Merge branch 'master' into 55-table-tennis-dev

fancy_gym/black_box/black_box_wrapper.py

@@ -94,14 +94,14 @@ class BlackBoxWrapper(gym.ObservationWrapper):
 
         clipped_params = np.clip(action, self.traj_gen_action_space.low, self.traj_gen_action_space.high)
         self.traj_gen.set_params(clipped_params)
-        bc_time = np.array(0 if not self.do_replanning else self.current_traj_steps * self.dt)
+        init_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.
 
         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_initial_conditions(init_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())

fancy_gym/examples/examples_movement_primitives.py

@@ -33,7 +33,7 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
             # Just make sure the correct mode is set before executing the step.
             env.render(mode="human")
         else:
-            env.render(mode=None)
+            env.render()
 
         # Now the action space is not the raw action but the parametrization of the trajectory generator,
         # such as a ProMP

fancy_gym/examples/examples_open_ai.py

@@ -22,7 +22,7 @@ def example_mp(env_name, seed=1, render=True):
         if render and i % 2 == 0:
             env.render(mode="human")
         else:
-            env.render(mode=None)
+            env.render()
         ac = env.action_space.sample()
         obs, reward, done, info = env.step(ac)
         returns += reward

setup.py

@@ -35,7 +35,7 @@ setup(
     extras_require=extras,
     install_requires=[
         'gym[mujoco]<0.25.0,>=0.24.1',
-        'mp_pytorch @ git+https://github.com/ALRhub/MP_PyTorch.git@main'
+        'mp_pytorch<=0.1.3'
     ],
     packages=[package for package in find_packages() if package.startswith("fancy_gym")],
     package_data={