class SawyerReach(SawyerEnv):
"""
This class corresponds to the a primitive policy for the reach task on the Sawyer robot arm.
Uniform random sample on x, y, or z axis in range of 20 to 60 cm for x and y, 0 to 40 cm for z (center of table)
"""
def __init__(
self,
gripper_type="TwoFingerGripper",
lower_goal_range=[-0.1, -0.1, -0.1],
upper_goal_range=[0.1, 0.1, 0.2],
seed=False,
random_arm_init=None, # please pass in [low, high]
table_full_size=(0.8, 0.8, 0.6),
table_friction=(1., 5e-3, 1e-4),
use_camera_obs=False,
use_object_obs=True,
reward_shaping=True,
placement_initializer=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:
prim_axis (str): which axis is being explored
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 UniformRandomSampler
is used by default.
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.
"""
self.upper_goal_range = upper_goal_range
self.lower_goal_range = lower_goal_range
self.random_arm_init = random_arm_init
self.seed = seed
self.distance_threshold = 0.015
# 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
# object placement initializer
self.placement_initializer = placement_initializer
# order: di["eef_pos"] di["gripper_qpos"] di["gripper_to_marker"] self.goal
# dim: 3, 2, 3, 3
# low = -np.ones(11) * np.inf
# high = np.ones(11) * np.inf
# self.observation_space = spaces.Box(low=low, high=high)
self.controller = SawyerIKController(
bullet_data_path=os.path.join(robosuite.models.assets_root,
"bullet_data"),
robot_jpos_getter=self._robot_jpos_getter,
)
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):
"""
Loads an xml model, puts it in self.model
"""
super()._load_model()
self.mujoco_robot.set_base_xpos([0, 0, 0])
# load model for table top workspace
self.mujoco_arena = TableArena(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(
[0.16 + self.table_full_size[0] / 2, 0, 0])
self.mujoco_objects = OrderedDict([])
# task includes arena, robot, and objects of interest
self.model = TableTopTask(
self.mujoco_arena,
self.mujoco_robot,
self.mujoco_objects,
initializer=self.placement_initializer,
)
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()
# self.cube_body_id = self.sim.model.body_name2id("cube")
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.cube_geom_id = self.sim.model.geom_name2id("cube")
def _reset_internal(self):
"""
Resets simulation internal configurations.
"""
super()._reset_internal()
# reset positions of objects
self.model.place_objects()
if self.random_arm_init is not None:
if self.seed:
constant_quat = np.array(
[-0.01704371, -0.99972409, 0.00199679, -0.01603944])
target_position = np.array([
0.5 + np.random.uniform(0.0, 0.0),
np.random.uniform(0.0, 0.0),
# self.table_full_size[2] + 0.15211762])
0.8 + 0.20211762
])
else:
# random initialization of arm
constant_quat = np.array(
[-0.01704371, -0.99972409, 0.00199679, -0.01603944])
target_position = np.array([
0.5 + np.random.uniform(self.random_arm_init[0],
self.random_arm_init[1]),
np.random.uniform(self.random_arm_init[0],
self.random_arm_init[1]),
self.table_full_size[2] + 0.20211762
])
self.controller.sync_ik_robot(self._robot_jpos_getter(),
simulate=True)
joint_list = self.controller.inverse_kinematics(
target_position, constant_quat)
init_pos = np.array(joint_list)
else:
# default robosuite init
init_pos = np.array(
[-0.5538, -0.8208, 0.4155, 1.8409, -0.4955, 0.6482, 1.9628])
init_pos += np.random.randn(init_pos.shape[0]) * 0.02
self.sim.data.qpos[self._ref_joint_pos_indexes] = np.array(init_pos)
def reset(self):
self._destroy_viewer()
self._reset_internal()
self.sim.forward()
# reset goal (marker)
gripper_site_pos = np.array(self.sim.data.site_xpos[self.eef_site_id])
distance = np.random.uniform(self.lower_goal_range,
self.upper_goal_range)
while np.linalg.norm(distance) <= (self.distance_threshold * 1.75):
distance = np.random.uniform(self.lower_goal_range,
self.upper_goal_range)
if self.seed:
distance = np.array((0.05, -0.1, 0.1))
self.goal = gripper_site_pos + distance
return self._get_observation()
def _robot_jpos_getter(self):
return np.array([0, -1.18, 0.00, 2.18, 0.00, 0.57, 3.3161])
def reward(self, action=None):
"""
Reward function for the task.
The dense reward has one component.
Reaching: in [0, 1], to encourage the arm to reach the marker
The sparse reward only consists of the lifting component.
Args:
action (np array): unused for this task
Returns:
reward (float): the reward
"""
# velocity_pen = np.linalg(np.array(
# [self.sim.data.qvel[x] for x in self._ref_joint_vel_indexes]
# ))
marker_pos = self.goal
gripper_site_pos = self.sim.data.site_xpos[self.eef_site_id]
return self.compute_reward(gripper_site_pos, marker_pos, None)
# for goalenv wrapper
def compute_reward(self, achieved_goal, desired_goal, info=None):
d = np.linalg.norm(achieved_goal - desired_goal)
if self.reward_shaping: # dense
reward = 1 - np.tanh(10 * d)
if d <= self.distance_threshold:
reward = 10.0
else: # sparse (-1 or 0)
#reward = -np.float32(d > distance_threshold)
if d > self.distance_threshold:
reward = -1.0
else:
reward = 60.0
return reward
# for goalenv wrapper
# TODO check the desired_goal/achieved_goal for indexing
def get_goalenv_dict(self, obs_dict):
# using only object-state and robot-state
ob_lst = []
di = {}
for key in obs_dict:
if key in ["robot-state", "object-state"]:
ob_lst.append(obs_dict[key])
di['observation'] = np.concatenate(ob_lst)
di['desired_goal'] = obs_dict['object-state'][0:3]
di['achieved_goal'] = obs_dict['robot-state'][23:26]
return di
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()
# camera observations
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:
gripper_site_pos = np.array(
self.sim.data.site_xpos[self.eef_site_id])
di["gripper_to_marker"] = gripper_site_pos - self.goal
di["object-state"] = np.concatenate(
[di["gripper_to_marker"], self.goal])
return di
def _gripper_visualization(self):
"""
Do any needed visualization here. Overrides superclass implementations.
"""
# color the gripper site appropriately based on distance to cube
if self.gripper_visualization:
# get distance to cube
cube_site_id = self.sim.model.site_name2id("cube")
dist = np.sum(
np.square(self.sim.data.site_xpos[cube_site_id] -
self.sim.data.get_site_xpos("grip_site")))
# set RGBA for the EEF site here
max_dist = 0.1
scaled = (1.0 - 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
class SawyerPrimitivePick(SawyerEnv):
"""
This class corresponds to the lifting task for the Sawyer robot arm.
"""
def __init__(
self,
instructive=0.0,
decay=0.0,
random_arm_init=None,
gripper_type="TwoFingerGripper",
table_full_size=(0.8, 0.8, 0.6),
table_friction=(1., 5e-3, 1e-4),
use_camera_obs=True,
use_object_obs=True,
reward_shaping=False,
placement_initializer=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 UniformRandomSampler
is used by default.
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.
"""
self.random_arm_init = random_arm_init
self.instructive = instructive
self.instructive_counter = 0
self.eval_mode = False
self.decay = decay
# 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
# object placement initializer
if placement_initializer:
self.placement_initializer = placement_initializer
else:
self.placement_initializer = UniformRandomSampler(
x_range=[-0.00, 0.00],
y_range=[-0.00, 0.00],
ensure_object_boundary_in_range=False,
z_rotation=None,
)
# order: di["eef_pos"] di["gripper_qpos"] di["gripper_to_marker"] self.goal
# dim: 3, 2, 3, 3
# low = -np.ones(11) * np.inf
# high = np.ones(11) * np.inf
# self.observation_space = spaces.Box(low=low, high=high)
self.controller = SawyerIKController(
bullet_data_path=os.path.join(robosuite.models.assets_root,
"bullet_data"),
robot_jpos_getter=self._robot_jpos_getter,
)
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):
"""
Loads an xml model, puts it in self.model
"""
super()._load_model()
self.mujoco_robot.set_base_xpos([0, 0, 0])
# load model for table top workspace
self.mujoco_arena = TableArena(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(
[0.16 + self.table_full_size[0] / 2, 0, 0])
# initialize objects of interest
cube = BoxObject(
# size_min=[0.020, 0.020, 0.020], # [0.015, 0.015, 0.015],
# size_max=[0.022, 0.022, 0.022], # [0.018, 0.018, 0.018])
size_min=[0.015, 0.015, 0.015],
size_max=[0.015, 0.015, 0.015],
rgba=[1, 0, 0, 1],
)
self.mujoco_objects = OrderedDict([("cube", cube)])
# task includes arena, robot, and objects of interest
self.model = TableTopTask(
self.mujoco_arena,
self.mujoco_robot,
self.mujoco_objects,
initializer=self.placement_initializer,
)
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()
self.cube_body_id = self.sim.model.body_name2id("cube")
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.cube_geom_id = self.sim.model.geom_name2id("cube")
def _reset_internal(self):
"""
Resets simulation internal configurations.
"""
super(SawyerEnv, self)._reset_internal()
self.model.place_objects()
if self.random_arm_init:
# random initialization of arm
constant_quat = np.array(
[-0.01704371, -0.99972409, 0.00199679, -0.01603944])
target_position = np.array([
0.5 + np.random.uniform(self.random_arm_init[0],
self.random_arm_init[1]),
np.random.uniform(self.random_arm_init[0],
self.random_arm_init[1]),
self.table_full_size[2] + 0.15211762
])
self.controller.sync_ik_robot(self._robot_jpos_getter(),
simulate=True)
joint_list = self.controller.inverse_kinematics(
target_position, constant_quat)
init_pos = np.array(joint_list)
else:
# default robosuite init
init_pos = np.array(
[-0.5538, -0.8208, 0.4155, 1.8409, -0.4955, 0.6482, 1.9628])
init_pos += np.random.randn(init_pos.shape[0]) * 0.02
self.sim.data.qpos[self._ref_joint_pos_indexes] = init_pos
self.sim.data.qpos[self._ref_gripper_joint_pos_indexes] = np.array(
[0.0115, -0.0115]) # Open
self.sim.forward()
self.sim.data.qpos[10:12] = self.sim.data.site_xpos[
self.eef_site_id][:2]
# decay rate (1 / (1 + decay_param * #resets))
chance = self.instructive * (
1 / (1 + self.decay * self.instructive_counter))
if np.random.uniform() < chance: # and not self.eval_mode:
self.sim.data.qpos[13] = self.sim.data.site_xpos[
self.eef_site_id][2]
self.sim.data.qpos[self._ref_gripper_joint_pos_indexes] = np.array(
[-0.0, -0.0]) #np.array([-0.21021952, -0.00024167]) # gripped
self.instructive_counter = self.instructive_counter + 1
cube_pos = np.array(self.sim.data.body_xpos[self.cube_body_id])
self.goal = cube_pos + np.array((0, 0, 0.075))
def _robot_jpos_getter(self):
return np.array([0, -1.18, 0.00, 2.18, 0.00, 0.57, 3.3161])
def reward(self, action=None):
"""
Reward function for the task.
The dense reward has three components.
Reaching: in [0, 1], to encourage the arm to reach the cube
Grasping: in {0, 0.25}, non-zero if arm is grasping the cube
Lifting: in {0, 1}, non-zero if arm has lifted the cube
The sparse reward only consists of the lifting component.
Args:
action (np array): unused for this task
Returns:
reward (float): the reward
"""
cube_height = self.sim.data.body_xpos[self.cube_body_id]
return self.compute_reward(cube_height, self.goal)
# for goalenv wrapper
def compute_reward(self, achieved_goal, desired_goal, info=None):
# -1 if cube is below, 0 if cube is above
reward = -1.0
if achieved_goal[2] > desired_goal[2]:
reward = 100.0
return reward
# for goalenv wrapper
def get_goalenv_dict(self, obs_dict):
# using only object-state and robot-state
ob_lst = []
di = {}
for key in obs_dict:
if key in ["robot-state", "object-state"]:
ob_lst.append(obs_dict[key])
di['observation'] = np.concatenate(ob_lst)
di['desired_goal'] = self.goal
di['achieved_goal'] = obs_dict['object-state'][0:3]
return di
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()
# camera observations
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:
# position and rotation of object
cube_pos = np.array(self.sim.data.body_xpos[self.cube_body_id])
cube_quat = convert_quat(np.array(
self.sim.data.body_xquat[self.cube_body_id]),
to="xyzw")
di["cube_pos"] = cube_pos
di["cube_quat"] = cube_quat
gripper_site_pos = np.array(
self.sim.data.site_xpos[self.eef_site_id])
di["gripper_to_cube"] = gripper_site_pos - cube_pos
# di["object-state"] = np.concatenate(
# [cube_pos, cube_quat, di["gripper_to_cube"]]
# )
di["object-state"] = np.concatenate(
[di["gripper_to_cube"], cube_pos])
return di
def _gripper_visualization(self):
"""
Do any needed visualization here. Overrides superclass implementations.
"""
# color the gripper site appropriately based on distance to cube
if self.gripper_visualization:
# get distance to cube
cube_site_id = self.sim.model.site_name2id("cube")
dist = np.sum(
np.square(self.sim.data.site_xpos[cube_site_id] -
self.sim.data.get_site_xpos("grip_site")))
# set RGBA for the EEF site here
max_dist = 0.1
scaled = (1.0 - 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