fix tau bound and init bound bug

fancy_gym/black_box/black_box_wrapper.py

@@ -55,6 +55,14 @@ class BlackBoxWrapper(gym.ObservationWrapper):
         # self.traj_gen.set_mp_times(self.time_steps)
         self.traj_gen.set_duration(self.duration, self.dt)
 
+        # check
+        self.tau_bound = [-np.inf, np.inf]
+        self.delay_bound = [-np.inf, np.inf]
+        if hasattr(self.traj_gen.phase_gn, "tau_bound"):
+            self.tau_bound = self.traj_gen.phase_gn.tau_bound
+        if hasattr(self.traj_gen.phase_gn, "delay_bound"):
+            self.delay_bound = self.traj_gen.phase_gn.delay_bound
+
         # reward computation
         self.reward_aggregation = reward_aggregation
 
@@ -139,7 +147,8 @@ class BlackBoxWrapper(gym.ObservationWrapper):
 
         position, velocity = self.get_trajectory(action)
         position, velocity = self.env.set_episode_arguments(action, position, velocity)
-        traj_is_valid, position, velocity = self.env.preprocessing_and_validity_callback(action, position, velocity)
+        traj_is_valid, position, velocity = self.env.preprocessing_and_validity_callback(action, position, velocity,
+                                                                                         self.tau_bound, self.delay_bound)
 
         trajectory_length = len(position)
         rewards = np.zeros(shape=(trajectory_length,))
@@ -153,7 +162,8 @@ class BlackBoxWrapper(gym.ObservationWrapper):
 
         if not traj_is_valid:
             obs, trajectory_return, done, infos = self.env.invalid_traj_callback(action, position, velocity,
-                                                                                 self.return_context_observation)
+                                                                                 self.return_context_observation,
+                                                                                 self.tau_bound, self.delay_bound)
             return self.observation(obs), trajectory_return, done, infos
 
         self.plan_steps += 1

fancy_gym/black_box/raw_interface_wrapper.py

@@ -52,7 +52,8 @@ class RawInterfaceWrapper(gym.Wrapper):
         """
         return self.env.dt
 
-    def preprocessing_and_validity_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray) \
+    def preprocessing_and_validity_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray,
+                                            tau_bound: list = None, delay_bound: list = None ) \
             -> Tuple[bool, np.ndarray, np.ndarray]:
         """
         Used to preprocess the action and check if the desired trajectory is valid.
@@ -61,6 +62,8 @@ class RawInterfaceWrapper(gym.Wrapper):
             specified, else only traj_gen parameters
             pos_traj: a vector instance of the raw position trajectory
             vel_traj: a vector instance of the raw velocity trajectory
+            tau_bound: a list of two elements, the lower and upper bound of the trajectory length scaling factor
+            delay_bound: a list of two elements, the lower and upper bound of the time to wait before execute
         Returns:
             validity flag: bool, True if the raw trajectory is valid, False if not
             pos_traj: a vector instance of the preprocessed position trajectory 
@@ -97,7 +100,8 @@ class RawInterfaceWrapper(gym.Wrapper):
         """
         return True
 
-    def invalid_traj_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray) -> Tuple[np.ndarray, float, bool, dict]:
+    def invalid_traj_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray,
+                              tau_bound: list, delay_bound: list) -> Tuple[np.ndarray, float, bool, dict]:
         """
         Used to return a artificial return from the env if the desired trajectory is invalid.
         Args:
@@ -105,6 +109,8 @@ class RawInterfaceWrapper(gym.Wrapper):
             specified, else only traj_gen parameters
             pos_traj: a vector instance of the raw position trajectory
             vel_traj: a vector instance of the raw velocity trajectory
+            tau_bound: a list of two elements, the lower and upper bound of the trajectory length scaling factor
+            delay_bound: a list of two elements, the lower and upper bound of the time to wait before execute
         Returns:
             obs: artificial observation if the trajectory is invalid, by default a zero vector
             reward: artificial reward if the trajectory is invalid, by default 0

fancy_gym/envs/__init__.py

@@ -557,8 +557,8 @@ for _v in _versions:
     kwargs_dict_tt_promp['name'] = _v
     kwargs_dict_tt_promp['controller_kwargs']['p_gains'] = 0.5 * np.array([1.0, 4.0, 2.0, 4.0, 1.0, 4.0, 1.0])
     kwargs_dict_tt_promp['controller_kwargs']['d_gains'] = 0.5 * np.array([0.1, 0.4, 0.2, 0.4, 0.1, 0.4, 0.1])
-    kwargs_dict_tt_promp['phase_generator_kwargs']['learn_tau'] = False
+    kwargs_dict_tt_promp['phase_generator_kwargs']['learn_tau'] = True
-    kwargs_dict_tt_promp['phase_generator_kwargs']['learn_delay'] = False
+    kwargs_dict_tt_promp['phase_generator_kwargs']['learn_delay'] = True
     kwargs_dict_tt_promp['phase_generator_kwargs']['tau_bound'] = [0.8, 1.5]
     kwargs_dict_tt_promp['phase_generator_kwargs']['delay_bound'] = [0.05, 0.15]
     kwargs_dict_tt_promp['basis_generator_kwargs']['num_basis'] = 3

fancy_gym/envs/mujoco/table_tennis/mp_wrapper.py

@@ -29,15 +29,16 @@ class TT_MPWrapper(RawInterfaceWrapper):
     def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
         return self.data.qvel[:7].copy()
 
-    def preprocessing_and_validity_callback(self, action, pos_traj, vel_traj):
+    def preprocessing_and_validity_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray,
-        return self.check_traj_validity(action, pos_traj, vel_traj)
+                                            tau_bound: list, delay_bound:list):
+        return self.check_traj_validity(action, pos_traj, vel_traj, tau_bound, delay_bound)
 
     def set_episode_arguments(self, action, pos_traj, vel_traj):
         return pos_traj, vel_traj
 
     def invalid_traj_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray,
-                              return_contextual_obs: bool) -> Tuple[np.ndarray, float, bool, dict]:
+                              return_contextual_obs: bool, tau_bound:list, delay_bound:list) -> Tuple[np.ndarray, float, bool, dict]:
-        return self.get_invalid_traj_step_return(action, pos_traj, return_contextual_obs)
+        return self.get_invalid_traj_step_return(action, pos_traj, return_contextual_obs, tau_bound, delay_bound)
 
 class TTVelObs_MPWrapper(TT_MPWrapper):
 

fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py

@@ -5,7 +5,7 @@ from gym import utils, spaces
 from gym.envs.mujoco import MujocoEnv
 
 from fancy_gym.envs.mujoco.table_tennis.table_tennis_utils import is_init_state_valid, magnus_force
-from fancy_gym.envs.mujoco.table_tennis.table_tennis_utils import jnt_pos_low, jnt_pos_high, delay_bound, tau_bound
+from fancy_gym.envs.mujoco.table_tennis.table_tennis_utils import jnt_pos_low, jnt_pos_high
 
 import mujoco
 
@@ -225,7 +225,7 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
             init_ball_state = self._generate_random_ball(random_pos=random_pos, random_vel=random_vel)
         return init_ball_state
 
-    def _get_traj_invalid_penalty(self, action, pos_traj):
+    def _get_traj_invalid_penalty(self, action, pos_traj, tau_bound, delay_bound):
         tau_invalid_penalty = 3 * (np.max([0, action[0] - tau_bound[1]]) + np.max([0, tau_bound[0] - action[0]]))
         delay_invalid_penalty = 3 * (np.max([0, action[1] - delay_bound[1]]) + np.max([0, delay_bound[0] - action[1]]))
         violate_high_bound_error = np.mean(np.maximum(pos_traj - jnt_pos_high, 0))
@@ -234,9 +234,9 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
                           violate_high_bound_error + violate_low_bound_error
         return -invalid_penalty
 
-    def get_invalid_traj_step_return(self, action, pos_traj, contextual_obs):
+    def get_invalid_traj_step_return(self, action, pos_traj, contextual_obs, tau_bound, delay_bound):
         obs = self._get_obs() if contextual_obs else np.concatenate([self._get_obs(), np.array([0])]) # 0 for invalid traj
-        penalty = self._get_traj_invalid_penalty(action, pos_traj)
+        penalty = self._get_traj_invalid_penalty(action, pos_traj, tau_bound, delay_bound)
         return obs, penalty, True, {
             "hit_ball": [False],
             "ball_returned_success": [False],
@@ -247,7 +247,7 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
         }
 
     @staticmethod
-    def check_traj_validity(action, pos_traj, vel_traj):
+    def check_traj_validity(action, pos_traj, vel_traj, tau_bound, delay_bound):
         time_invalid = action[0] > tau_bound[1] or action[0] < tau_bound[0] \
                      or action[1] > delay_bound[1] or action[1] < delay_bound[0]
         if time_invalid or np.any(pos_traj > jnt_pos_high) or np.any(pos_traj < jnt_pos_low):