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

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hongyi.zhou 2023-01-27 14:38:25 +01:00
commit 67f684cf14
4 changed files with 5 additions and 5 deletions
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4
fancy_gym/black_box/black_box_wrapper.py
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@ -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) clipped_params = np.clip(action, self.traj_gen_action_space.low, self.traj_gen_action_space.high)
self.traj_gen.set_params(clipped_params) 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 # 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. # 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_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 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) self.traj_gen.set_duration(duration, self.dt)
# traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True) # traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
position = get_numpy(self.traj_gen.get_traj_pos()) position = get_numpy(self.traj_gen.get_traj_pos())

2
fancy_gym/examples/examples_movement_primitives.py
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@ -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. # Just make sure the correct mode is set before executing the step.
env.render(mode="human") env.render(mode="human")
else: else:
env.render(mode=None) env.render()
# Now the action space is not the raw action but the parametrization of the trajectory generator, # Now the action space is not the raw action but the parametrization of the trajectory generator,
# such as a ProMP # such as a ProMP

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

2
setup.py
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@ -35,7 +35,7 @@ setup(
extras_require=extras, extras_require=extras,
install_requires=[ install_requires=[
'gym[mujoco]<0.25.0,>=0.24.1', '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")], packages=[package for package in find_packages() if package.startswith("fancy_gym")],
package_data={ package_data={