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Remove useless code

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This commit is contained in:
wty-yy
2026-04-07 11:06:02 +08:00
parent 090fcc093a
commit 7558467aca
13 changed files with 170 additions and 106 deletions
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55
legged_gym/envs/base/legged_robot.py
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@@ -129,6 +129,9 @@ class LeggedRobot(BaseTask):
# compute observations, rewards, resets, ...
self.check_termination()
self.compute_reward()
# resample commands must after reward computing
resampling_env_ids = ((self.commands_resampling_step <= 0.0) * (self.episode_length_buf < self.max_episode_length - 1)).nonzero(as_tuple=False).flatten()
self._resample_commands(resampling_env_ids)
env_ids = self.reset_buf.nonzero(as_tuple=False).flatten()
self.reset_idx(env_ids)
@@ -137,6 +140,7 @@ class LeggedRobot(BaseTask):
self.compute_observations() # in some cases a simulation step might be required to refresh some obs (for example body positions)
self.last_last_actions[:] = self.last_actions[:]
self.last_actions[:] = self.actions[:]
self.last_dof_vel[:] = self.dof_vel[:]
self.last_root_vel[:] = self.root_states[:, 7:13]
@@ -180,7 +184,7 @@ class LeggedRobot(BaseTask):
def reset_idx(self, env_ids):
""" Reset some environments.
Calls self._reset_dofs(env_ids), self._reset_root_states(env_ids), and self._resample_commands(env_ids)
[Optional] calls self._update_terrain_curriculum(env_ids), self.update_command_curriculum(env_ids) and
[Optional] calls self._update_terrain_curriculum(env_ids),
Logs episode info
Resets some buffers
@@ -216,14 +220,13 @@ class LeggedRobot(BaseTask):
# reset buffers
self.actions[env_ids] = 0.
self.last_actions[env_ids] = 0.
self.last_last_actions[env_ids] = 0.
self.last_dof_vel[env_ids] = 0.
self.feet_air_time[env_ids] = 0.
self.episode_length_buf[env_ids] = 0
self.reset_buf[env_ids] = 1
self.commands_resampling_step[env_ids] = self.cfg.commands.resampling_time / self.dt
self.commands_xy_accumulation[env_ids] = 0.0
if self.cfg.commands.curriculum:
self.update_command_curriculum(env_ids)
self._resample_commands(env_ids)
# fill extras
self.extras["episode"] = {}
@@ -238,8 +241,7 @@ class LeggedRobot(BaseTask):
for key in self.episode_sums.keys():
self.extras["episode"]['rew_' + key] = torch.mean(self.episode_sums[key][env_ids]) / self.max_episode_length_s
self.episode_sums[key][env_ids] = 0.
if self.cfg.commands.curriculum:
self.extras["episode"]["max_command_x"] = self.command_ranges["lin_vel_x"][1]
self.extras["episode"]["max_command_x"] = self.command_ranges["lin_vel_x"][1]
# send timeout info to the algorithm
if self.cfg.env.send_timeouts:
self.extras["time_outs"] = self.time_out_buf
@@ -403,20 +405,8 @@ class LeggedRobot(BaseTask):
def _post_physics_step_callback(self):
""" Callback called before computing terminations, rewards, and observations
Default behaviour: Compute ang vel command based on target and heading, compute measured terrain heights and randomly push robots
Default behaviour: Compute measured terrain heights and randomly push robots
"""
# env_ids = (self.episode_length_buf % int(self.cfg.commands.resampling_time / self.dt)==0).nonzero(as_tuple=False).flatten()
resampling_env_ids = ((self.commands_resampling_step <= 0.0) * (self.episode_length_buf < self.max_episode_length - 1)).nonzero(as_tuple=False).flatten()
self._resample_commands(resampling_env_ids)
if self.cfg.commands.heading_command:
mask = (self.stop_heading == 0.0)
forward = quat_apply(self.base_quat[mask], self.forward_vec[mask])
heading = torch.atan2(forward[:, 1], forward[:, 0])
self.commands[mask, 2] = torch.clip(
0.5*wrap_to_pi(self.commands[mask, 3] - heading),
self.env_command_ranges["ang_vel_yaw"][:, 0],
self.env_command_ranges["ang_vel_yaw"][:, 1]
)
if self.cfg.terrain.measure_heights:
self.measured_heights = self._get_heights()
@@ -591,6 +581,17 @@ class LeggedRobot(BaseTask):
self.commands_xy_accumulation[env_ids] += self.commands[env_ids, :2]
if self.cfg.commands.heading_command:
heading_env_ids = env_ids[self.stop_heading[env_ids] == 0.0]
if len(heading_env_ids) > 0:
forward = quat_apply(self.base_quat[heading_env_ids], self.forward_vec[heading_env_ids])
heading = torch.atan2(forward[:, 1], forward[:, 0])
self.commands[heading_env_ids, 2] = torch.clip(
0.5 * wrap_to_pi(self.commands[heading_env_ids, 3] - heading),
self.env_command_ranges["ang_vel_yaw"][heading_env_ids, 0],
self.env_command_ranges["ang_vel_yaw"][heading_env_ids, 1]
)
def _compute_torques(self, actions):
""" Compute torques from actions.
Actions can be interpreted as position or velocity targets given to a PD controller, or directly as scaled torques.
@@ -722,21 +723,7 @@ class LeggedRobot(BaseTask):
self.gym.set_actor_root_state_tensor_indexed(self.sim,
gymtorch.unwrap_tensor(self.root_states),
gymtorch.unwrap_tensor(env_ids_int32), len(env_ids_int32))
def update_command_curriculum(self, env_ids):
""" Implements a curriculum of increasing commands
Args:
env_ids (List[int]): ids of environments being reset
"""
# If the tracking reward is above 80% of the maximum, increase the range of commands
if torch.mean(self.episode_sums["tracking_lin_vel"][env_ids]) / self.max_episode_length > 0.8 * self.reward_scales["tracking_lin_vel"]:
self.command_ranges["lin_vel_x"][0] = np.clip(self.command_ranges["lin_vel_x"][0] - 0.5, -self.cfg.commands.max_curriculum, 0.)
self.command_ranges["lin_vel_x"][1] = np.clip(self.command_ranges["lin_vel_x"][1] + 0.5, 0., self.cfg.commands.max_curriculum)
def _get_noise_scale_vec(self, cfg):
""" Sets a vector used to scale the noise added to the observations.
[NOTE]: Must be adapted when changing the observations structure
@@ -807,6 +794,7 @@ class LeggedRobot(BaseTask):
self.d_gains = torch.zeros(self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.last_actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.last_last_actions = torch.zeros(self.num_envs, self.num_actions, dtype=torch.float, device=self.device, requires_grad=False)
self.last_dof_vel = torch.zeros_like(self.dof_vel)
self.last_root_vel = torch.zeros_like(self.root_states[:, 7:13])
self.commands = torch.zeros(self.num_envs, self.cfg.commands.num_commands, dtype=torch.float, device=self.device, requires_grad=False) # x vel, y vel, yaw vel, heading
@@ -1372,10 +1360,7 @@ class LeggedRobot(BaseTask):
def _reward_action_smoothness(self):
# a_t - 2a_{t-1} + a_{t-2}
if not hasattr(self, 'last_last_actions'):
self.last_last_actions = torch.zeros_like(self.last_actions)
rew = torch.sum((self.actions - 2 * self.last_actions + self.last_last_actions).pow(2), dim=1)
self.last_last_actions[:] = self.last_actions[:]
return rew
def _reward_dof_power(self):

2
legged_gym/envs/base/legged_robot_config.py
View File

@@ -41,8 +41,6 @@ class LeggedRobotCfg(BaseConfig):
move_down_by_accumulated_xy_command = False # move down the terrain curriculum based on accumulated xy command distance instead of absolute distance
class commands:
curriculum = False
max_curriculum = 1.
num_commands = 4 # default: lin_vel_x, lin_vel_y, ang_vel_yaw, heading (in heading mode ang_vel_yaw is recomputed from heading error)
resampling_time = 10. # time before command are changed[s]
heading_command = False # if true: compute ang vel command from heading error