def _load_model(self):
"""
Loads an xml model, puts it in self.model
"""
super()._load_model()
# Verify the correct robot has been loaded
assert isinstance(self.robots[0], SingleArm), \
"Error: Expected one single-armed robot! Got {} type instead.".format(type(self.robots[0]))
# Adjust base pose accordingly
xpos = self.robots[0].robot_model.base_xpos_offset["table"](
self.table_full_size[0])
self.robots[0].robot_model.set_base_xpos(xpos)
# load model for table top workspace
self.mujoco_arena = PegsArena(table_full_size=self.table_full_size,
table_friction=self.table_friction,
table_offset=(0, 0, 0.82))
if self.use_indicator_object:
self.mujoco_arena.add_pos_indicator()
# Arena always gets set to zero origin
self.mujoco_arena.set_origin([0, 0, 0])
# define mujoco objects
self.ob_inits = [SquareNutObject, RoundNutObject]
self.item_names = ["SquareNut", "RoundNut"]
self.item_names_org = list(self.item_names)
self.obj_to_use = (self.item_names[1] + "{}").format(0)
self.ngeoms = [5, 9]
lst = []
for i in range(len(self.ob_inits)):
ob_name = (self.item_names[i] + "0")
ob = self.ob_inits[i](
name=ob_name,
joints=[dict(type="free", damping="0.0005")
], # damp the free joint for each object
)
lst.append((ob_name, ob))
self.mujoco_objects = OrderedDict(lst)
self.n_objects = len(self.mujoco_objects)
# task includes arena, robot, and objects of interest
self.model = ManipulationTask(
mujoco_arena=self.mujoco_arena,
mujoco_robots=[robot.robot_model for robot in self.robots],
mujoco_objects=self.mujoco_objects,
visual_objects=None,
initializer=self.placement_initializer,
)
# set positions of objects
self.model.place_objects()
def _load_model(self):
super()._load_model()
self.mujoco_robot.set_base_xpos([0, 0, 0])
# load model for table top workspace
self.mujoco_arena = PegsArena(table_full_size=self.table_full_size,
table_friction=self.table_friction)
if self.use_indicator_object:
self.mujoco_arena.add_pos_indicator()
# The sawyer robot has a pedestal, we want to align it with the table
self.mujoco_arena.set_origin([.5, -0.15, 0])
# define mujoco objects
self.ob_inits = [SquareNutObject, RoundNutObject]
self.item_names = ["SquareNut", "RoundNut"]
self.item_names_org = list(self.item_names)
self.obj_to_use = (self.item_names[1] + "{}").format(0)
self.ngeoms = [5, 9]
lst = []
for i in range(len(self.ob_inits)):
ob = self.ob_inits[i]()
lst.append((str(self.item_names[i]) + "0", ob))
self.mujoco_objects = OrderedDict(lst)
self.n_objects = len(self.mujoco_objects)
# task includes arena, robot, and objects of interest
self.model = NutAssemblyTask(
self.mujoco_arena,
self.mujoco_robot,
self.mujoco_objects,
self.placement_initializer,
)
self.model.place_objects()
self.table_pos = string_to_array(self.model.table_body.get("pos"))
self.peg1_pos = string_to_array(
self.model.peg1_body.get("pos")) # square
self.peg2_pos = string_to_array(
self.model.peg2_body.get("pos")) # round
class SawyerNutAssembly(SawyerEnv):
"""
This class corresponds to the nut assembly task for the Sawyer robot arm.
"""
def __init__(
self,
gripper_type="TwoFingerGripper",
table_full_size=(0.45, 0.69, 0.82),
table_friction=(1, 0.005, 0.0001),
use_camera_obs=True,
use_object_obs=True,
reward_shaping=False,
placement_initializer=None,
single_object_mode=0,
nut_type=None,
gripper_visualization=False,
use_indicator_object=False,
has_renderer=False,
has_offscreen_renderer=True,
render_collision_mesh=False,
render_visual_mesh=True,
control_freq=10,
horizon=1000,
ignore_done=False,
camera_name="frontview",
camera_height=256,
camera_width=256,
camera_depth=False,
):
"""
Args:
gripper_type (str): type of gripper, used to instantiate
gripper models from gripper factory.
table_full_size (3-tuple): x, y, and z dimensions of the table.
table_friction (3-tuple): the three mujoco friction parameters for
the table.
use_camera_obs (bool): if True, every observation includes a
rendered image.
use_object_obs (bool): if True, include object (cube) information in
the observation.
reward_shaping (bool): if True, use dense rewards.
placement_initializer (ObjectPositionSampler instance): if provided, will
be used to place objects on every reset, else a UniformRandomPegsSampler
is used by default.
single_object_mode (int): specifies which version of the task to do. Note that
the observations change accordingly.
0: corresponds to the full task with both types of nuts.
1: corresponds to an easier task with only one type of nut initialized
on the table with every reset. The type is randomized on every reset.
2: corresponds to an easier task with only one type of nut initialized
on the table with every reset. The type is kept constant and will not
change between resets.
nut_type (string): if provided, should be either "round" or "square". Determines
which type of nut (round or square) will be spawned on every environment
reset. Only used if @single_object_mode is 2.
gripper_visualization (bool): True if using gripper visualization.
Useful for teleoperation.
use_indicator_object (bool): if True, sets up an indicator object that
is useful for debugging.
has_renderer (bool): If true, render the simulation state in
a viewer instead of headless mode.
has_offscreen_renderer (bool): True if using off-screen rendering.
render_collision_mesh (bool): True if rendering collision meshes
in camera. False otherwise.
render_visual_mesh (bool): True if rendering visual meshes
in camera. False otherwise.
control_freq (float): how many control signals to receive
in every second. This sets the amount of simulation time
that passes between every action input.
horizon (int): Every episode lasts for exactly @horizon timesteps.
ignore_done (bool): True if never terminating the environment (ignore @horizon).
camera_name (str): name of camera to be rendered. Must be
set if @use_camera_obs is True.
camera_height (int): height of camera frame.
camera_width (int): width of camera frame.
camera_depth (bool): True if rendering RGB-D, and RGB otherwise.
"""
# task settings
self.single_object_mode = single_object_mode
self.nut_to_id = {"square": 0, "round": 1}
if nut_type is not None:
assert (nut_type in self.nut_to_id.keys()
), "invalid @nut_type argument - choose one of {}".format(
list(self.nut_to_id.keys()))
self.nut_id = self.nut_to_id[
nut_type] # use for convenient indexing
self.obj_to_use = None
# settings for table top
self.table_full_size = table_full_size
self.table_friction = table_friction
# whether to use ground-truth object states
self.use_object_obs = use_object_obs
# reward configuration
self.reward_shaping = reward_shaping
# placement initilizer
if placement_initializer:
self.placement_initializer = placement_initializer
else:
self.placement_initializer = UniformRandomPegsSampler(
x_range=[-0.15, 0.],
y_range=[-0.2, 0.2],
z_range=[0.02, 0.10],
ensure_object_boundary_in_range=False,
z_rotation=True,
)
super().__init__(
gripper_type=gripper_type,
gripper_visualization=gripper_visualization,
use_indicator_object=use_indicator_object,
has_renderer=has_renderer,
has_offscreen_renderer=has_offscreen_renderer,
render_collision_mesh=render_collision_mesh,
render_visual_mesh=render_visual_mesh,
control_freq=control_freq,
horizon=horizon,
ignore_done=ignore_done,
use_camera_obs=use_camera_obs,
camera_name=camera_name,
camera_height=camera_height,
camera_width=camera_width,
camera_depth=camera_depth,
)
def _load_model(self):
super()._load_model()
self.mujoco_robot.set_base_xpos([0, 0, 0])
# load model for table top workspace
self.mujoco_arena = PegsArena(table_full_size=self.table_full_size,
table_friction=self.table_friction)
if self.use_indicator_object:
self.mujoco_arena.add_pos_indicator()
# The sawyer robot has a pedestal, we want to align it with the table
self.mujoco_arena.set_origin([.5, -0.15, 0])
# define mujoco objects
self.ob_inits = [SquareNutObject, RoundNutObject]
self.item_names = ["SquareNut", "RoundNut"]
self.item_names_org = list(self.item_names)
self.obj_to_use = (self.item_names[1] + "{}").format(0)
self.ngeoms = [5, 9]
lst = []
for i in range(len(self.ob_inits)):
ob = self.ob_inits[i]()
lst.append((str(self.item_names[i]) + "0", ob))
self.mujoco_objects = OrderedDict(lst)
self.n_objects = len(self.mujoco_objects)
# task includes arena, robot, and objects of interest
self.model = NutAssemblyTask(
self.mujoco_arena,
self.mujoco_robot,
self.mujoco_objects,
self.placement_initializer,
)
self.model.place_objects()
self.table_pos = string_to_array(self.model.table_body.get("pos"))
self.peg1_pos = string_to_array(
self.model.peg1_body.get("pos")) # square
self.peg2_pos = string_to_array(
self.model.peg2_body.get("pos")) # round
def clear_objects(self, obj):
"""
Clears objects with name @obj out of the task space. This is useful
for supporting task modes with single types of objects, as in
@self.single_object_mode without changing the model definition.
"""
for obj_name, obj_mjcf in self.mujoco_objects.items():
if obj_name == obj:
continue
else:
sim_state = self.sim.get_state()
# print(self.sim.model.get_joint_qpos_addr(obj_name))
sim_state.qpos[self.sim.model.get_joint_qpos_addr(obj_name)
[0]] = 10
self.sim.set_state(sim_state)
self.sim.forward()
def _get_reference(self):
super()._get_reference()
self.obj_body_id = {}
self.obj_geom_id = {}
for i in range(len(self.ob_inits)):
obj_str = str(self.item_names[i]) + "0"
self.obj_body_id[obj_str] = self.sim.model.body_name2id(obj_str)
geom_ids = []
for j in range(self.ngeoms[i]):
geom_ids.append(
self.sim.model.geom_name2id(obj_str + "-{}".format(j)))
self.obj_geom_id[obj_str] = geom_ids
# information of objects
self.object_names = list(self.mujoco_objects.keys())
self.object_site_ids = [
self.sim.model.site_name2id(ob_name)
for ob_name in self.object_names
]
# id of grippers for contact checking
self.finger_names = self.gripper.contact_geoms()
self.l_finger_geom_ids = [
self.sim.model.geom_name2id(x)
for x in self.gripper.left_finger_geoms
]
self.r_finger_geom_ids = [
self.sim.model.geom_name2id(x)
for x in self.gripper.right_finger_geoms
]
# self.sim.data.contact # list, geom1, geom2
self.collision_check_geom_names = self.sim.model._geom_name2id.keys()
self.collision_check_geom_ids = [
self.sim.model._geom_name2id[k]
for k in self.collision_check_geom_names
]
# keep track of which objects are on their corresponding pegs
self.objects_on_pegs = np.zeros(len(self.ob_inits))
def _reset_internal(self):
super()._reset_internal()
# reset positions of objects, and move objects out of the scene depending on the mode
self.model.place_objects()
if self.single_object_mode == 1:
self.obj_to_use = (random.choice(self.item_names) + "{}").format(0)
self.clear_objects(self.obj_to_use)
elif self.single_object_mode == 2:
self.obj_to_use = (self.item_names[self.nut_id] + "{}").format(0)
self.clear_objects(self.obj_to_use)
def reward(self, action=None):
# compute sparse rewards
self._check_success()
reward = np.sum(self.objects_on_pegs)
# add in shaped rewards
if self.reward_shaping:
staged_rewards = self.staged_rewards()
reward += max(staged_rewards)
return reward
def staged_rewards(self):
"""
Returns staged rewards based on current physical states.
Stages consist of reaching, grasping, lifting, and hovering.
"""
reach_mult = 0.1
grasp_mult = 0.35
lift_mult = 0.5
hover_mult = 0.7
# filter out objects that are already on the correct pegs
names_to_reach = []
objs_to_reach = []
geoms_to_grasp = []
geoms_by_array = []
for i in range(len(self.ob_inits)):
if self.objects_on_pegs[i]:
continue
obj_str = str(self.item_names[i]) + "0"
names_to_reach.append(obj_str)
objs_to_reach.append(self.obj_body_id[obj_str])
geoms_to_grasp.extend(self.obj_geom_id[obj_str])
geoms_by_array.append(self.obj_geom_id[obj_str])
### reaching reward governed by distance to closest object ###
r_reach = 0.
if len(objs_to_reach):
# reaching reward via minimum distance to the handles of the objects (the last geom of each nut)
geom_ids = [elem[-1] for elem in geoms_by_array]
target_geom_pos = self.sim.data.geom_xpos[geom_ids]
gripper_site_pos = self.sim.data.site_xpos[self.eef_site_id]
dists = np.linalg.norm(target_geom_pos -
gripper_site_pos.reshape(1, -1),
axis=1)
r_reach = (1 - np.tanh(10.0 * min(dists))) * reach_mult
### grasping reward for touching any objects of interest ###
touch_left_finger = False
touch_right_finger = False
for i in range(self.sim.data.ncon):
c = self.sim.data.contact[i]
if c.geom1 in geoms_to_grasp:
if c.geom2 in self.l_finger_geom_ids:
touch_left_finger = True
if c.geom2 in self.r_finger_geom_ids:
touch_right_finger = True
elif c.geom2 in geoms_to_grasp:
if c.geom1 in self.l_finger_geom_ids:
touch_left_finger = True
if c.geom1 in self.r_finger_geom_ids:
touch_right_finger = True
has_grasp = touch_left_finger and touch_right_finger
r_grasp = int(has_grasp) * grasp_mult
### lifting reward for picking up an object ###
r_lift = 0.
if len(objs_to_reach) and r_grasp > 0.:
z_target = self.table_pos[2] + 0.2
object_z_locs = self.sim.data.body_xpos[objs_to_reach][:, 2]
z_dists = np.maximum(z_target - object_z_locs, 0.)
r_lift = grasp_mult + (1 - np.tanh(15.0 * min(z_dists))) * (
lift_mult - grasp_mult)
### hover reward for getting object above peg ###
r_hover = 0.
if len(objs_to_reach):
r_hovers = np.zeros(len(objs_to_reach))
for i in range(len(objs_to_reach)):
if names_to_reach[i].startswith(self.item_names[0]):
peg_pos = self.peg1_pos[:2]
elif names_to_reach[i].startswith(self.item_names[1]):
peg_pos = self.peg2_pos[:2]
else:
raise Exception("Got invalid object to reach: {}".format(
names_to_reach[i]))
ob_xy = self.sim.data.body_xpos[objs_to_reach[i]][:2]
dist = np.linalg.norm(peg_pos - ob_xy)
r_hovers[i] = r_lift + (1 - np.tanh(10.0 * dist)) * (
hover_mult - lift_mult)
r_hover = np.max(r_hovers)
return r_reach, r_grasp, r_lift, r_hover
def on_peg(self, obj_pos, peg_id):
if peg_id == 0:
peg_pos = self.peg1_pos
else:
peg_pos = self.peg2_pos
res = False
if (abs(obj_pos[0] - peg_pos[0]) < 0.03
and abs(obj_pos[1] - peg_pos[1]) < 0.03
and obj_pos[2] < self.model.table_offset[2] + 0.05):
res = True
return res
def _get_observation(self):
"""
Returns an OrderedDict containing observations [(name_string, np.array), ...].
Important keys:
robot-state: contains robot-centric information.
object-state: requires @self.use_object_obs to be True.
contains object-centric information.
image: requires @self.use_camera_obs to be True.
contains a rendered frame from the simulation.
depth: requires @self.use_camera_obs and @self.camera_depth to be True.
contains a rendered depth map from the simulation
"""
di = super()._get_observation()
if self.use_camera_obs:
camera_obs = self.sim.render(
camera_name=self.camera_name,
width=self.camera_width,
height=self.camera_height,
depth=self.camera_depth,
)
if self.camera_depth:
di["image"], di["depth"] = camera_obs
else:
di["image"] = camera_obs
# low-level object information
if self.use_object_obs:
# remember the keys to collect into object info
object_state_keys = []
# for conversion to relative gripper frame
gripper_pose = T.pose2mat((di["eef_pos"], di["eef_quat"]))
world_pose_in_gripper = T.pose_inv(gripper_pose)
for i in range(len(self.item_names_org)):
if self.single_object_mode == 2 and self.nut_id != i:
# skip observations
continue
obj_str = str(self.item_names_org[i]) + "0"
obj_pos = np.array(
self.sim.data.body_xpos[self.obj_body_id[obj_str]])
obj_quat = T.convert_quat(
self.sim.data.body_xquat[self.obj_body_id[obj_str]],
to="xyzw")
di["{}_pos".format(obj_str)] = obj_pos
di["{}_quat".format(obj_str)] = obj_quat
object_pose = T.pose2mat((obj_pos, obj_quat))
rel_pose = T.pose_in_A_to_pose_in_B(object_pose,
world_pose_in_gripper)
rel_pos, rel_quat = T.mat2pose(rel_pose)
di["{}_to_eef_pos".format(obj_str)] = rel_pos
di["{}_to_eef_quat".format(obj_str)] = rel_quat
object_state_keys.append("{}_pos".format(obj_str))
object_state_keys.append("{}_quat".format(obj_str))
object_state_keys.append("{}_to_eef_pos".format(obj_str))
object_state_keys.append("{}_to_eef_quat".format(obj_str))
if self.single_object_mode == 1:
# zero out other objs
for obj_str, obj_mjcf in self.mujoco_objects.items():
if obj_str == self.obj_to_use:
continue
else:
di["{}_pos".format(obj_str)] *= 0.0
di["{}_quat".format(obj_str)] *= 0.0
di["{}_to_eef_pos".format(obj_str)] *= 0.0
di["{}_to_eef_quat".format(obj_str)] *= 0.0
di["object-state"] = np.concatenate(
[di[k] for k in object_state_keys])
return di
def _check_contact(self):
"""
Returns True if gripper is in contact with an object.
"""
collision = False
for contact in self.sim.data.contact[:self.sim.data.ncon]:
if (self.sim.model.geom_id2name(contact.geom1) in self.finger_names
or self.sim.model.geom_id2name(
contact.geom2) in self.finger_names):
collision = True
break
return collision
def _check_success(self):
"""
Returns True if task has been completed.
"""
# remember objects that are on the correct pegs
gripper_site_pos = self.sim.data.site_xpos[self.eef_site_id]
for i in range(len(self.ob_inits)):
obj_str = str(self.item_names[i]) + "0"
obj_pos = self.sim.data.body_xpos[self.obj_body_id[obj_str]]
dist = np.linalg.norm(gripper_site_pos - obj_pos)
r_reach = 1 - np.tanh(10.0 * dist)
self.objects_on_pegs[i] = int(
self.on_peg(obj_pos, i) and r_reach < 0.6)
if self.single_object_mode > 0:
return np.sum(self.objects_on_pegs) > 0 # need one object on peg
# returns True if all objects are on correct pegs
return np.sum(self.objects_on_pegs) == len(self.ob_inits)
def _gripper_visualization(self):
"""
Do any needed visualization here. Overrides superclass implementations.
"""
# color the gripper site appropriately based on distance to nearest object
if self.gripper_visualization:
# find closest object
square_dist = lambda x: np.sum(
np.square(x - self.sim.data.get_site_xpos("grip_site")))
dists = np.array(list(map(square_dist, self.sim.data.site_xpos)))
dists[
self.
eef_site_id] = np.inf # make sure we don't pick the same site
dists[self.eef_cylinder_id] = np.inf
ob_dists = dists[
self.object_site_ids] # filter out object sites we care about
min_dist = np.min(ob_dists)
ob_id = np.argmin(ob_dists)
ob_name = self.object_names[ob_id]
# set RGBA for the EEF site here
max_dist = 0.1
scaled = (1.0 - min(min_dist / max_dist, 1.))**15
rgba = np.zeros(4)
rgba[0] = 1 - scaled
rgba[1] = scaled
rgba[3] = 0.5
self.sim.model.site_rgba[self.eef_site_id] = rgba
def _load_model(self):
"""
Loads an xml model, puts it in self.model
"""
super()._load_model()
# Adjust base pose accordingly
xpos = self.robots[0].robot_model.base_xpos_offset["table"](
self.table_full_size[0])
self.robots[0].robot_model.set_base_xpos(xpos)
# load model for table top workspace
mujoco_arena = PegsArena(
table_full_size=self.table_full_size,
table_friction=self.table_friction,
table_offset=self.table_offset,
)
# Arena always gets set to zero origin
mujoco_arena.set_origin([0, 0, 0])
# define nuts
self.nuts = []
nut_names = ("SquareNut", "RoundNut")
# Create default (SequentialCompositeSampler) sampler if it has not already been specified
if self.placement_initializer is None:
self.placement_initializer = SequentialCompositeSampler(
name="ObjectSampler")
for nut_name, default_y_range in zip(
nut_names, ([0.11, 0.225], [-0.225, -0.11])):
self.placement_initializer.append_sampler(
sampler=UniformRandomSampler(
name=f"{nut_name}Sampler",
x_range=[-0.115, -0.11],
y_range=default_y_range,
rotation=None,
rotation_axis="z",
ensure_object_boundary_in_range=False,
ensure_valid_placement=True,
reference_pos=self.table_offset,
z_offset=0.02,
))
# Reset sampler before adding any new samplers / objects
self.placement_initializer.reset()
for i, (nut_cls, nut_name) in enumerate(
zip(
(SquareNutObject, RoundNutObject),
nut_names,
)):
nut = nut_cls(name=nut_name)
self.nuts.append(nut)
# Add this nut to the placement initializer
if isinstance(self.placement_initializer,
SequentialCompositeSampler):
# assumes we have two samplers so we add nuts to them
self.placement_initializer.add_objects_to_sampler(
sampler_name=f"{nut_name}Sampler", mujoco_objects=nut)
else:
# This is assumed to be a flat sampler, so we just add all nuts to this sampler
self.placement_initializer.add_objects(nut)
# task includes arena, robot, and objects of interest
self.model = ManipulationTask(
mujoco_arena=mujoco_arena,
mujoco_robots=[robot.robot_model for robot in self.robots],
mujoco_objects=self.nuts,
)
class NutAssembly(RobotEnv):
"""
This class corresponds to the nut assembly task for a single robot arm.
Args:
robots (str or list of str): Specification for specific robot arm(s) to be instantiated within this env
(e.g: "Sawyer" would generate one arm; ["Panda", "Panda", "Sawyer"] would generate three robot arms)
Note: Must be a single single-arm robot!
controller_configs (str or list of dict): If set, contains relevant controller parameters for creating a
custom controller. Else, uses the default controller for this specific task. Should either be single
dict if same controller is to be used for all robots or else it should be a list of the same length as
"robots" param
gripper_types (str or list of str): type of gripper, used to instantiate
gripper models from gripper factory. Default is "default", which is the default grippers(s) associated
with the robot(s) the 'robots' specification. None removes the gripper, and any other (valid) model
overrides the default gripper. Should either be single str if same gripper type is to be used for all
robots or else it should be a list of the same length as "robots" param
gripper_visualizations (bool or list of bool): True if using gripper visualization.
Useful for teleoperation. Should either be single bool if gripper visualization is to be used for all
robots or else it should be a list of the same length as "robots" param
initialization_noise (dict or list of dict): Dict containing the initialization noise parameters.
The expected keys and corresponding value types are specified below:
:`'magnitude'`: The scale factor of uni-variate random noise applied to each of a robot's given initial
joint positions. Setting this value to `None` or 0.0 results in no noise being applied.
If "gaussian" type of noise is applied then this magnitude scales the standard deviation applied,
If "uniform" type of noise is applied then this magnitude sets the bounds of the sampling range
:`'type'`: Type of noise to apply. Can either specify "gaussian" or "uniform"
Should either be single dict if same noise value is to be used for all robots or else it should be a
list of the same length as "robots" param
:Note: Specifying "default" will automatically use the default noise settings.
Specifying None will automatically create the required dict with "magnitude" set to 0.0.
table_full_size (3-tuple): x, y, and z dimensions of the table.
table_friction (3-tuple): the three mujoco friction parameters for
the table.
use_camera_obs (bool): if True, every observation includes rendered image(s)
use_object_obs (bool): if True, include object (cube) information in
the observation.
reward_scale (None or float): Scales the normalized reward function by the amount specified.
If None, environment reward remains unnormalized
reward_shaping (bool): if True, use dense rewards.
placement_initializer (ObjectPositionSampler instance): if provided, will
be used to place objects on every reset, else a UniformRandomSampler
is used by default.
single_object_mode (int): specifies which version of the task to do. Note that
the observations change accordingly.
:`0`: corresponds to the full task with both types of nuts.
:`1`: corresponds to an easier task with only one type of nut initialized
on the table with every reset. The type is randomized on every reset.
:`2`: corresponds to an easier task with only one type of nut initialized
on the table with every reset. The type is kept constant and will not
change between resets.
nut_type (string): if provided, should be either "round" or "square". Determines
which type of nut (round or square) will be spawned on every environment
reset. Only used if @single_object_mode is 2.
use_indicator_object (bool): if True, sets up an indicator object that
is useful for debugging.
has_renderer (bool): If true, render the simulation state in
a viewer instead of headless mode.
has_offscreen_renderer (bool): True if using off-screen rendering
render_camera (str): Name of camera to render if `has_renderer` is True. Setting this value to 'None'
will result in the default angle being applied, which is useful as it can be dragged / panned by
the user using the mouse
render_collision_mesh (bool): True if rendering collision meshes in camera. False otherwise.
render_visual_mesh (bool): True if rendering visual meshes in camera. False otherwise.
control_freq (float): how many control signals to receive in every second. This sets the amount of
simulation time that passes between every action input.
horizon (int): Every episode lasts for exactly @horizon timesteps.
ignore_done (bool): True if never terminating the environment (ignore @horizon).
hard_reset (bool): If True, re-loads model, sim, and render object upon a reset call, else,
only calls sim.reset and resets all robosuite-internal variables
camera_names (str or list of str): name of camera to be rendered. Should either be single str if
same name is to be used for all cameras' rendering or else it should be a list of cameras to render.
:Note: At least one camera must be specified if @use_camera_obs is True.
:Note: To render all robots' cameras of a certain type (e.g.: "robotview" or "eye_in_hand"), use the
convention "all-{name}" (e.g.: "all-robotview") to automatically render all camera images from each
robot's camera list).
camera_heights (int or list of int): height of camera frame. Should either be single int if
same height is to be used for all cameras' frames or else it should be a list of the same length as
"camera names" param.
camera_widths (int or list of int): width of camera frame. Should either be single int if
same width is to be used for all cameras' frames or else it should be a list of the same length as
"camera names" param.
camera_depths (bool or list of bool): True if rendering RGB-D, and RGB otherwise. Should either be single
bool if same depth setting is to be used for all cameras or else it should be a list of the same length as
"camera names" param.
Raises:
AssertionError: [Invalid nut type specified]
AssertionError: [Invalid number of robots specified]
"""
def __init__(
self,
robots,
controller_configs=None,
gripper_types="default",
gripper_visualizations=False,
initialization_noise="default",
table_full_size=(0.8, 0.8, 0.05),
table_friction=(1, 0.005, 0.0001),
use_camera_obs=True,
use_object_obs=True,
reward_scale=1.0,
reward_shaping=False,
placement_initializer=None,
single_object_mode=0,
nut_type=None,
use_indicator_object=False,
has_renderer=False,
has_offscreen_renderer=True,
render_camera="frontview",
render_collision_mesh=False,
render_visual_mesh=True,
control_freq=10,
horizon=1000,
ignore_done=False,
hard_reset=True,
camera_names="agentview",
camera_heights=256,
camera_widths=256,
camera_depths=False,
):
# First, verify that only one robot is being inputted
self._check_robot_configuration(robots)
# task settings
self.single_object_mode = single_object_mode
self.nut_to_id = {"square": 0, "round": 1}
if nut_type is not None:
assert (nut_type in self.nut_to_id.keys()
), "invalid @nut_type argument - choose one of {}".format(
list(self.nut_to_id.keys()))
self.nut_id = self.nut_to_id[
nut_type] # use for convenient indexing
self.obj_to_use = None
# settings for table top
self.table_full_size = table_full_size
self.table_friction = table_friction
# reward configuration
self.reward_scale = reward_scale
self.reward_shaping = reward_shaping
# whether to use ground-truth object states
self.use_object_obs = use_object_obs
# object placement initializer
if placement_initializer:
self.placement_initializer = placement_initializer
else:
# treat sampling of each type of nut differently since we require different
# sampling ranges for each
self.placement_initializer = SequentialCompositeSampler()
self.placement_initializer.sample_on_top(
"SquareNut0",
surface_name="table",
x_range=[-0.115, -0.11],
y_range=[0.11, 0.225],
rotation=None,
rotation_axis='z',
z_offset=0.02,
ensure_object_boundary_in_range=False,
)
self.placement_initializer.sample_on_top(
"RoundNut0",
surface_name="table",
x_range=[-0.115, -0.11],
y_range=[-0.225, -0.11],
rotation=None,
rotation_axis='z',
z_offset=0.02,
ensure_object_boundary_in_range=False,
)
super().__init__(
robots=robots,
controller_configs=controller_configs,
gripper_types=gripper_types,
gripper_visualizations=gripper_visualizations,
initialization_noise=initialization_noise,
use_camera_obs=use_camera_obs,
use_indicator_object=use_indicator_object,
has_renderer=has_renderer,
has_offscreen_renderer=has_offscreen_renderer,
render_camera=render_camera,
render_collision_mesh=render_collision_mesh,
render_visual_mesh=render_visual_mesh,
control_freq=control_freq,
horizon=horizon,
ignore_done=ignore_done,
hard_reset=hard_reset,
camera_names=camera_names,
camera_heights=camera_heights,
camera_widths=camera_widths,
camera_depths=camera_depths,
)
def reward(self, action=None):
"""
Reward function for the task.
Sparse un-normalized reward:
- a discrete reward of 1.0 per nut if it is placed around its correct peg
Un-normalized components if using reward shaping, where the maximum is returned if not solved:
- Reaching: in [0, 0.1], proportional to the distance between the gripper and the closest nut
- Grasping: in {0, 0.35}, nonzero if the gripper is grasping a nut
- Lifting: in {0, [0.35, 0.5]}, nonzero only if nut is grasped; proportional to lifting height
- Hovering: in {0, [0.5, 0.7]}, nonzero only if nut is lifted; proportional to distance from nut to peg
Note that a successfully completed task (nut around peg) will return 1.0 per nut irregardless of whether the
environment is using sparse or shaped rewards
Note that the final reward is normalized and scaled by reward_scale / 2.0 (or 1.0 if only a single nut is
being used) as well so that the max score is equal to reward_scale
Args:
action (np.array): [NOT USED]
Returns:
float: reward value
"""
# compute sparse rewards
self._check_success()
reward = np.sum(self.objects_on_pegs)
# add in shaped rewards
if self.reward_shaping:
staged_rewards = self.staged_rewards()
reward += max(staged_rewards)
if self.reward_scale is not None:
reward *= self.reward_scale
if self.single_object_mode == 0:
reward /= 2.0
return reward
def staged_rewards(self):
"""
Calculates staged rewards based on current physical states.
Stages consist of reaching, grasping, lifting, and hovering.
Returns:
4-tuple:
- (float) reaching reward
- (float) grasping reward
- (float) lifting reward
- (float) hovering reward
"""
reach_mult = 0.1
grasp_mult = 0.35
lift_mult = 0.5
hover_mult = 0.7
# filter out objects that are already on the correct pegs
names_to_reach = []
objs_to_reach = []
geoms_to_grasp = []
geoms_by_array = []
for i in range(len(self.ob_inits)):
if self.objects_on_pegs[i]:
continue
obj_str = str(self.item_names[i]) + "0"
names_to_reach.append(obj_str)
objs_to_reach.append(self.obj_body_id[obj_str])
geoms_to_grasp.extend(self.obj_geom_id[obj_str])
geoms_by_array.append(self.obj_geom_id[obj_str])
### reaching reward governed by distance to closest object ###
r_reach = 0.
if len(objs_to_reach):
# reaching reward via minimum distance to the handles of the objects (the last geom of each nut)
geom_ids = [elem[-1] for elem in geoms_by_array]
target_geom_pos = self.sim.data.geom_xpos[geom_ids]
gripper_site_pos = self.sim.data.site_xpos[
self.robots[0].eef_site_id]
dists = np.linalg.norm(target_geom_pos -
gripper_site_pos.reshape(1, -1),
axis=1)
r_reach = (1 - np.tanh(10.0 * min(dists))) * reach_mult
### grasping reward for touching any objects of interest ###
touch_left_finger = False
touch_right_finger = False
for i in range(self.sim.data.ncon):
c = self.sim.data.contact[i]
if c.geom1 in geoms_to_grasp:
if c.geom2 in self.l_finger_geom_ids:
touch_left_finger = True
if c.geom2 in self.r_finger_geom_ids:
touch_right_finger = True
elif c.geom2 in geoms_to_grasp:
if c.geom1 in self.l_finger_geom_ids:
touch_left_finger = True
if c.geom1 in self.r_finger_geom_ids:
touch_right_finger = True
has_grasp = touch_left_finger and touch_right_finger
r_grasp = int(has_grasp) * grasp_mult
### lifting reward for picking up an object ###
r_lift = 0.
table_pos = np.array(self.sim.data.body_xpos[self.table_body_id])
if len(objs_to_reach) and r_grasp > 0.:
z_target = table_pos[2] + 0.2
object_z_locs = self.sim.data.body_xpos[objs_to_reach][:, 2]
z_dists = np.maximum(z_target - object_z_locs, 0.)
r_lift = grasp_mult + (1 - np.tanh(15.0 * min(z_dists))) * (
lift_mult - grasp_mult)
### hover reward for getting object above peg ###
r_hover = 0.
if len(objs_to_reach):
r_hovers = np.zeros(len(objs_to_reach))
for i in range(len(objs_to_reach)):
if names_to_reach[i].startswith(self.item_names[0]):
peg_pos = np.array(
self.sim.data.body_xpos[self.peg1_body_id])[:2]
elif names_to_reach[i].startswith(self.item_names[1]):
peg_pos = np.array(
self.sim.data.body_xpos[self.peg2_body_id])[:2]
else:
raise Exception("Got invalid object to reach: {}".format(
names_to_reach[i]))
ob_xy = self.sim.data.body_xpos[objs_to_reach[i]][:2]
dist = np.linalg.norm(peg_pos - ob_xy)
r_hovers[i] = r_lift + (1 - np.tanh(10.0 * dist)) * (
hover_mult - lift_mult)
r_hover = np.max(r_hovers)
return r_reach, r_grasp, r_lift, r_hover
def on_peg(self, obj_pos, peg_id):
if peg_id == 0:
peg_pos = np.array(self.sim.data.body_xpos[self.peg1_body_id])
else:
peg_pos = np.array(self.sim.data.body_xpos[self.peg2_body_id])
res = False
if (abs(obj_pos[0] - peg_pos[0]) < 0.03
and abs(obj_pos[1] - peg_pos[1]) < 0.03
and obj_pos[2] < self.mujoco_arena.table_offset[2] + 0.05):
res = True
return res
def clear_objects(self, obj):
"""
Clears objects without the name @obj out of the task space. This is useful
for supporting task modes with single types of objects, as in
@self.single_object_mode without changing the model definition.
Args:
obj (str): Name of object to keep in the task space
"""
for obj_name, obj_mjcf in self.mujoco_objects.items():
if obj_name == obj:
continue
else:
sim_state = self.sim.get_state()
# print(self.sim.model.get_joint_qpos_addr(obj_name))
sim_state.qpos[self.sim.model.get_joint_qpos_addr(
obj_name + "_jnt0")[0]] = 10
self.sim.set_state(sim_state)
self.sim.forward()
def _load_model(self):
"""
Loads an xml model, puts it in self.model
"""
super()._load_model()
# Verify the correct robot has been loaded
assert isinstance(self.robots[0], SingleArm), \
"Error: Expected one single-armed robot! Got {} type instead.".format(type(self.robots[0]))
# Adjust base pose accordingly
xpos = self.robots[0].robot_model.base_xpos_offset["table"](
self.table_full_size[0])
self.robots[0].robot_model.set_base_xpos(xpos)
# load model for table top workspace
self.mujoco_arena = PegsArena(table_full_size=self.table_full_size,
table_friction=self.table_friction,
table_offset=(0, 0, 0.82))
if self.use_indicator_object:
self.mujoco_arena.add_pos_indicator()
# Arena always gets set to zero origin
self.mujoco_arena.set_origin([0, 0, 0])
# define mujoco objects
self.ob_inits = [SquareNutObject, RoundNutObject]
self.item_names = ["SquareNut", "RoundNut"]
self.item_names_org = list(self.item_names)
self.obj_to_use = (self.item_names[1] + "{}").format(0)
self.ngeoms = [5, 9]
lst = []
for i in range(len(self.ob_inits)):
ob_name = (self.item_names[i] + "0")
ob = self.ob_inits[i](
name=ob_name,
joints=[dict(type="free", damping="0.0005")
], # damp the free joint for each object
)
lst.append((ob_name, ob))
self.mujoco_objects = OrderedDict(lst)
self.n_objects = len(self.mujoco_objects)
# task includes arena, robot, and objects of interest
self.model = ManipulationTask(
mujoco_arena=self.mujoco_arena,
mujoco_robots=[robot.robot_model for robot in self.robots],
mujoco_objects=self.mujoco_objects,
visual_objects=None,
initializer=self.placement_initializer,
)
# set positions of objects
self.model.place_objects()
def _get_reference(self):
"""
Sets up references to important components. A reference is typically an
index or a list of indices that point to the corresponding elements
in a flatten array, which is how MuJoCo stores physical simulation data.
"""
super()._get_reference()
# Additional object references from this env
self.obj_body_id = {}
self.obj_geom_id = {}
self.table_body_id = self.sim.model.body_name2id("table")
self.peg1_body_id = self.sim.model.body_name2id("peg1")
self.peg2_body_id = self.sim.model.body_name2id("peg2")
for i in range(len(self.ob_inits)):
obj_str = str(self.item_names[i]) + "0"
self.obj_body_id[obj_str] = self.sim.model.body_name2id(obj_str)
geom_ids = []
for j in range(self.ngeoms[i]):
geom_ids.append(
self.sim.model.geom_name2id(obj_str + "-{}".format(j)))
self.obj_geom_id[obj_str] = geom_ids
# information of objects
self.object_names = list(self.mujoco_objects.keys())
self.object_site_ids = [
self.sim.model.site_name2id(ob_name)
for ob_name in self.object_names
]
# id of grippers for contact checking
self.l_finger_geom_ids = [
self.sim.model.geom_name2id(x)
for x in self.robots[0].gripper.important_geoms["left_finger"]
]
self.r_finger_geom_ids = [
self.sim.model.geom_name2id(x)
for x in self.robots[0].gripper.important_geoms["right_finger"]
]
# self.sim.data.contact # list, geom1, geom2
self.collision_check_geom_names = self.sim.model._geom_name2id.keys()
self.collision_check_geom_ids = [
self.sim.model._geom_name2id[k]
for k in self.collision_check_geom_names
]
# keep track of which objects are on their corresponding pegs
self.objects_on_pegs = np.zeros(len(self.ob_inits))
def _reset_internal(self):
"""
Resets simulation internal configurations.
"""
super()._reset_internal()
# Reset all object positions using initializer sampler if we're not directly loading from an xml
if not self.deterministic_reset:
# Sample from the placement initializer for all objects
obj_pos, obj_quat = self.model.place_objects()
# Loop through all objects and reset their positions
for i, (obj_name, _) in enumerate(self.mujoco_objects.items()):
self.sim.data.set_joint_qpos(
obj_name + "_jnt0",
np.concatenate(
[np.array(obj_pos[i]),
np.array(obj_quat[i])]))
# information of objects
self.object_names = list(self.mujoco_objects.keys())
self.object_site_ids = [
self.sim.model.site_name2id(ob_name)
for ob_name in self.object_names
]
# Move objects out of the scene depending on the mode
if self.single_object_mode == 1:
self.obj_to_use = (random.choice(self.item_names) + "{}").format(0)
self.clear_objects(self.obj_to_use)
elif self.single_object_mode == 2:
self.obj_to_use = (self.item_names[self.nut_id] + "{}").format(0)
self.clear_objects(self.obj_to_use)
def _get_observation(self):
"""
Returns an OrderedDict containing observations [(name_string, np.array), ...].
Important keys:
`'robot-state'`: contains robot-centric information.
`'object-state'`: requires @self.use_object_obs to be True. Contains object-centric information.
`'image'`: requires @self.use_camera_obs to be True. Contains a rendered frame from the simulation.
`'depth'`: requires @self.use_camera_obs and @self.camera_depth to be True.
Contains a rendered depth map from the simulation
Returns:
OrderedDict: Observations from the environment
"""
di = super()._get_observation()
# low-level object information
if self.use_object_obs:
# Get robot prefix
pr = self.robots[0].robot_model.naming_prefix
# remember the keys to collect into object info
object_state_keys = []
# for conversion to relative gripper frame
gripper_pose = T.pose2mat(
(di[pr + "eef_pos"], di[pr + "eef_quat"]))
world_pose_in_gripper = T.pose_inv(gripper_pose)
for i in range(len(self.item_names_org)):
if self.single_object_mode == 2 and self.nut_id != i:
# skip observations
continue
obj_str = str(self.item_names_org[i]) + "0"
obj_pos = np.array(
self.sim.data.body_xpos[self.obj_body_id[obj_str]])
obj_quat = T.convert_quat(
self.sim.data.body_xquat[self.obj_body_id[obj_str]],
to="xyzw")
di["{}_pos".format(obj_str)] = obj_pos
di["{}_quat".format(obj_str)] = obj_quat
object_pose = T.pose2mat((obj_pos, obj_quat))
rel_pose = T.pose_in_A_to_pose_in_B(object_pose,
world_pose_in_gripper)
rel_pos, rel_quat = T.mat2pose(rel_pose)
di["{}_to_{}eef_pos".format(obj_str, pr)] = rel_pos
di["{}_to_{}eef_quat".format(obj_str, pr)] = rel_quat
object_state_keys.append("{}_pos".format(obj_str))
object_state_keys.append("{}_quat".format(obj_str))
object_state_keys.append("{}_to_{}eef_pos".format(obj_str, pr))
object_state_keys.append("{}_to_{}eef_quat".format(
obj_str, pr))
if self.single_object_mode == 1:
# zero out other objs
for obj_str, obj_mjcf in self.mujoco_objects.items():
if obj_str == self.obj_to_use:
continue
else:
di["{}_pos".format(obj_str)] *= 0.0
di["{}_quat".format(obj_str)] *= 0.0
di["{}_to_{}eef_pos".format(obj_str, pr)] *= 0.0
di["{}_to_{}eef_quat".format(obj_str, pr)] *= 0.0
di["object-state"] = np.concatenate(
[di[k] for k in object_state_keys])
return di
def _check_success(self):
"""
Check if all nuts have been successfully placed around their corresponding pegs.
Returns:
bool: True if all nuts are placed correctly
"""
# remember objects that are on the correct pegs
gripper_site_pos = self.sim.data.site_xpos[self.robots[0].eef_site_id]
for i in range(len(self.ob_inits)):
obj_str = str(self.item_names[i]) + "0"
obj_pos = self.sim.data.body_xpos[self.obj_body_id[obj_str]]
dist = np.linalg.norm(gripper_site_pos - obj_pos)
r_reach = 1 - np.tanh(10.0 * dist)
self.objects_on_pegs[i] = int(
self.on_peg(obj_pos, i) and r_reach < 0.6)
if self.single_object_mode > 0:
return np.sum(self.objects_on_pegs) > 0 # need one object on peg
# returns True if all objects are on correct pegs
return np.sum(self.objects_on_pegs) == len(self.ob_inits)
def _visualization(self):
"""
Do any needed visualization here. Overrides superclass implementations.
"""
# color the gripper site appropriately based on distance to cube
if self.robots[0].gripper_visualization:
# find closest object
square_dist = lambda x: np.sum(
np.square(x - self.sim.data.get_site_xpos(self.robots[
0].gripper.visualization_sites["grip_site"])))
dists = np.array(list(map(square_dist, self.sim.data.site_xpos)))
dists[
self.robots[0].
eef_site_id] = np.inf # make sure we don't pick the same site
dists[self.robots[0].eef_cylinder_id] = np.inf
ob_dists = dists[
self.object_site_ids] # filter out object sites we care about
min_dist = np.min(ob_dists)
ob_id = np.argmin(ob_dists)
# set RGBA for the EEF site here
max_dist = 0.1
scaled = (1.0 - min(min_dist / max_dist, 1.))**15
rgba = np.zeros(4)
rgba[0] = 1 - scaled
rgba[1] = scaled
rgba[3] = 0.5
self.sim.model.site_rgba[self.robots[0].eef_site_id] = rgba
def _check_robot_configuration(self, robots):
"""
Sanity check to make sure the inputted robots and configuration is acceptable
Args:
robots (str or list of str): Robots to instantiate within this env
"""
if type(robots) is list:
assert len(
robots
) == 1, "Error: Only one robot should be inputted for this task!"