def _create_task(self, task_args: dict) -> Task:
# Create the tasks
continuous_rew_fcn = task_args.get('continuous_rew_fcn', True)
task_box = create_box_lift_task(self.spec,
continuous_rew_fcn,
succ_thold=0.03)
task_check_bounds = create_check_all_boundaries_task(self.spec,
penalty=1e3)
task_collision = create_collision_task(self.spec, factor=1.)
task_ts_discrepancy = create_task_space_discrepancy_task(
self.spec,
AbsErrRewFcn(q=0.5 * np.ones(3), r=np.zeros(self.act_space.shape)))
return ParallelTasks(
[task_box, task_check_bounds, task_collision, task_ts_discrepancy],
hold_rew_when_done=False)
def _create_task(self, task_args: dict) -> Task:
# Create the tasks
continuous_rew_fcn = task_args.get('continuous_rew_fcn', True)
task_box = create_box_flip_task(self.spec, continuous_rew_fcn)
task_check_bounds = create_check_all_boundaries_task(self.spec,
penalty=1e3)
# task_collision = create_collision_task(self.spec, factor=5e-2)
from pyrado.environments.rcspysim.box_flipping import create_task_space_discrepancy_task
task_ts_discrepancy = create_task_space_discrepancy_task(
self.spec,
AbsErrRewFcn(q=5e-2 * np.ones(2),
r=np.zeros(self.act_space.shape)))
return ParallelTasks(
[
task_box,
task_check_bounds,
# task_collision,
task_ts_discrepancy
],
hold_rew_when_done=False)
def _create_task(self, task_args: dict) -> Task:
# Create the tasks
des_state1 = self.get_body_position(
'Goal', '', '')[:2] # x and y coordinates in world frame
task_box = create_extract_ball_task(self.spec, task_args, des_state1)
des_state2 = np.array(
self.get_body_position('Slider', '', '')[1] -
0.5) # y coordinate in world frame
task_slider = create_extract_slider_task(self.spec, task_args,
des_state2)
task_check_bounds = create_check_all_boundaries_task(self.spec,
penalty=1e3)
task_collision = create_collision_task(self.spec, factor=5e-2)
task_ts_discrepancy = create_task_space_discrepancy_task(
self.spec,
AbsErrRewFcn(q=0.5 * np.ones(6),
r=np.zeros(self.act_space.flat_dim)))
return ParallelTasks([
task_box, task_slider, task_check_bounds, task_collision,
task_ts_discrepancy
],
hold_rew_when_done=False)
def _create_task(self, task_args: dict) -> Task:
# Define the task including the reward function
state_des = task_args.get("state_des", None)
if state_des is None:
# Get the goal position in world coordinates
p = self.get_body_position("Goal", "", "")
state_des = np.array([p[0], p[2], 0, 0, 0,
0]) # X, Z, B, Xd, Zd, Bd
# Create the individual subtasks
task_reach_goal = create_insert_task(
self.spec,
state_des,
rew_fcn=ExpQuadrErrRewFcn(
Q=np.diag([2e1, 2e1, 1e-1, 1e-2, 1e-2, 1e-2]),
R=2e-2 * np.eye(self.act_space.flat_dim)),
success_fcn=partial(proximity_succeeded,
thold_dist=0.07,
dims=[0, 1, 2]), # position and angle
)
task_ts_discrepancy = create_task_space_discrepancy_task(
self.spec,
AbsErrRewFcn(q=0.1 * np.ones(2), r=np.zeros(self.act_space.shape)))
return ParallelTasks([task_reach_goal, task_ts_discrepancy])
def _create_task(self, task_args: dict) -> Task:
# Define the indices for selection. This needs to match the observations' names in RcsPySim.
if task_args.get("consider_velocities", False):
idcs = ["Effector_X", "Effector_Z", "Effector_Xd", "Effector_Zd"]
else:
idcs = ["Effector_X", "Effector_Z"]
# Get the masked environment specification
spec = EnvSpec(
self.spec.obs_space,
self.spec.act_space,
self.spec.state_space.subspace(
self.spec.state_space.create_mask(idcs)),
)
# Get the goal position in world coordinates for all three sub-goals
p1 = self.get_body_position("Goal1", "", "")
p2 = self.get_body_position("Goal2", "", "")
p3 = self.get_body_position("Goal3", "", "")
state_des1 = np.array([p1[0], p1[2], 0, 0])
state_des2 = np.array([p2[0], p2[2], 0, 0])
state_des3 = np.array([p3[0], p3[2], 0, 0])
if task_args.get("consider_velocities", False):
Q = np.diag([5e-1, 5e-1, 5e-3, 5e-3])
else:
Q = np.diag([1e0, 1e0])
state_des1 = state_des1[:2]
state_des2 = state_des2[:2]
state_des3 = state_des3[:2]
success_fcn = partial(proximity_succeeded,
thold_dist=7.5e-2,
dims=[0, 1]) # min distance = 7cm
R = np.zeros((spec.act_space.flat_dim, spec.act_space.flat_dim))
# Create the tasks
subtask_1 = FinalRewTask(DesStateTask(spec, state_des1,
ExpQuadrErrRewFcn(Q, R),
success_fcn),
mode=FinalRewMode(time_dependent=True))
subtask_2 = FinalRewTask(DesStateTask(spec, state_des2,
ExpQuadrErrRewFcn(Q, R),
success_fcn),
mode=FinalRewMode(time_dependent=True))
subtask_3 = FinalRewTask(DesStateTask(spec, state_des3,
ExpQuadrErrRewFcn(Q, R),
success_fcn),
mode=FinalRewMode(time_dependent=True))
subtask_4 = FinalRewTask(DesStateTask(spec, state_des1,
ExpQuadrErrRewFcn(Q, R),
success_fcn),
mode=FinalRewMode(time_dependent=True))
subtask_5 = FinalRewTask(DesStateTask(spec, state_des2,
ExpQuadrErrRewFcn(Q, R),
success_fcn),
mode=FinalRewMode(time_dependent=True))
subtask_6 = FinalRewTask(DesStateTask(spec, state_des3,
ExpQuadrErrRewFcn(Q, R),
success_fcn),
mode=FinalRewMode(time_dependent=True))
task = FinalRewTask(
SequentialTasks(
[
subtask_1, subtask_2, subtask_3, subtask_4, subtask_5,
subtask_6
],
hold_rew_when_done=True,
verbose=True,
),
mode=FinalRewMode(always_positive=True),
factor=5e3,
)
masked_task = MaskedTask(self.spec, task, idcs)
# Additional tasks
task_check_bounds = create_check_all_boundaries_task(self.spec,
penalty=1e3)
if isinstance(self, Planar3LinkJointCtrlSim):
# Return the masked task and and additional task that ends the episode if the unmasked state is out of bound
return ParallelTasks([masked_task, task_check_bounds],
easily_satisfied=True)
else:
task_ts_discrepancy = create_task_space_discrepancy_task(
self.spec,
AbsErrRewFcn(q=0.5 * np.ones(2),
r=np.zeros(self.act_space.shape)))
# Return the masked task and and additional task that ends the episode if the unmasked state is out of bound
return ParallelTasks(
[masked_task, task_check_bounds, task_ts_discrepancy],
easily_satisfied=True)