add weights scaling for box pushing replanning

README.md

@@ -46,7 +46,7 @@ pip install -e .
 In case you want to use dm_control oder metaworld, you can install them by specifying extras
 
 ```bash 
-pip install -e .[dmc, metaworld]
+pip install -e .[dmc,metaworld]
 ```
 
 > **Note:**   

fancy_gym/black_box/black_box_wrapper.py

@@ -56,6 +56,7 @@ class BlackBoxWrapper(gym.ObservationWrapper):
         # reward computation
         self.reward_aggregation = reward_aggregation
 
+        # self.traj_gen.basis_gn.show_basis(plot=True)
         # spaces
         self.return_context_observation = not (learn_sub_trajectories or self.do_replanning)
         self.traj_gen_action_space = self._get_traj_gen_action_space()

fancy_gym/envs/__init__.py

@@ -68,8 +68,7 @@ DEFAULT_BB_DICT_ProDMP = {
     "wrappers": [],
     "trajectory_generator_kwargs": {
         'trajectory_generator_type': 'prodmp',
-        'duration': 2.0,
-        'weight_scale': 1.0,
+        'weights_scale': 1.0,
     },
     "phase_generator_kwargs": {
         'phase_generator_type': 'exp',
@@ -497,6 +496,8 @@ 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['black_box_kwargs']['replanning_schedule'] = lambda pos, vel, obs, action, t: t % 25 == 0
     register(
         id=_env_id,

fancy_gym/envs/mujoco/box_pushing/mp_wrapper.py

@@ -16,8 +16,8 @@ class MPWrapper(RawInterfaceWrapper):
             [False] * 7,  # joints gravity compensation
             [False] * 3,  # position of rod tip
             [False] * 4,  # orientation of rod
-            [False] * 3,  # position of box
-            [False] * 4,  # orientation of box
+            [True] * 3,  # position of box
+            [True] * 4,  # orientation of box
             [True] * 3,  # position of target
             [True] * 4,  # orientation of target
             # [True] * 1,  # time

fancy_gym/examples/examples_movement_primitives.py

@@ -157,17 +157,17 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
 if __name__ == '__main__':
     render = True
     # DMP
-    example_mp("HoleReacherDMP-v0", seed=10, iterations=5, render=render)
+    # example_mp("HoleReacherDMP-v0", seed=10, iterations=5, render=render)
 
     # ProMP
-    example_mp("HoleReacherProMP-v0", seed=10, iterations=5, render=render)
-    example_mp("BoxPushingTemporalSparseProMP-v0", seed=10, iterations=1, render=render)
+    # example_mp("HoleReacherProMP-v0", seed=10, iterations=5, render=render)
+    # example_mp("BoxPushingTemporalSparseProMP-v0", seed=10, iterations=1, render=render)
 
     # ProDMP
-    example_mp("BoxPushingDenseProDMP-v0", seed=10, iterations=1, render=render)
+    example_mp("BoxPushingDenseProDMP-v0", seed=10, iterations=16, render=render)
 
     # Altered basis functions
-    obs1 = example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=1, render=render)
+    # obs1 = example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=1, render=render)
 
     # Custom MP
-    example_fully_custom_mp(seed=10, iterations=1, render=render)
+    # example_fully_custom_mp(seed=10, iterations=1, render=render)

test/test_replanning_envs.py

@@ -0,0 +1,15 @@
+from itertools import chain
+
+import pytest
+
+import fancy_gym
+from test.utils import run_env, run_env_determinism
+
+Fancy_ProDMP_IDS = fancy_gym.ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS['ProDMP']
+
+All_ProDMP_IDS = fancy_gym.ALL_MOVEMENT_PRIMITIVE_ENVIRONMENTS['ProDMP']
+
+@pytest.mark.parametrize('env_id', Fancy_ProDMP_IDS)
+def test_prodmp_envs(env_id: str):
+    """Tests that ProDMP environments run without errors using random actions."""
+    run_env(env_id)