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temporal updates

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This commit is contained in:
Hongyi Zhou 2022-11-08 10:15:43 +01:00
parent 2a39a72af0
commit d384e6e764
4 changed files with 53 additions and 11 deletions
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15
fancy_gym/black_box/black_box_wrapper.py
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@ -81,13 +81,15 @@ class BlackBoxWrapper(gym.ObservationWrapper):
self.verbose = verbose
# condition value
self.desired_conditioning = True
self.desired_conditioning = False
self.condition_pos = None
self.condition_vel = None
self.max_planning_times = max_planning_times
self.plan_counts = 0
self.tau_first_prediction = None
def observation(self, observation):
# return context space if we are
if self.return_context_observation:
@ -96,7 +98,9 @@ class BlackBoxWrapper(gym.ObservationWrapper):
return observation.astype(self.observation_space.dtype)
def get_trajectory(self, action: np.ndarray) -> Tuple:
duration = self.duration
# duration = self.duration
# duration = self.duration - self.current_traj_steps * self.dt
duration = 2.
if self.learn_sub_trajectories:
duration = None
# reset with every new call as we need to set all arguments, such as tau, delay, again.
@ -117,7 +121,8 @@ class BlackBoxWrapper(gym.ObservationWrapper):
self.condition_pos = torch.as_tensor(self.condition_pos, dtype=torch.float32)
self.condition_vel = torch.as_tensor(self.condition_vel, dtype=torch.float32)
self.traj_gen.set_boundary_conditions(bc_time, self.condition_pos, self.condition_vel)
self.traj_gen.set_duration(duration, self.dt)
# self.traj_gen.set_duration(duration, self.dt)
self.traj_gen.set_duration(self.tau_first_prediction, self.dt)
# traj_dict = self.traj_gen.get_trajs(get_pos=True, get_vel=True)
position = get_numpy(self.traj_gen.get_traj_pos())
velocity = get_numpy(self.traj_gen.get_traj_vel())
@ -160,6 +165,9 @@ 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"""
if self.plan_counts == 0:
self.tau_first_prediction = action[0]
## tricky part, only use weights basis
# basis_weights = action.reshape(7, -1)
# goal_weights = np.zeros((7, 1))
@ -253,5 +261,6 @@ class BlackBoxWrapper(gym.ObservationWrapper):
def reset(self, *, seed: Optional[int] = None, return_info: bool = False, options: Optional[dict] = None):
self.current_traj_steps = 0
self.plan_counts = 0
self.tau_first_prediction = None
self.traj_gen.reset()
return super(BlackBoxWrapper, self).reset()

33
fancy_gym/envs/__init__.py
View File

@ -254,7 +254,7 @@ for ctxt_dim in [2, 4]:
register(
id='TableTennis{}D-v0'.format(ctxt_dim),
entry_point='fancy_gym.envs.mujoco:TableTennisEnv',
max_episode_steps=350,
max_episode_steps=500,
kwargs={
"ctxt_dim": ctxt_dim,
'frame_skip': 4
@ -523,7 +523,7 @@ for _v in _versions:
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['alpha'] = 10.
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['basis_bandwidth_factor'] = 3 # 3.5, 4 to try
kwargs_dict_box_pushing_prodmp['phase_generator_kwargs']['alpha_phase'] = 3
kwargs_dict_box_pushing_prodmp['black_box_kwargs']['max_planning_times'] = 2
kwargs_dict_box_pushing_prodmp['black_box_kwargs']['max_planning_times'] = 4
kwargs_dict_box_pushing_prodmp['black_box_kwargs']['replanning_schedule'] = lambda pos, vel, obs, action, t : t % 25 == 0
register(
id=_env_id,
@ -555,6 +555,35 @@ for _v in _versions:
kwargs=kwargs_dict_tt_promp
)
ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProMP"].append(_env_id)
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['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])
kwargs_dict_tt_prodmp['trajectory_generator_kwargs']['weights_scale'] = 1.0
kwargs_dict_tt_prodmp['trajectory_generator_kwargs']['goal_scale'] = 1.0
kwargs_dict_tt_prodmp['trajectory_generator_kwargs']['auto_scale_basis'] = True
kwargs_dict_tt_prodmp['trajectory_generator_kwargs']['goal_offset'] = 1.0
kwargs_dict_tt_prodmp['phase_generator_kwargs']['learn_tau'] = True
kwargs_dict_tt_prodmp['phase_generator_kwargs']['learn_delay'] = True
kwargs_dict_tt_prodmp['basis_generator_kwargs']['num_basis'] = 2
kwargs_dict_tt_prodmp['basis_generator_kwargs']['alpha'] = 25.
kwargs_dict_tt_prodmp['basis_generator_kwargs']['basis_bandwidth_factor'] = 3 # 3.5, 4 to try
kwargs_dict_tt_prodmp['basis_generator_kwargs']['pre_compute_length_factor'] = 5
kwargs_dict_tt_prodmp['phase_generator_kwargs']['alpha_phase'] = 3
# kwargs_dict_tt_prodmp['black_box_kwargs']['duration'] = 4.
kwargs_dict_tt_prodmp['black_box_kwargs']['max_planning_times'] = 4
kwargs_dict_tt_prodmp['black_box_kwargs']['replanning_schedule'] = lambda pos, vel, obs, action, t : t % 50 == 0
register(
id=_env_id,
entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_tt_prodmp
)
ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProDMP"].append(_env_id)
#
# ## Walker2DJump
# _versions = ['Walker2DJump-v0']

6
fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py
View File

@ -127,8 +127,8 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
def reset_model(self):
self._steps = 0
self._init_ball_state = self._generate_valid_init_ball(random_pos=True, random_vel=False)
self._goal_pos = self._generate_goal_pos(random=True)
self._init_ball_state = self._generate_valid_init_ball(random_pos=False, random_vel=False)
self._goal_pos = self._generate_goal_pos(random=False)
self.data.joint("tar_x").qpos = self._init_ball_state[0]
self.data.joint("tar_y").qpos = self._init_ball_state[1]
self.data.joint("tar_z").qpos = self._init_ball_state[2]
@ -214,6 +214,6 @@ if __name__ == "__main__":
for _ in range(2000):
env.render("human")
obs, reward, done, info = env.step(np.zeros(7))
print(reward)
# print(reward)
if done:
break

10
fancy_gym/examples/examples_movement_primitives.py
View File

@ -40,6 +40,8 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
# Now the action space is not the raw action but the parametrization of the trajectory generator,
# such as a ProMP
ac = env.action_space.sample()
# ac[0] = 0.6866657733917236
# ac[1] = 0.08587364107370377
# This executes a full trajectory and gives back the context (obs) of the last step in the trajectory, or the
# full observation space of the last step, if replanning/sub-trajectory learning is used. The 'reward' is equal
# to the return of a trajectory. Default is the sum over the step-wise rewards.
@ -50,7 +52,8 @@ def example_mp(env_name="HoleReacherProMP-v0", seed=1, iterations=1, render=True
if done:
# print(reward)
obs = env.reset()
print("steps: {}".format(info["num_steps"][-1]))
print("=================New Episode======================")
# print("steps: {}".format(info["num_steps"][-1]))
def example_custom_mp(env_name="Reacher5dProMP-v0", seed=1, iterations=1, render=True):
@ -158,17 +161,18 @@ def example_fully_custom_mp(seed=1, iterations=1, render=True):
if __name__ == '__main__':
render = True
render = False
# DMP
# 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("TableTennis4DProMP-v0", seed=10, iterations=10, render=True)
# example_mp("TableTennis4DProMP-v0", seed=10, iterations=10, render=True)
# ProDMP
# example_mp("BoxPushingDenseProDMP-v0", seed=10, iterations=16, render=render)
example_mp("TableTennis4DProDMP-v0", seed=10, iterations=5000, render=render)
# Altered basis functions
# obs1 = example_custom_mp("Reacher5dProMP-v0", seed=10, iterations=1, render=render)