episode_proto,
self._robot, action,
self._env_step_counter)
reward = self._reward()
for s in self.all_sensors():
s.on_step(self)
if self._task and hasattr(self._task, 'update'):
self._task.update(self) # TODO(b/154635313): resolve API mismatch
done = self._termination()
self._env_step_counter += 1
# TODO(b/161941829): terminate removed for now, change terminate to other
# names.
return self._get_observation(), reward, done, {}
def step(self, action):
"""Step forward the simulation, given the action.
Args:
action: A list of desired motor angles for eight motors.
Returns:
observations: The angles, velocities and torques of all motors.
reward: The reward for the current state-action pair.
done: Whether the episode has ended.
info: A dictionary that stores diagnostic information.
Raises:
ValueError: The action dimension is not the same as the number of motors.
ValueError: The magnitude of actions is out of bounds.
:param control_m:
"""
self._last_base_position = self.minitaur.GetBasePosition()
if self._is_render:
# Sleep, otherwise the computation takes less time than real time,
# which will make the visualization like a fast-forward video.
time_spent = time.time() - self._last_frame_time
self._last_frame_time = time.time()
time_to_sleep = self.control_time_step - time_spent
if time_to_sleep > 0:
time.sleep(time_to_sleep)
base_pos = self.minitaur.GetBasePosition()
# Keep the previous orientation of the camera set by the user.
[yaw, pitch,
dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]
self._pybullet_client.resetDebugVisualizerCamera(
dist, yaw, pitch, base_pos)
for env_randomizer in self._env_randomizers:
env_randomizer.randomize_step(self)
action = self._transform_action_to_motor_command(action)
self.minitaur.Step(action)
reward = self._reward()
done = self._termination()
if self._log_path is not None:
minitaur_logging.update_episode_proto(self._episode_proto,
self.minitaur, action,
self._env_step_counter)
self._env_step_counter += 1
if done:
self.minitaur.Terminate()
return np.array(self._get_observation()), reward, done, {}
def _step(self, action):
"""Step forward the simulation, given the action.
Args:
action: A list of desired motor angles for eight motors.
Returns:
observations: The angles, velocities and torques of all motors.
reward: The reward for the current state-action pair.
done: Whether the episode has ended.
info: A dictionary that stores diagnostic information.
Raises:
ValueError: The action dimension is not the same as the number of motors.
ValueError: The magnitude of actions is out of bounds.
"""
self._last_base_position = self.minitaur.GetBasePosition()
if self._is_render:
# Sleep, otherwise the computation takes less time than real time,
# which will make the visualization like a fast-forward video.
time_spent = time.time() - self._last_frame_time
self._last_frame_time = time.time()
time_to_sleep = self.control_time_step - time_spent
if time_to_sleep > 0:
time.sleep(time_to_sleep)
base_pos = self.minitaur.GetBasePosition()
# Keep the previous orientation of the camera set by the user.
[yaw, pitch,
dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]
self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch,
base_pos)
for env_randomizer in self._env_randomizers:
env_randomizer.randomize_step(self)
action = self._transform_action_to_motor_command(action)
self.minitaur.Step(action)
reward = self._reward()
done = self._termination()
if self._log_path is not None:
minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur,
action, self._env_step_counter)
self._env_step_counter += 1
if done:
self.minitaur.Terminate()
return np.array(self._get_observation()), reward, done, {}
def _step(self, action):
"""Step forward the simulation, given the action.
Args:
action: A list of desired motor angles for eight motors.
Returns:
observations: Roll, pitch of the base, and roll, pitch rate.
reward: The reward for the current state-action pair.
done: Whether the episode has ended.
info: A dictionary that stores diagnostic information.
Raises:
ValueError: The action dimension is not the same as the number of motors.
ValueError: The magnitude of actions is out of bounds.
"""
if self._is_render:
# Sleep, otherwise the computation takes less time than real time,
# which will make the visualization like a fast-forward video.
time_spent = time.time() - self._last_frame_time
self._last_frame_time = time.time()
time_to_sleep = self.control_time_step - time_spent
if time_to_sleep > 0:
time.sleep(time_to_sleep)
base_pos = self.minitaur.GetBasePosition()
# Keep the previous orientation of the camera set by the user.
[yaw, pitch,
dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]
self._pybullet_client.resetDebugVisualizerCamera(
dist, yaw, pitch, base_pos)
action = self._transform_action_to_motor_command(action)
t = self._env_step_counter % MOVING_FLOOR_TOTAL_STEP
if t == 0:
self._seed()
orientation_x = random.uniform(-0.2, 0.2)
self._seed()
orientation_y = random.uniform(-0.2, 0.2)
_, self._cur_ori = self._pybullet_client.getBasePositionAndOrientation(
0)
self._goal_ori = self._pybullet_client.getQuaternionFromEuler(
[orientation_x, orientation_y, 0])
t = float(float(t) / float(MOVING_FLOOR_TOTAL_STEP))
ori = map(operator.add, [x * (1.0 - t) for x in self._cur_ori],
[x * t for x in self._goal_ori])
ori = list(ori)
print("ori=", ori)
self._pybullet_client.resetBasePositionAndOrientation(
0, [0, 0, 0], ori)
if self._env_step_counter % PERTURBATION_TOTAL_STEP == 0:
self._perturbation_magnitude = random.uniform(0.0, 0.0)
if self._sign < 0.5:
self._sign = 1.0
else:
self._sign = 0.0
self._pybullet_client.applyExternalForce(
objectUniqueId=1,
linkIndex=-1,
forceObj=[self._sign * self._perturbation_magnitude, 0.0, 0.0],
posObj=[0.0, 0.0, 0.0],
flags=self._pybullet_client.LINK_FRAME)
self.minitaur.Step(action)
self._env_step_counter += 1
done = self._termination()
obs = self._get_true_observation()
reward = self._reward(action, obs)
if self._log_path is not None:
minitaur_logging.update_episode_proto(self._episode_proto,
self.minitaur, action,
self._env_step_counter)
if done:
self.minitaur.Terminate()
return np.array(self._get_observation()), reward, done, {}
def step(self, action):
"""Step forward the simulation, given the action.
Args:
action: A list of desired motor angles for eight motors.
Returns:
observations: Roll, pitch of the base, and roll, pitch rate.
reward: The reward for the current state-action pair.
done: Whether the episode has ended.
info: A dictionary that stores diagnostic information.
Raises:
ValueError: The action dimension is not the same as the number of motors.
ValueError: The magnitude of actions is out of bounds.
"""
if self._is_render:
# Sleep, otherwise the computation takes less time than real time,
# which will make the visualization like a fast-forward video.
time_spent = time.time() - self._last_frame_time
self._last_frame_time = time.time()
time_to_sleep = self.control_time_step - time_spent
if time_to_sleep > 0:
time.sleep(time_to_sleep)
base_pos = self.minitaur.GetBasePosition()
# Keep the previous orientation of the camera set by the user.
[yaw, pitch,
dist] = self._pybullet_client.getDebugVisualizerCamera()[8:11]
self._pybullet_client.resetDebugVisualizerCamera(dist, yaw, pitch,
base_pos)
action = self._transform_action_to_motor_command(action)
t = self._env_step_counter % MOVING_FLOOR_TOTAL_STEP
if t == 0:
self.seed()
orientation_x = random.uniform(-0.2, 0.2)
self.seed()
orientation_y = random.uniform(-0.2, 0.2)
_, self._cur_ori = self._pybullet_client.getBasePositionAndOrientation(0)
self._goal_ori = self._pybullet_client.getQuaternionFromEuler(
[orientation_x, orientation_y, 0])
t = float(float(t) / float(MOVING_FLOOR_TOTAL_STEP))
ori = map(operator.add, [x * (1.0 - t) for x in self._cur_ori],
[x * t for x in self._goal_ori])
ori=list(ori)
print("ori=",ori)
self._pybullet_client.resetBasePositionAndOrientation(0, [0, 0, 0], ori)
if self._env_step_counter % PERTURBATION_TOTAL_STEP == 0:
self._perturbation_magnitude = random.uniform(0.0, 0.0)
if self._sign < 0.5:
self._sign = 1.0
else:
self._sign = 0.0
self._pybullet_client.applyExternalForce(
objectUniqueId=1,
linkIndex=-1,
forceObj=[self._sign * self._perturbation_magnitude, 0.0, 0.0],
posObj=[0.0, 0.0, 0.0],
flags=self._pybullet_client.LINK_FRAME)
self.minitaur.Step(action)
self._env_step_counter += 1
done = self._termination()
obs = self._get_true_observation()
reward = self._reward()
if self._log_path is not None:
minitaur_logging.update_episode_proto(self._episode_proto, self.minitaur,
action, self._env_step_counter)
if done:
self.minitaur.Terminate()
return np.array(self._get_observation()), reward, done, {}