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.goal_observation(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
if hasattr(self, 'goal_marker'):
del self.goal_marker
# 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._reset_platform_frontend()
self.goal = {
"position": goal_object_pose.position,
"orientation": goal_object_pose.orientation,
}
# visualize the goal
if self.visualization:
if self.is_level_4:
self.goal_marker = visual_objects.CubeMarker(
width=0.065,
position=goal_object_pose.position,
orientation=goal_object_pose.orientation,
)
self.ori_goal_marker = VisualCubeOrientation(
goal_object_pose.position,
goal_object_pose.orientation
)
else:
self.goal_marker = visual_objects.SphereMaker(
radius=0.065 / 2,
position=goal_object_pose.position,
)
self.info = dict()
self.step_count = 0
observation, _, _, _ = self.step(self._initial_action)
# init_obs = self._create_observation(0)
if self.visualization:
self.ori_marker = VisualCubeOrientation(
init_obs['object_position'],
init_obs['object_orientation']
)
return init_obs