diff --git a/fancy_gym/black_box/black_box_wrapper.py b/fancy_gym/black_box/black_box_wrapper.py
index 908a664..6ba6cd9 100644
--- a/fancy_gym/black_box/black_box_wrapper.py
+++ b/fancy_gym/black_box/black_box_wrapper.py
@@ -150,7 +150,9 @@ class BlackBoxWrapper(gym.ObservationWrapper):
         """ This function generates a trajectory based on a MP and then does the usual loop over reset and step"""
 
         ## tricky part, only use weights basis
-        weights_basis = action.reshape(-1, 7)
+        basis_weights = action.reshape(7, -1)
+        goal_weights = np.zeros((7, 1))
+        action = np.concatenate((basis_weights, goal_weights), axis=1).flatten()
 
         # TODO remove this part, right now only needed for beer pong
         mp_params, env_spec_params = self.env.episode_callback(action, self.traj_gen)
diff --git a/fancy_gym/envs/__init__.py b/fancy_gym/envs/__init__.py
index e2bea2e..1ad261f 100644
--- a/fancy_gym/envs/__init__.py
+++ b/fancy_gym/envs/__init__.py
@@ -496,11 +496,11 @@ for _v in _versions:
     kwargs_dict_box_pushing_prodmp['name'] = _v
     kwargs_dict_box_pushing_prodmp['controller_kwargs']['p_gains'] = 0.01 * np.array([120., 120., 120., 120., 50., 30., 10.])
     kwargs_dict_box_pushing_prodmp['controller_kwargs']['d_gains'] = 0.01 * np.array([10., 10., 10., 10., 6., 5., 3.])
-    # kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['weights_scale'] = np.array([3.4944e+01, 4.3734e+01, 9.6711e+01, 2.4429e+02, 5.8272e+02])
-    # kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_scale'] = 3.1264e-01
-    kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['weights_scale'] = 0.3 * np.array([100., 166., 500., 1000.])
-    kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_scale'] = 0.3 * 1.
-    kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['num_basis'] = 4
+    kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['weights_scale'] = np.array([3.4944e+01, 4.3734e+01, 9.6711e+01, 2.4429e+02, 5.8272e+02])
+    kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_scale'] = 3.1264e-01
+    # kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['weights_scale'] = 0.3 * np.array([100., 166., 500., 1000.])
+    # kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_scale'] = 0.3 * 1.
+    kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['num_basis'] = 5
     kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['alpha'] = 10.
     kwargs_dict_box_pushing_prodmp['black_box_kwargs']['max_replan_times'] = 4
     kwargs_dict_box_pushing_prodmp['black_box_kwargs']['replanning_schedule'] = lambda pos, vel, obs, action, t : t % 25 == 0