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v0.1.6; add move_down_by_acuumulated_xy_command, dynamic_resample_commands optional

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wty-yy
2026-01-07 23:17:43 +08:00
parent 41295f7e72
commit 6f477bf42d
5 changed files with 367 additions and 35 deletions
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83
legged_gym/envs/base/legged_robot.py
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@@ -221,7 +221,7 @@ class LeggedRobot(BaseTask):
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_accummulation[env_ids] = 0.0
self.commands_xy_accumulation[env_ids] = 0.0
if self.cfg.commands.curriculum:
self.update_command_curriculum(env_ids)
self._resample_commands(env_ids)
@@ -442,36 +442,47 @@ class LeggedRobot(BaseTask):
self.cfg.commands.command_range_curriculum.pop(i)
self._update_env_command_ranges()
print(f"Command range updated at iter {current_iter}: {self.command_ranges}")
# 到达边界0.625倍宽度的剩余距离
remaining_dist = torch.clip(0.625 * self.cfg.terrain.terrain_length - torch.norm(self.commands_xy_accummulation[env_ids], dim=1) * self.cfg.commands.resampling_time, 0.0)
if ((self.max_episode_length - self.episode_length_buf[env_ids]) == 0).any():
raise ValueError("Some envs have zero remaining episode length during command resampling")
vel_low_bound = torch.clip(remaining_dist / ((self.max_episode_length - self.episode_length_buf[env_ids] + 1e-9) * self.dt), 0.0)
self.commands[env_ids, 0] = sample_disjoint_intervals(
env_ids,
vel_low_bound,
self.env_command_ranges["lin_vel_x"][env_ids, 0],
self.env_command_ranges["lin_vel_x"][env_ids, 1],
self.device
)
self.commands[env_ids, 1] = sample_disjoint_intervals(
env_ids,
vel_low_bound,
self.env_command_ranges["lin_vel_y"][env_ids, 0],
self.env_command_ranges["lin_vel_y"][env_ids, 1],
self.device
)
if self.cfg.commands.heading_command:
r = torch.rand(len(env_ids), device=self.device)
lower = self.env_command_ranges["heading"][env_ids, 0]
upper = self.env_command_ranges["heading"][env_ids, 1]
self.commands[env_ids, 3] = (upper - lower) * r + lower
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)
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():
raise ValueError("Some envs have zero remaining episode length during command resampling")
vel_low_bound = torch.clip(remaining_dist / ((self.max_episode_length - self.episode_length_buf[env_ids] + 1e-9) * self.dt), 0.0)
self.commands[env_ids, 0] = sample_disjoint_intervals(
env_ids,
vel_low_bound,
self.env_command_ranges["lin_vel_x"][env_ids, 0],
self.env_command_ranges["lin_vel_x"][env_ids, 1],
self.device
)
self.commands[env_ids, 1] = sample_disjoint_intervals(
env_ids,
vel_low_bound,
self.env_command_ranges["lin_vel_y"][env_ids, 0],
self.env_command_ranges["lin_vel_y"][env_ids, 1],
self.device
)
if self.cfg.commands.heading_command:
r = torch.rand(len(env_ids), device=self.device)
lower = self.env_command_ranges["heading"][env_ids, 0]
upper = self.env_command_ranges["heading"][env_ids, 1]
self.commands[env_ids, 3] = (upper - lower) * r + lower
else:
r = torch.rand(len(env_ids), device=self.device)
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:
r = torch.rand(len(env_ids), device=self.device)
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
self.commands[env_ids, 0] = torch_rand_float(self.command_ranges["lin_vel_x"][0], self.command_ranges["lin_vel_x"][1], (len(env_ids), 1), device=self.device).squeeze(1)
self.commands[env_ids, 1] = torch_rand_float(self.command_ranges["lin_vel_y"][0], self.command_ranges["lin_vel_y"][1], (len(env_ids), 1), device=self.device).squeeze(1)
if self.cfg.commands.heading_command:
self.commands[env_ids, 3] = torch_rand_float(self.command_ranges["heading"][0], self.command_ranges["heading"][1], (len(env_ids), 1), device=self.device).squeeze(1)
else:
self.commands[env_ids, 2] = torch_rand_float(self.command_ranges["ang_vel_yaw"][0], self.command_ranges["ang_vel_yaw"][1], (len(env_ids), 1), device=self.device).squeeze(1)
# set small commands to zero
self.commands[env_ids, :2] *= (torch.norm(self.commands[env_ids, :2], dim=1) > 0.2).unsqueeze(1)
# set small commands to zero
# self.commands[env_ids, :2] *= (torch.norm(self.commands[env_ids, :2], dim=1) > 0.2).unsqueeze(1)
@@ -559,7 +570,7 @@ class LeggedRobot(BaseTask):
self.commands[zero_env_ids, :3] = 0.0
self.stop_heading[zero_env_ids] = True
self.commands_xy_accummulation[env_ids] += self.commands[env_ids, :2]
self.commands_xy_accumulation[env_ids] += self.commands[env_ids, :2]
def _compute_torques(self, actions):
""" Compute torques from actions.
@@ -782,7 +793,7 @@ class LeggedRobot(BaseTask):
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
self.commands_scale = torch.tensor([self.obs_scales.lin_vel, self.obs_scales.lin_vel, self.obs_scales.ang_vel], device=self.device, requires_grad=False,) # TODO change this
self.commands_resampling_step = torch.zeros(self.num_envs, dtype=torch.float, device=self.device, requires_grad=False)
self.commands_xy_accummulation = torch.zeros(self.num_envs, 2, dtype=torch.float, device=self.device, requires_grad=False)
self.commands_xy_accumulation = torch.zeros(self.num_envs, 2, dtype=torch.float, device=self.device, requires_grad=False)
self.zero_command_proba = 0.0
self.feet_air_time = torch.zeros(self.num_envs, self.feet_indices.shape[0], dtype=torch.float, device=self.device, requires_grad=False)
self.last_contacts = torch.zeros(self.num_envs, len(self.feet_indices), dtype=torch.bool, device=self.device, requires_grad=False)
@@ -1118,9 +1129,11 @@ class LeggedRobot(BaseTask):
distance = self.max_move_distance[env_ids]
# robots that walked far enough progress to harder terains
move_up = distance > self.terrain.env_length / 2
# robots that walked less than half of their required distance go to simpler terrains
# move_down = (distance < torch.norm(self.commands[env_ids, :2], dim=1) * self.max_episode_length_s * 0.5) * ~move_up
move_down = (distance < torch.norm(self.commands_xy_accummulation[env_ids], dim=1) * (self.cfg.commands.resampling_time * (1 - self.zero_command_proba)) * 0.5) * ~move_up
if self.cfg.terrain.move_down_by_acuumulated_xy_command:
move_down = (distance < torch.norm(self.commands_xy_accumulation[env_ids], dim=1) * (self.cfg.commands.resampling_time * (1 - self.zero_command_proba)) * 0.5) * ~move_up
else:
# robots that walked less than half of their required distance go to simpler terrains
move_down = (distance < torch.norm(self.commands[env_ids, :2], dim=1) * self.max_episode_length_s * 0.5) * ~move_up
self.terrain_levels[env_ids] += 1 * move_up - 1 * move_down
# Robots that solve the last level are sent to a random one

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

@@ -38,6 +38,7 @@ class LeggedRobotCfg(BaseConfig):
terrain_proportions = [0.1, 0.1, 0.1, 0.2, 0.2, 0.1, 0.1, 0.1, 0.0]
# trimesh only:
slope_treshold = 0.75 # slopes above this threshold will be corrected to vertical surfaces
move_down_by_acuumulated_xy_command = False # move down the terrain curriculum based on accumulated xy command distance instead of absolute distance
class commands:
curriculum = False
@@ -53,6 +54,7 @@ class LeggedRobotCfg(BaseConfig):
limit_vel_invert_when_continuous = True # invert the limit logic when using continuous sample limit velocity commands
limit_vel = {"lin_vel_x": [-1, 1], "lin_vel_y": [-1, 1], "ang_vel_yaw": [-1, 0, 1]} # sample vel commands from min [-1] or zero [0] or max [1] range only
stop_heading_at_limit = True # stop heading updates when vel is limited
dynamic_resample_commands = False # sample commands with low bounds
command_range_curriculum = [] # list for command range curriculums at specific training iterations
# eg: [{
# 'iter': 20000, # training iteration at which the command ranges are updated