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fix link and generate

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This commit is contained in:
youyuan.chen
2026-07-31 13:45:20 +08:00
parent 0d2afbf2bb
commit f40874bc90
3 changed files with 41 additions and 15 deletions
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12
source/robot_lab/robot_lab/tasks/go1/env_cfg.py
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@@ -27,7 +27,7 @@ JOINT_NAMES = [
"RL_hip_joint", "RL_thigh_joint", "RL_calf_joint",
]
BASE_LINK_NAME = "trunk"
BASE_LINK_NAME = "base"
FOOT_LINK_NAME = ".*_foot"
BASE_HEIGHT_TARGET = 0.30 # Go1 nominal trunk height used by the original Gym task.
@@ -63,7 +63,7 @@ class Go1SceneCfg(InteractiveSceneCfg):
robot: ArticulationCfg = GO1_CFG_UNITREE.replace(prim_path="{ENV_REGEX_NS}/Robot")
height_scanner = RayCasterCfg(
prim_path="{ENV_REGEX_NS}/Robot/trunk",
prim_path="{ENV_REGEX_NS}/Robot/base",
offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)),
ray_alignment="yaw",
pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[1.6, 1.0]),
@@ -71,7 +71,7 @@ class Go1SceneCfg(InteractiveSceneCfg):
mesh_prim_paths=["/World/ground"],
)
height_scanner_small = RayCasterCfg(
prim_path="{ENV_REGEX_NS}/Robot/trunk",
prim_path="{ENV_REGEX_NS}/Robot/base",
offset=RayCasterCfg.OffsetCfg(pos=(0.0, 0.0, 20.0)),
ray_alignment="yaw",
pattern_cfg=patterns.GridPatternCfg(resolution=0.1, size=[0.4, 0.3]),
@@ -260,7 +260,7 @@ class EventCfg:
func=mdp.randomize_rigid_body_mass,
mode="startup",
params={
"asset_cfg": SceneEntityCfg("robot", body_names="^(?!.*trunk).*"),
"asset_cfg": SceneEntityCfg("robot", body_names="^(?!.*base).*"),
"mass_distribution_params": (0.9, 1.1),
"operation": "scale",
"recompute_inertia": True,
@@ -466,7 +466,7 @@ class Go1EnvCfg(ManagerBasedRLEnvCfg):
"""Merged configuration for the Go1 robot on rough terrain."""
# Scene settings
scene: Go1SceneCfg = Go1SceneCfg(num_envs=16384, env_spacing=0.5)
scene: Go1SceneCfg = Go1SceneCfg(num_envs=4096, env_spacing=0.5)
# Basic settings
observations: ObservationsCfg = ObservationsCfg()
actions: ActionsCfg = ActionsCfg()
@@ -489,7 +489,7 @@ class Go1EnvCfg(ManagerBasedRLEnvCfg):
# Physics material settings from subclass
self.sim.physics_material = self.scene.terrain.physics_material
self.sim.physx.gpu_max_rigid_patch_count = int(1 * 1024 * 1024) # 1 million
self.sim.physx.gpu_collision_stack_size = int(512 * 1024 * 1024) # 128 MB
self.sim.physx.gpu_collision_stack_size = int(128 * 1024 * 1024) # 128 MB
self.sim.physx.enable_external_forces_every_iteration = True
# Update sensor periods

16
source/rsl_rl/rsl_rl/algorithms/moe_cts.py
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@@ -233,9 +233,12 @@ class MoECTS:
# RND loss
mean_rnd_loss = 0 if self.rnd else None
# Get mini batch generator
generator = self.storage.mini_batch_generator(self.num_mini_batches, self.num_learning_epochs)
data = list(generator)
# Reuse the exact same shuffled samples for both update phases without
# materializing every epoch and mini-batch on the GPU at once.
batch_indices = self.storage.generate_mini_batch_indices()
generator = self.storage.mini_batch_generator(
self.num_mini_batches, self.num_learning_epochs, batch_indices=batch_indices
)
# Iterate over batches
teacher_samples = self.teacher_num_envs * self.storage.num_transitions_per_env // self.num_mini_batches
@@ -251,7 +254,7 @@ class MoECTS:
old_sigma_batch,
hidden_states_batch,
masks_batch,
) in data:
) in generator:
original_batch_size = obs_batch.batch_size[0]
# Check if we should normalize advantages per mini batch
@@ -377,6 +380,9 @@ class MoECTS:
if mean_rnd_loss is not None:
mean_rnd_loss += rnd_loss.item()
generator = self.storage.mini_batch_generator(
self.num_mini_batches, self.num_learning_epochs, batch_indices=batch_indices
)
for (
obs_batch,
actions_batch,
@@ -388,7 +394,7 @@ class MoECTS:
old_sigma_batch,
hidden_states_batch,
masks_batch,
) in data:
) in generator:
# Student encoder loss
obs_a_batch = self.policy.get_actor_obs(obs_batch)
obs_a_batch = self.policy.actor_obs_normalizer(obs_a_batch)

26
source/rsl_rl/rsl_rl/storage/rollout_storage_cts.py
View File

@@ -127,7 +127,25 @@ class RolloutStorageCTS:
yield self.observations[i], self.actions[i], self.privileged_actions[i], self.dones[i]
# For reinforcement learning with feedforward networks
def mini_batch_generator(self, num_mini_batches: int, num_epochs: int = 8) -> Generator:
def generate_mini_batch_indices(self) -> tuple[torch.Tensor, torch.Tensor]:
"""Generate the teacher and student permutations shared by both update phases."""
if self.training_type != "rl":
raise ValueError("This function is only available for reinforcement learning training.")
teacher_samples_num = self.teacher_num_envs * self.num_transitions_per_env
student_samples_num = self.student_num_envs * self.num_transitions_per_env
teacher_indices = torch.randperm(teacher_samples_num, requires_grad=False, device=self.device)
student_indices = teacher_samples_num + torch.randperm(
student_samples_num, requires_grad=False, device=self.device
)
return teacher_indices, student_indices
def mini_batch_generator(
self,
num_mini_batches: int,
num_epochs: int = 8,
batch_indices: tuple[torch.Tensor, torch.Tensor] | None = None,
) -> Generator:
if self.training_type != "rl":
raise ValueError("This function is only available for reinforcement learning training.")
@@ -136,8 +154,10 @@ class RolloutStorageCTS:
student_samples_num = self.student_num_envs * self.num_transitions_per_env
teacher_mini_batch_size = teacher_samples_num // num_mini_batches
student_mini_batch_size = student_samples_num // num_mini_batches
teacher_indices = torch.randperm(teacher_samples_num, requires_grad=False, device=self.device)
student_indices = teacher_samples_num + torch.randperm(student_samples_num, requires_grad=False, device=self.device)
if batch_indices is None:
teacher_indices, student_indices = self.generate_mini_batch_indices()
else:
teacher_indices, student_indices = batch_indices
# Core
observations = self.observations.transpose(0, 1).flatten(0, 1)