check time validity before pos validity

fancy_gym/black_box/black_box_wrapper.py

@@ -164,6 +164,9 @@ class BlackBoxWrapper(gym.ObservationWrapper):
 
     def step(self, action: np.ndarray):
         """ This function generates a trajectory based on a MP and then does the usual loop over reset and step"""
+        time_valid = self.env.check_time_validity(action)
+
+        if time_valid:
 
             if self.plan_counts == 0:
                 self.tau_first_prediction = action[0]
@@ -253,6 +256,9 @@ class BlackBoxWrapper(gym.ObservationWrapper):
             else:
                 obs, trajectory_return, done, infos = self.env.invalid_traj_callback(action, position, velocity)
                 return self.observation(obs), trajectory_return, done, infos
+        else:
+                obs, trajectory_return, done, infos = self.env.time_invalid_traj_callback(action)
+                return self.observation(obs), trajectory_return, done, infos
     def render(self, **kwargs):
         """Only set render options here, such that they can be used during the rollout.
         This only needs to be called once"""

fancy_gym/envs/__init__.py

@@ -560,7 +560,7 @@ for _v in _versions:
     _name = _v.split("-")
     _env_id = f'{_name[0]}ProDMP-{_name[1]}'
     kwargs_dict_tt_prodmp = deepcopy(DEFAULT_BB_DICT_ProDMP)
-    kwargs_dict_tt_prodmp['wrappers'].append(mujoco.box_pushing.MPWrapper)
+    kwargs_dict_tt_prodmp['wrappers'].append(mujoco.table_tennis.MPWrapper)
     kwargs_dict_tt_prodmp['name'] = _v
     kwargs_dict_tt_prodmp['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_prodmp['controller_kwargs']['d_gains'] = 0.5 * np.array([0.1, 0.4, 0.2, 0.4, 0.1, 0.4, 0.1])

fancy_gym/envs/mujoco/table_tennis/mp_wrapper.py

@@ -28,12 +28,31 @@ class MPWrapper(RawInterfaceWrapper):
     def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
         return self.data.qvel[:7].copy()
 
+    def check_time_validity(self, action):
+        return action[0] <= tau_bound[1] and action[0] >= tau_bound[0] \
+               and action[1] <= delay_bound[1] and action[1] >= delay_bound[0]
+
+    def time_invalid_traj_callback(self, action) \
+        -> Tuple[np.ndarray, float, bool, dict]:
+        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]]))
+        invalid_penalty = tau_invalid_penalty + delay_invalid_penalty
+        obs = np.concatenate([self.get_obs(), np.array([0])])
+        return obs, -invalid_penalty, True, {
+        "hit_ball": [False],
+        "ball_returned_success": [False],
+        "land_dist_error": [10.],
+        "is_success": [False],
+        'trajectory_length': 1,
+        "num_steps": [1]
+        }
+
     def episode_callback(self, action, pos_traj, vel_traj):
         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):
-            return False
             return True
+        return False
 
     def invalid_traj_callback(self, action, pos_traj: np.ndarray, vel_traj: np.ndarray) \
             -> Tuple[np.ndarray, float, bool, dict]:
@@ -43,7 +62,8 @@ class MPWrapper(RawInterfaceWrapper):
         violate_low_bound_error = np.mean(np.maximum(jnt_pos_low - pos_traj, 0))
         invalid_penalty = tau_invalid_penalty + delay_invalid_penalty + \
                           violate_high_bound_error + violate_low_bound_error
-        return self.get_obs(), -invalid_penalty, True, {
+        obs = np.concatenate([self.get_obs(), np.array([0])])
+        return obs, -invalid_penalty, True, {
         "hit_ball": [False],
         "ball_returned_success": [False],
         "land_dist_error": [10.],