dodox/fancy_gym
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Merge remote-tracking branch 'original/legacy' into HEAD

Browse Source
This commit is contained in:
Dominik Moritz Roth 2023-10-11 13:58:23 +02:00
commit 34a16ea5fe
10 changed files with 166 additions and 35 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored
View File

@ -106,6 +106,7 @@ venv.bak/
# pycharm
.DS_Store
.idea
.vscode
#configs
/configs/db.cfg

14
fancy_gym/black_box/black_box_wrapper.py
View File

@ -55,6 +55,14 @@ class BlackBoxWrapper(gym.ObservationWrapper):
# self.traj_gen.set_mp_times(self.time_steps)
self.traj_gen.set_duration(self.duration, self.dt)
# check
self.tau_bound = [-np.inf, np.inf]
self.delay_bound = [-np.inf, np.inf]
if hasattr(self.traj_gen.phase_gn, "tau_bound"):
self.tau_bound = self.traj_gen.phase_gn.tau_bound
if hasattr(self.traj_gen.phase_gn, "delay_bound"):
self.delay_bound = self.traj_gen.phase_gn.delay_bound
# reward computation
self.reward_aggregation = reward_aggregation
@ -139,7 +147,8 @@ class BlackBoxWrapper(gym.ObservationWrapper):
position, velocity = self.get_trajectory(action)
position, velocity = self.env.set_episode_arguments(action, position, velocity)
traj_is_valid, position, velocity = self.env.preprocessing_and_validity_callback(action, position, velocity)
traj_is_valid, position, velocity = self.env.preprocessing_and_validity_callback(action, position, velocity,
self.tau_bound, self.delay_bound)
trajectory_length = len(position)
rewards = np.zeros(shape=(trajectory_length,))
@ -153,7 +162,8 @@ class BlackBoxWrapper(gym.ObservationWrapper):
if not traj_is_valid:
obs, trajectory_return, done, infos = self.env.invalid_traj_callback(action, position, velocity,
self.return_context_observation)
self.return_context_observation,
self.tau_bound, self.delay_bound)
return self.observation(obs), trajectory_return, done, infos
self.plan_steps += 1

10
fancy_gym/black_box/raw_interface_wrapper.py
View File

@ -52,7 +52,8 @@ class RawInterfaceWrapper(gym.Wrapper):
"""
return self.env.dt
def preprocessing_and_validity_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray) \
def preprocessing_and_validity_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray,
tau_bound: list = None, delay_bound: list = None ) \
-> Tuple[bool, np.ndarray, np.ndarray]:
"""
Used to preprocess the action and check if the desired trajectory is valid.
@ -61,6 +62,8 @@ class RawInterfaceWrapper(gym.Wrapper):
specified, else only traj_gen parameters
pos_traj: a vector instance of the raw position trajectory
vel_traj: a vector instance of the raw velocity trajectory
tau_bound: a list of two elements, the lower and upper bound of the trajectory length scaling factor
delay_bound: a list of two elements, the lower and upper bound of the time to wait before execute
Returns:
validity flag: bool, True if the raw trajectory is valid, False if not
pos_traj: a vector instance of the preprocessed position trajectory
@ -97,7 +100,8 @@ class RawInterfaceWrapper(gym.Wrapper):
"""
return True
def invalid_traj_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray) -> Tuple[np.ndarray, float, bool, dict]:
def invalid_traj_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray,
tau_bound: list, delay_bound: list) -> Tuple[np.ndarray, float, bool, dict]:
"""
Used to return a artificial return from the env if the desired trajectory is invalid.
Args:
@ -105,6 +109,8 @@ class RawInterfaceWrapper(gym.Wrapper):
specified, else only traj_gen parameters
pos_traj: a vector instance of the raw position trajectory
vel_traj: a vector instance of the raw velocity trajectory
tau_bound: a list of two elements, the lower and upper bound of the trajectory length scaling factor
delay_bound: a list of two elements, the lower and upper bound of the time to wait before execute
Returns:
obs: artificial observation if the trajectory is invalid, by default a zero vector
reward: artificial reward if the trajectory is invalid, by default 0

39
fancy_gym/envs/__init__.py
View File

@ -237,6 +237,12 @@ for reward_type in ["Dense", "TemporalSparse", "TemporalSpatialSparse"]:
entry_point='fancy_gym.envs.mujoco:BoxPushing{}'.format(reward_type),
max_episode_steps=MAX_EPISODE_STEPS_BOX_PUSHING,
)
register(
id='BoxPushingRandomInit{}-v0'.format(reward_type),
entry_point='fancy_gym.envs.mujoco:BoxPushing{}'.format(reward_type),
max_episode_steps=MAX_EPISODE_STEPS_BOX_PUSHING,
kwargs={"random_init": True}
)
# Here we use the same reward as in BeerPong-v0, but now consider after the release,
# only one time step, i.e. we simulate until the end of th episode
@ -500,7 +506,9 @@ for _v in _versions:
# ########################################################################################################################
## Box Pushing
_versions = ['BoxPushingDense-v0', 'BoxPushingTemporalSparse-v0', 'BoxPushingTemporalSpatialSparse-v0']
_versions = ['BoxPushingDense-v0', 'BoxPushingTemporalSparse-v0', 'BoxPushingTemporalSpatialSparse-v0',
'BoxPushingRandomInitDense-v0', 'BoxPushingRandomInitTemporalSparse-v0',
'BoxPushingRandomInitTemporalSpatialSparse-v0']
for _v in _versions:
_name = _v.split("-")
_env_id = f'{_name[0]}ProMP-{_name[1]}'
@ -518,6 +526,27 @@ for _v in _versions:
)
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_box_pushing_prodmp = deepcopy(DEFAULT_BB_DICT_ProDMP)
kwargs_dict_box_pushing_prodmp['wrappers'].append(mujoco.box_pushing.MPWrapper)
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'] = 0.3
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_scale'] = 0.3
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['auto_scale_basis'] = True
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['num_basis'] = 4
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['basis_bandwidth_factor'] = 3
kwargs_dict_box_pushing_prodmp['phase_generator_kwargs']['alpha_phase'] = 3
register(
id=_env_id,
entry_point='fancy_gym.utils.make_env_helpers:make_bb_env_helper',
kwargs=kwargs_dict_box_pushing_prodmp
)
ALL_FANCY_MOVEMENT_PRIMITIVE_ENVIRONMENTS["ProDMP"].append(_env_id)
for _v in _versions:
_name = _v.split("-")
_env_id = f'{_name[0]}ReplanProDMP-{_name[1]}'
@ -529,9 +558,7 @@ for _v in _versions:
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['weights_scale'] = 0.3
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_scale'] = 0.3
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['auto_scale_basis'] = True
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['goal_offset'] = 1.0
kwargs_dict_box_pushing_prodmp['trajectory_generator_kwargs']['disable_goal'] = True
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['num_basis'] = 5
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['num_basis'] = 4
kwargs_dict_box_pushing_prodmp['basis_generator_kwargs']['basis_bandwidth_factor'] = 3
kwargs_dict_box_pushing_prodmp['phase_generator_kwargs']['alpha_phase'] = 3
kwargs_dict_box_pushing_prodmp['black_box_kwargs']['max_planning_times'] = 4
@ -557,8 +584,8 @@ for _v in _versions:
kwargs_dict_tt_promp['name'] = _v
kwargs_dict_tt_promp['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_promp['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_promp['phase_generator_kwargs']['learn_tau'] = False
kwargs_dict_tt_promp['phase_generator_kwargs']['learn_delay'] = False
kwargs_dict_tt_promp['phase_generator_kwargs']['learn_tau'] = True
kwargs_dict_tt_promp['phase_generator_kwargs']['learn_delay'] = True
kwargs_dict_tt_promp['phase_generator_kwargs']['tau_bound'] = [0.8, 1.5]
kwargs_dict_tt_promp['phase_generator_kwargs']['delay_bound'] = [0.05, 0.15]
kwargs_dict_tt_promp['basis_generator_kwargs']['num_basis'] = 3

97
fancy_gym/envs/mujoco/box_pushing/box_pushing_env.py
View File

@ -26,7 +26,7 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
3. time-spatial-depend sparse reward
"""
def __init__(self, frame_skip: int = 10):
def __init__(self, frame_skip: int = 10, random_init: bool = False):
utils.EzPickle.__init__(**locals())
self._steps = 0
self.init_qpos_box_pushing = np.array([0., 0., 0., -1.5, 0., 1.5, 0., 0., 0., 0.6, 0.45, 0.0, 1., 0., 0., 0.])
@ -39,6 +39,7 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
self._desired_rod_quat = desired_rod_quat
self._episode_energy = 0.
self.random_init = random_init
MujocoEnv.__init__(self,
model_path=os.path.join(os.path.dirname(__file__), "assets", "box_pushing.xml"),
frame_skip=self.frame_skip,
@ -93,7 +94,7 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
def reset_model(self):
# rest box to initial position
self.set_state(self.init_qpos_box_pushing, self.init_qvel_box_pushing)
box_init_pos = np.array([0.4, 0.3, -0.01, 0.0, 0.0, 0.0, 1.0])
box_init_pos = self.sample_context() if self.random_init else np.array([0.4, 0.3, -0.01, 0.0, 0.0, 0.0, 1.0])
self.data.joint("box_joint").qpos = box_init_pos
# set target position
@ -160,6 +161,9 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
penalty -= v_coeff * abs(np.sum(q_dot_error[q_dot_error > 0.]))
return penalty
def _get_box_vel(self):
return self.data.body("box_0").cvel.copy()
def get_body_jacp(self, name):
id = mujoco.mj_name2id(self.model, 1, name)
jacp = np.zeros((3, self.model.nv))
@ -281,8 +285,8 @@ class BoxPushingEnvBase(MujocoEnv, utils.EzPickle):
return q
class BoxPushingDense(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10):
super(BoxPushingDense, self).__init__(frame_skip=frame_skip)
def __init__(self, frame_skip: int = 10, random_init: bool = False):
super(BoxPushingDense, self).__init__(frame_skip=frame_skip, random_init=random_init)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action):
joint_penalty = self._joint_limit_violate_penalty(qpos,
@ -304,14 +308,14 @@ class BoxPushingDense(BoxPushingEnvBase):
return reward
class BoxPushingTemporalSparse(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10):
super(BoxPushingTemporalSparse, self).__init__(frame_skip=frame_skip)
def __init__(self, frame_skip: int = 10, random_init: bool = False):
super(BoxPushingTemporalSparse, self).__init__(frame_skip=frame_skip, random_init=random_init)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action):
reward = 0.
joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
energy_cost = -0.0005 * np.sum(np.square(action))
energy_cost = -0.02 * np.sum(np.square(action))
tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
reward += joint_penalty + tcp_box_dist_reward + energy_cost
rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
@ -327,20 +331,23 @@ class BoxPushingTemporalSparse(BoxPushingEnvBase):
box_goal_pos_dist_reward = -3.5 * box_goal_dist * 100
box_goal_rot_dist_reward = -rotation_distance(box_quat, target_quat) / np.pi * 100
reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward
ep_end_joint_vel = -50. * np.linalg.norm(qvel)
reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward + ep_end_joint_vel
return reward
class BoxPushingTemporalSpatialSparse(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10):
super(BoxPushingTemporalSpatialSparse, self).__init__(frame_skip=frame_skip)
def __init__(self, frame_skip: int = 10, random_init: bool = False):
super(BoxPushingTemporalSpatialSparse, self).__init__(frame_skip=frame_skip, random_init=random_init)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action):
reward = 0.
joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
energy_cost = -0.0005 * np.sum(np.square(action))
energy_cost = -0.02 * np.sum(np.square(action))
tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
reward += joint_penalty + tcp_box_dist_reward + energy_cost
rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
@ -360,3 +367,71 @@ class BoxPushingTemporalSpatialSparse(BoxPushingEnvBase):
reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward
return reward
class BoxPushingTemporalSpatialSparse2(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10, random_init: bool = False):
super(BoxPushingTemporalSpatialSparse2, self).__init__(frame_skip=frame_skip, random_init=random_init)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action):
reward = 0.
joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
energy_cost = -0.0005 * np.sum(np.square(action))
tcp_box_dist_reward = -2 * np.clip(np.linalg.norm(box_pos - rod_tip_pos), 0.05, 100)
reward += joint_penalty + energy_cost + tcp_box_dist_reward
rod_inclined_angle = rotation_distance(rod_quat, desired_rod_quat)
if rod_inclined_angle > np.pi / 4:
reward -= rod_inclined_angle / (np.pi)
if not episode_end:
return reward
# Force the robot to stop at the end
reward += -50. * np.linalg.norm(qvel)
box_goal_dist = np.linalg.norm(box_pos - target_pos)
if box_goal_dist < 0.1:
box_goal_pos_dist_reward = np.clip(- 350. * box_goal_dist, -200, 0)
box_goal_rot_dist_reward = np.clip(- rotation_distance(box_quat, target_quat)/np.pi * 100., -100, 0)
reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward
else:
reward -= 300.
return reward
class BoxPushingNoConstraintSparse(BoxPushingEnvBase):
def __init__(self, frame_skip: int = 10, random_init: bool = False):
super(BoxPushingNoConstraintSparse, self).__init__(frame_skip=frame_skip, random_init=random_init)
def _get_reward(self, episode_end, box_pos, box_quat, target_pos, target_quat,
rod_tip_pos, rod_quat, qpos, qvel, action):
reward = 0.
joint_penalty = self._joint_limit_violate_penalty(qpos, qvel, enable_pos_limit=True, enable_vel_limit=True)
energy_cost = -0.0005 * np.sum(np.square(action))
reward += joint_penalty + energy_cost
if not episode_end:
return reward
box_goal_dist = np.linalg.norm(box_pos - target_pos)
box_goal_pos_dist_reward = -3.5 * box_goal_dist * 100
box_goal_rot_dist_reward = -rotation_distance(box_quat, target_quat) / np.pi * 100
reward += box_goal_pos_dist_reward + box_goal_rot_dist_reward + self._get_end_vel_penalty()
return reward
def _get_end_vel_penalty(self):
rot_coeff = 150.
pos_coeff = 150.
box_rot_pos_vel = self._get_box_vel()
box_rot_vel = box_rot_pos_vel[:3]
box_pos_vel = box_rot_pos_vel[3:]
return -rot_coeff * np.linalg.norm(box_rot_vel) - pos_coeff * np.linalg.norm(box_pos_vel)

11
fancy_gym/envs/mujoco/box_pushing/mp_wrapper.py
View File

@ -10,6 +10,17 @@ class MPWrapper(RawInterfaceWrapper):
# Random x goal + random init pos
@property
def context_mask(self):
if self.random_init:
return np.hstack([
[True] * 7, # joints position
[False] * 7, # joints velocity
[True] * 3, # position of box
[True] * 4, # orientation of box
[True] * 3, # position of target
[True] * 4, # orientation of target
# [True] * 1, # time
])
return np.hstack([
[False] * 7, # joints position
[False] * 7, # joints velocity

6
fancy_gym/envs/mujoco/reacher/reacher.py
View File

@ -79,11 +79,11 @@ class ReacherEnv(MujocoEnv, utils.EzPickle):
# I Quadrant
# self.goal = self.np_random.uniform(low=0, high=self.n_links / 10, size=2)
# II Quadrant
# self.goal = np.random.uniform(low=[-self.n_links / 10, 0], high=[0, self.n_links / 10], size=2)
# self.goal = self.np_random.uniform(low=[-self.n_links / 10, 0], high=[0, self.n_links / 10], size=2)
# II + III Quadrant
# self.goal = np.random.uniform(low=-self.n_links / 10, high=[0, self.n_links / 10], size=2)
# self.goal = self.np_random.uniform(low=-self.n_links / 10, high=[0, self.n_links / 10], size=2)
# I + II Quadrant
# self.goal = np.random.uniform(low=[-self.n_links / 10, 0], high=self.n_links, size=2)
# self.goal = self.np_random.uniform(low=[-self.n_links / 10, 0], high=self.n_links, size=2)
if np.linalg.norm(self.goal) < self.n_links / 10:
break

9
fancy_gym/envs/mujoco/table_tennis/mp_wrapper.py
View File

@ -29,15 +29,16 @@ class TT_MPWrapper(RawInterfaceWrapper):
def current_vel(self) -> Union[float, int, np.ndarray, Tuple]:
return self.data.qvel[:7].copy()
def preprocessing_and_validity_callback(self, action, pos_traj, vel_traj):
return self.check_traj_validity(action, pos_traj, vel_traj)
def preprocessing_and_validity_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray,
tau_bound: list, delay_bound:list):
return self.check_traj_validity(action, pos_traj, vel_traj, tau_bound, delay_bound)
def set_episode_arguments(self, action, pos_traj, vel_traj):
return pos_traj, vel_traj
def invalid_traj_callback(self, action: np.ndarray, pos_traj: np.ndarray, vel_traj: np.ndarray,
return_contextual_obs: bool) -> Tuple[np.ndarray, float, bool, dict]:
return self.get_invalid_traj_step_return(action, pos_traj, return_contextual_obs)
return_contextual_obs: bool, tau_bound:list, delay_bound:list) -> Tuple[np.ndarray, float, bool, dict]:
return self.get_invalid_traj_step_return(action, pos_traj, return_contextual_obs, tau_bound, delay_bound)
class TTVelObs_MPWrapper(TT_MPWrapper):

10
fancy_gym/envs/mujoco/table_tennis/table_tennis_env.py
View File

@ -5,7 +5,7 @@ from gym import utils, spaces
from gym.envs.mujoco import MujocoEnv
from fancy_gym.envs.mujoco.table_tennis.table_tennis_utils import is_init_state_valid, magnus_force
from fancy_gym.envs.mujoco.table_tennis.table_tennis_utils import jnt_pos_low, jnt_pos_high, delay_bound, tau_bound
from fancy_gym.envs.mujoco.table_tennis.table_tennis_utils import jnt_pos_low, jnt_pos_high
import mujoco
@ -225,7 +225,7 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
init_ball_state = self._generate_random_ball(random_pos=random_pos, random_vel=random_vel)
return init_ball_state
def _get_traj_invalid_penalty(self, action, pos_traj):
def _get_traj_invalid_penalty(self, action, pos_traj, tau_bound, delay_bound):
tau_invalid_penalty = 3 * (np.max([0, action[0] - tau_bound[1]]) + np.max([0, tau_bound[0] - action[0]]))
delay_invalid_penalty = 3 * (np.max([0, action[1] - delay_bound[1]]) + np.max([0, delay_bound[0] - action[1]]))
violate_high_bound_error = np.mean(np.maximum(pos_traj - jnt_pos_high, 0))
@ -234,9 +234,9 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
violate_high_bound_error + violate_low_bound_error
return -invalid_penalty
def get_invalid_traj_step_return(self, action, pos_traj, contextual_obs):
def get_invalid_traj_step_return(self, action, pos_traj, contextual_obs, tau_bound, delay_bound):
obs = self._get_obs() if contextual_obs else np.concatenate([self._get_obs(), np.array([0])]) # 0 for invalid traj
penalty = self._get_traj_invalid_penalty(action, pos_traj)
penalty = self._get_traj_invalid_penalty(action, pos_traj, tau_bound, delay_bound)
return obs, penalty, True, {
"hit_ball": [False],
"ball_returned_success": [False],
@ -247,7 +247,7 @@ class TableTennisEnv(MujocoEnv, utils.EzPickle):
}
@staticmethod
def check_traj_validity(action, pos_traj, vel_traj):
def check_traj_validity(action, pos_traj, vel_traj, tau_bound, delay_bound):
time_invalid = action[0] > tau_bound[1] or action[0] < tau_bound[0] \
or action[1] > delay_bound[1] or action[1] < delay_bound[0]
if time_invalid or np.any(pos_traj > jnt_pos_high) or np.any(pos_traj < jnt_pos_low):

4
fancy_gym/examples/pd_control_gain_tuning.py
View File

@ -31,8 +31,8 @@ ax = fig.add_subplot(1, 1, 1)
img = ax.imshow(env.env.render(mode="rgb_array"))
fig.show()
for t, pos_vel in enumerate(zip(pos, vel)):
actions = env.tracking_controller.get_action(pos_vel[0], pos_vel[1], env.current_vel, env.current_pos)
for t, (des_pos, des_vel) in enumerate(zip(pos, vel)):
actions = env.tracking_controller.get_action(des_pos, des_vel, env.current_pos, env.current_vel)
actions = np.clip(actions, env.env.action_space.low, env.env.action_space.high)
_, _, _, _ = env.env.step(actions)
if t % 15 == 0: