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v0.1.8; add configs, fix bugs

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wty-yy
2026-01-10 02:50:58 +08:00
parent 836a4e544f
commit bcb3757403
7 changed files with 711 additions and 6 deletions
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11
legged_gym/envs/base/legged_robot.py
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@@ -426,6 +426,8 @@ class LeggedRobot(BaseTask):
Args:
env_ids (List[int]): Environments ids for which new commands are needed
"""
if len(env_ids) == 0:
return
self.stop_heading[env_ids] = False
# update command curriculum with train steps
if len(self.cfg.commands.command_range_curriculum):
@@ -443,6 +445,7 @@ class LeggedRobot(BaseTask):
self._update_env_command_ranges()
print(f"Command range updated at iter {current_iter}: {self.command_ranges}")
remaining_dist = torch.clip(0.625 * self.cfg.terrain.terrain_length - torch.norm(self.commands_xy_accumulation[env_ids], dim=1) * self.cfg.commands.resampling_time, 0.0)
self.commands_resampling_step[env_ids] = self.cfg.commands.resampling_time / self.dt
if self.cfg.commands.dynamic_resample_commands:
# arrive at boundary 0.625 times the width of the remaining distance
if ((self.max_episode_length - self.episode_length_buf[env_ids]) == 0).any():
@@ -472,7 +475,6 @@ class LeggedRobot(BaseTask):
lower = self.env_command_ranges["ang_vel_yaw"][env_ids, 0]
upper = self.env_command_ranges["ang_vel_yaw"][env_ids, 1]
self.commands[env_ids, 2] = (upper - lower) * r + lower
self.commands_resampling_step[env_ids] = self.cfg.commands.resampling_time / self.dt
else:
self.commands[env_ids, 0] = sample_single_interval(
env_ids,
@@ -859,6 +861,12 @@ class LeggedRobot(BaseTask):
def _update_env_command_ranges(self):
""" Update environment-wise command ranges based on current command ranges and terrain type """
if not hasattr(self, 'terrain_ids'):
self.env_command_ranges = {
'lin_vel_x': torch.tensor(self.command_ranges['lin_vel_x'], device=self.device, requires_grad=False).repeat(self.num_envs, 1),
'lin_vel_y': torch.tensor(self.command_ranges['lin_vel_y'], device=self.device, requires_grad=False).repeat(self.num_envs, 1),
'ang_vel_yaw': torch.tensor(self.command_ranges['ang_vel_yaw'], device=self.device, requires_grad=False).repeat(self.num_envs, 1),
'heading': torch.tensor(self.command_ranges['heading'], device=self.device, requires_grad=False).repeat(self.num_envs, 1),
}
return
for terrain_id, terrain_command_ranges in enumerate(self.cfg.commands.terrain_max_command_ranges):
env_ids = (self.terrain_ids == terrain_id).nonzero(as_tuple=False).flatten()
@@ -1322,6 +1330,7 @@ class LeggedRobot(BaseTask):
sigma_y = self._get_dynamic_sigma(torch.abs(self.commands[:, 1]), vmin, vmax)
lin_vel_error_sq = torch.square(self.commands[:, :2] - self.base_lin_vel[:, :2])
scaled_error = lin_vel_error_sq[:, 0] / sigma_x + lin_vel_error_sq[:, 1] / sigma_y
# print(f"{self.base_lin_vel[:, :2]=}, {lin_vel_error_sq=}")
return torch.exp(-scaled_error)
def _reward_tracking_ang_vel(self):