def generate_test_set(levels: typing.List[int], samples_per_level: int,
logfile_tmpl: str) -> typing.List[TestSample]:
"""Generate random test set for policy evaluation.
For each difficulty level a list of samples, consisting of randomized
initial pose and goal pose, is generated.
Args:
levels: List of difficulty levels.
samples_per_level: How many samples are generated per level.
logfile_tmpl: Format string for the log file associated with this
sample. Needs to contain placeholders "{level}" and "{iteration}".
Returns:
List of ``len(levels) * samples_per_level`` test samples.
"""
samples = []
for level in levels:
for i in range(samples_per_level):
init = move_cube.sample_goal(-1)
goal = move_cube.sample_goal(level)
logfile = logfile_tmpl.format(level=level, iteration=i)
samples.append(
TestSample(level, i, init.to_json(), goal.to_json(), logfile))
return samples
def generate_eval_episodes(num_episodes):
'''genereate code/eval_episodes/level[n] directories that contain evaluation episodes.'''
for level in range(1, 4 + 1):
eval_dir = os.environ['RRC_SIM_ROOT'] + '/code/eval_episodes/level{}'.format(level)
if not os.path.isdir(eval_dir):
os.makedirs(eval_dir)
for i in range(num_episodes):
# sample init_pos, goal and convert it to json
init = sample_goal(-1)
goal = sample_goal(level)
with open(os.path.join(eval_dir, '{:05d}-init.json'.format(i)), 'w') as f:
f.write(init.to_json())
with open(os.path.join(eval_dir, '{:05d}-goal.json'.format(i)), 'w') as f:
f.write(goal.to_json())
else:
print('directory {} already exists. skipping...'.format(eval_dir))
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 test_sample_goal_difficulty_4_no_initial_pose(self):
for i in range(1000):
goal = move_cube.sample_goal(difficulty=4)
# verify the goal is valid (i.e. within the allowed ranges)
try:
move_cube.validate_goal(goal)
except move_cube.InvalidGoalError as e:
self.fail(msg="Invalid goal: {} pose is {}, {}".format(
e, e.position, e.orientation), )
# verify the goal satisfies conditions of difficulty 2
self.assertLessEqual(goal.position[2], move_cube._max_height)
self.assertGreaterEqual(goal.position[2], move_cube._min_height)
def test_sample_goal_difficulty_1_no_initial_pose(self):
for i in range(1000):
goal = move_cube.sample_goal(difficulty=1)
# verify the goal is valid (i.e. within the allowed ranges)
try:
move_cube.validate_goal(goal)
except move_cube.InvalidGoalError as e:
self.fail(msg="Invalid goal: {} pose is {}, {}".format(
e, e.position, e.orientation), )
# verify the goal satisfies conditions of difficulty 1
# always on ground
self.assertEqual(goal.position[2], move_cube._CUBE_WIDTH / 2)
# no orientation
np.testing.assert_array_equal(goal.orientation, [0, 0, 0, 1])
def get_goal(self):
"""Get a random goal depending on the difficulty."""
self.difficulty = np.random.choice(self.difficulties)
return move_cube.sample_goal(difficulty=self.difficulty)
def get_initial_state(self):
"""Get a random initial object pose (always on the ground)."""
return move_cube.sample_goal(difficulty=-1)
def get_goal(self):
"""Get a random goal depending on the difficulty."""
self.difficulty = random.randrange(1, 5)
return move_cube.sample_goal(difficulty=self.difficulty )
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
