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update according to reviews opinion & fix bugs in box pushing IK

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This commit is contained in:
Hongyi Zhou
2022-11-20 21:56:32 +01:00
parent fc3051bf57
commit 2674bf80fe
10 changed files with 94 additions and 185 deletions
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fancy_gym/black_box/black_box_wrapper.py
+12 -16
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@@ -24,7 +24,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
Callable[[np.ndarray, np.ndarray, np.ndarray, np.ndarray, int], bool]] = None,
reward_aggregation: Callable[[np.ndarray], float] = np.sum,
max_planning_times: int = None,
desired_traj_bc: bool = False
condition_on_desired: bool = False
):
"""
gym.Wrapper for leveraging a black box approach with a trajectory generator.
@@ -71,12 +71,12 @@ class BlackBoxWrapper(gym.ObservationWrapper):
self.verbose = verbose
# condition value
self.desired_traj_bc = desired_traj_bc
self.condition_on_desired = condition_on_desired
self.condition_pos = None
self.condition_vel = None
self.max_planning_times = max_planning_times
self.plan_counts = 0
self.plan_steps = 0
def observation(self, observation):
# return context space if we are
@@ -98,15 +98,11 @@ class BlackBoxWrapper(gym.ObservationWrapper):
bc_time = np.array(0 if not self.do_replanning else self.current_traj_steps * self.dt)
# TODO we could think about initializing with the previous desired value in order to have a smooth transition
# at least from the planning point of view.
# self.traj_gen.set_boundary_conditions(bc_time, self.current_pos, self.current_vel)
if self.current_traj_steps == 0:
self.condition_pos = self.current_pos
self.condition_vel = self.current_vel
bc_time = torch.as_tensor(bc_time, dtype=torch.float32)
self.condition_pos = torch.as_tensor(self.condition_pos, dtype=torch.float32)
self.condition_vel = torch.as_tensor(self.condition_vel, dtype=torch.float32)
self.traj_gen.set_boundary_conditions(bc_time, self.condition_pos, self.condition_vel)
condition_pos = self.condition_pos if self.condition_pos is not None else self.current_pos
condition_vel = self.condition_vel if self.condition_vel is not None else self.current_vel
self.traj_gen.set_boundary_conditions(bc_time, condition_pos, condition_vel)
self.traj_gen.set_duration(duration, self.dt)
# traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
position = get_numpy(self.traj_gen.get_traj_pos())
@@ -164,7 +160,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
infos = dict()
done = False
self.plan_counts += 1
self.plan_steps += 1
for t, (pos, vel) in enumerate(zip(position, velocity)):
step_action = self.tracking_controller.get_action(pos, vel, self.current_pos, self.current_vel)
c_action = np.clip(step_action, self.env.action_space.low, self.env.action_space.high)
@@ -186,11 +182,11 @@ class BlackBoxWrapper(gym.ObservationWrapper):
if done or self.replanning_schedule(self.current_pos, self.current_vel, obs, c_action,
t + 1 + self.current_traj_steps):
if self.max_planning_times is not None and self.plan_counts >= self.max_planning_times:
if self.max_planning_times is not None and self.plan_steps >= self.max_planning_times:
continue
self.condition_pos = pos if self.desired_traj_bc else self.current_pos
self.condition_vel = vel if self.desired_traj_bc else self.current_vel
self.condition_pos = pos if self.condition_on_desired else None
self.condition_vel = vel if self.condition_on_desired else None
break
@@ -215,6 +211,6 @@ class BlackBoxWrapper(gym.ObservationWrapper):
def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
self.current_traj_steps = 0
self.plan_counts = 0
self.plan_steps = 0
self.traj_gen.reset()
return super(BlackBoxWrapper, self).reset()