class DummyEnvParameter:
a: int = build_randomizable_param(0, low=-5, high=5)
b: float = build_randomizable_param(0.0, low=-1.0, high=1.0)
x: int = 0 # Non randomizable parameter.
nested: DummyNestedEnvParameter = DummyNestedEnvParameter()
class DominosRearrangeSimParameters(RearrangeSimParameters):
# How "skewed" the domino is. A bigger value corresponds to a thinner and taller domino.
domino_eccentricity: float = build_randomizable_param(1.5,
low=1.0,
high=4.5)
# The proportion compared to `object_size` to distance the dominos from each other
domino_distance_mul: float = build_randomizable_param(4, low=2.0, high=5.0)
num_objects: int = build_randomizable_param(5, low=1, high=8)
class HoldoutRearrangeSimParameters(RearrangeSimParameters):
scene_object_configs: List[SceneObjectConfig] = attr.ib(
default=[], converter=convert_scene_object_config)
task_object_configs: List[TaskObjectConfig] = attr.ib(
default=[TaskObjectConfig(xml_path="object/tests/block.xml")],
converter=convert_task_object_config,
)
shared_settings: str = attr.ib(default="", converter=str)
num_objects: int = build_randomizable_param(low=1, high=32)
@num_objects.default
def default_num_objects(self):
return self._num_objects()
@num_objects.validator
def validate_num_objects(self, _, value):
if value > self._num_objects():
raise ValueError(
f"Too many objects {value}, only {self._num_objects()} "
f"objects are defined in config")
def _num_objects(self):
return sum(o.count for o in self.task_object_configs)
class RearrangeRobotControlParameters(RobotControlParameters):
""" Robot control parameters – defined as parameters since max_position_change can be
subject to ADR randomization """
# refer to RobotControlParameters.default_max_pos_change_for_solver to set a
# reasonable default here.
max_position_change: float = build_randomizable_param(0.1, low=0.01, high=2.5)
class RearrangeEnvParameters(RobotEnvParameters):
robot_control_params: RearrangeRobotControlParameters = build_nested_attr(
RearrangeRobotControlParameters
)
simulation_params: RearrangeSimParameters = build_nested_attr(
RearrangeSimParameters
)
# If true, turn on debugging features like visualizing bounding boxes.
debug: bool = False
# the range of object rescaling ratios (in log scale)
object_scale_low: float = build_randomizable_param(0.0, low=0.0, high=0.5)
object_scale_high: float = build_randomizable_param(0.0, low=0.0, high=1.8)
# List of materials available for this environment. If multiple materials are specified
# we will randomly select material from the list for each object.
# If the list is None, all materials under materials folder will be considered.
# If the list is empty, no material specific mujoco args will be applied.
material_names: Optional[List[str]] = ["default"]
class ComposerRearrangeSimParameters(RearrangeSimParameters):
# Overwrite the object group config type.
object_groups: List[ComposerObjectGroupConfig] = None # type: ignore
def __attrs_post_init__(self):
if self.object_groups is None:
self.object_groups = [
ComposerObjectGroupConfig(count=1, object_ids=[obj_id])
for obj_id in range(self.num_objects)
]
# Num. of geoms to use to compose one object.
num_max_geoms: int = build_randomizable_param(1, low=1, high=20)
# The type of object composer to be used.
composer: Composer = attr.ib( # type: ignore
default="mixed",
converter=composer_converter, # type: ignore
)
class RearrangeSimParameters(SimulationParameters):
num_objects: int = build_randomizable_param(1, low=1, high=32)
# The object size in half-size (as per Mujoco convention).
object_size: float = build_randomizable_param(0.0254, low=0.01, high=0.1)
# If not set, we use num_objects as max_num_objects.
max_num_objects: Optional[int] = None
# Max percent of table area to use.
used_table_portion: float = build_randomizable_param(1.0,
low=0.4,
high=1.0)
# Max number of times for placement retry. To place multiple objects, placement algorithm
# place each object one by one. Whenever placing a new object is failed the placement
# algorithm retry `max_placement_retry_per_object` times and if it still fails it start over
# from the first object. The placement can start over for `max_placement_retry` times.
max_placement_retry: int = 100
max_placement_retry_per_object: int = 20
# The `goal_distance_ratio` is used to decrease distance from object position to goal position
# after uniformly sampling the goal. The decreased distance will be `goal_distance_ratio *
# original_distance`. The distance cannot be decreased below `goal_distance_min`.
goal_distance_ratio: float = build_randomizable_param(1.0,
low=0.0,
high=1.0)
goal_distance_min: float = 0.06
# The offset put on the relative positional distance between target and object. This can be
# used to make the success threshold for object position different less strict. By adding
# negative offset to relative positional distance, we can enjoy the effect of increasing the
# success threshold by -goal_pos_offset.
goal_pos_offset: float = build_randomizable_param(0.0, low=-0.04, high=0.0)
# The weight put on the relative rotational distance between target and object. This can
# be used to smoothly interpolate between not caring about rotational goals (when value = 0)
# to fully caring about them (when value = 1).
goal_rot_weight: float = build_randomizable_param(1.0, low=0.0, high=1.0)
# Height of reach target object above the table, curriculum from high to low
# helps policy learn to handle safety plane in z.
target_height: float = build_randomizable_param(0.1, low=0.0, high=0.2)
# Collection of penalties to impose on sim
# Penalties are defined as positive numbers and are subtracted from
# the reward.
penalty: dict = dict(table_collision=0.0,
objects_off_table=1.0,
wrist_collision=0.0)
# for camera view randomization (fov, pos and quat)
camera_fovy_radius: float = build_randomizable_param(0.0, low=0, high=0.1)
camera_pos_radius: float = build_randomizable_param(0.0, low=0, high=0.01)
# about 5 degrees
camera_quat_radius: float = build_randomizable_param(0.0, low=0, high=0.09)
# Whether we should cast shadows. This is disabled by default because it slows down rendering
# signficantly when using envs with custom meshes. We enable it for sim2real experiments.
cast_shadows: bool = False
# Lighting randomizations.
light_pos_range: float = build_randomizable_param(0.0, low=0.0, high=0.8)
light_ambient_intensity: float = build_randomizable_param(0.1,
low=0.1,
high=0.7)
light_diffuse_intensity: float = build_randomizable_param(0.4,
low=0.1,
high=0.7)
# Objects assigned with the same group id should be visually same (same shape, size, color).
# We always assume group id starting from 0 and new ones are assigned incrementally.
object_groups: List[ObjectGroupConfig] = None # type: ignore
def __attrs_post_init__(self):
# Assign default value to object_groups if empty. Hard to use @object_groups.default here
# as it depends on self.num_objects and the order is messed up in different subclasses.
if self.object_groups is None:
self.object_groups = [
ObjectGroupConfig(count=1, object_ids=[obj_id])
for obj_id in range(self.num_objects)
]
class DummyNestedEnvParameter:
c: int = build_randomizable_param(1, low=-3, high=3)