def car():
return SpaceDict({
"sensors":
SpaceDict({
"position":
spaces.Box(low=-100, high=100, shape=(3, )),
"velocity":
spaces.Box(low=-1, high=1, shape=(3, )),
"front_cam":
spaces.Tuple((
spaces.Box(low=0, high=1, shape=(10, 10, 3)),
spaces.Box(low=0, high=1, shape=(10, 10, 3)),
)),
"rear_cam":
spaces.Box(low=0, high=1, shape=(10, 10, 3)),
}),
"ext_controller":
spaces.MultiDiscrete((5, 2, 2)),
"inner_state": {
"charge":
spaces.Discrete(100),
"system_checks":
spaces.MultiBinary(10),
"job_status":
SpaceDict({
"task": spaces.Discrete(5),
"progress": spaces.Box(low=0, high=100, shape=()),
}),
},
})
def get_observation_space():
"""Return only position and gripper_width by default"""
observation_space = {}
observation_space["position"] = spaces.Box(
low=np.array([0.3, -0.85, 0]), high=np.array([0.85, 0.85, 0.8]))
observation_space["gripper_width"] = spaces.Box(low=0.03, high=0.11, shape=(1,))
return SpaceDict(observation_space)
def state_space(self):
return SpaceDict({
"joint_position":
spaces.Box(low=0,
high=1,
shape=(AllegroHand.num_dof, ),
dtype=np.float32)
})
def action_space(self):
return SpaceDict({
"joint_torque":
spaces.Box(low=0,
high=1,
shape=(AllegroHand.num_dof, ),
dtype=np.float32)
})
def get_gmm_update_parameter_space(self):
parameter_space = {}
parameter_space["mu"] = spaces.Box(low=-0.001,
high=0.001,
shape=self.mu.shape)
parameter_space["priors"] = spaces.Box(low=-0.1,
high=0.1,
shape=self.priors.shape)
#parameter_space["sigma"] = spaces.Box(low=-0.001, high=0.001, shape=self.mu.shape)
return SpaceDict(parameter_space)
def get_observation_space(self):
observation_space = {}
observation_space["position"] = spaces.Box(
low=np.array([0.3, -0.85, 0]), high=np.array([0.85, 0.85, 0.8]))
if self.with_gripper_width:
observation_space["gripper_width"] = spaces.Box(low=0.03, high=0.11, shape=(1,))
if self.with_tactile_sensor:
t_width, t_height = self.env.cfg.tacto.width, self.env.cfg.tacto.height
observation_space["tactile_sensor"] = spaces.Tuple(
tuple([spaces.Box(low=-np.ones((t_height,t_width)), high=np.ones((t_height, t_width)))
for _ in range(2)]))
if self.with_force:
observation_space["force"] = spaces.Box(
low=np.array([0, 0]), high=np.array([2.0, 2.0]))
return SpaceDict(observation_space)
def state_space(self):
return SpaceDict({
"end_effector": {
"position":
spaces.Box(
low=np.array([0.3, -0.85, 0]),
high=np.array([0.85, 0.85, 0.8]),
shape=(3, ),
dtype=np.float32,
),
"orientation":
spaces.Box(low=-np.pi,
high=np.pi,
shape=(4, ),
dtype=np.float32),
},
"gripper_width":
spaces.Box(low=0.03, high=0.11, shape=(1, ), dtype=np.float32),
})
def state_space(self):
return SpaceDict({
"position":
spaces.Box(low=0, high=1, shape=(), dtype=np.float32)
})
def action_space(self):
return SpaceDict(
{"force": spaces.Box(low=0, high=1, shape=(), dtype=np.float32)})
def state_space(self):
return SpaceDict({
"image":
spaces.Box(low=0, high=255, shape=(3, 4), dtype=np.uint8)
})