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aligned to go2_rl_gym.

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This commit is contained in:
wertyuilife
2026-03-25 14:40:03 +08:00
parent a1523d6949
commit d946c689da
5 changed files with 127 additions and 33 deletions
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7
README.md
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@@ -60,15 +60,8 @@ python scripts/reinforcement_learning/rsl_rl/play.py --task=RobotLab-Go2-v0
## Differences with `go2_rl_gym`
- Terrain's composition are different(see code).
- Rewards
- feet_regulation are lacked.
- dof_pos_limits are lacked.
- tracking reward are different (fixed sigma vs. dynamic sigma).
- Terminations:
- contact termination are lacked.
## Acknowledgements
This repository would not exist without the following open-source projects:

2
source/robot_lab/robot_lab/assets/unitree.py
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@@ -19,7 +19,7 @@ UNITREE_GO2_CFG = ArticulationCfg(
spawn=sim_utils.UrdfFileCfg(
fix_base=False,
merge_fixed_joints=True,
replace_cylinders_with_capsules=False,
replace_cylinders_with_capsules=True,
asset_path=f"{ISAACLAB_ASSETS_DATA_DIR}/Robots/unitree/go2_description/urdf/go2_description.urdf",
activate_contact_sensors=True,
rigid_props=sim_utils.RigidBodyPropertiesCfg(

71
source/robot_lab/robot_lab/tasks/go2/env_cfg.py
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@@ -62,10 +62,10 @@ TERRAIN_CFG = terrain_gen.TerrainGeneratorCfg(
holes=False,
),
"boxes": terrain_gen.MeshRandomGridTerrainCfg(
proportion=0.15, grid_width=0.45, grid_height_range=(0.01, 0.15), platform_width=2.0
proportion=0.15, grid_width=0.45, grid_height_range=(0.05, 0.2), platform_width=2.0
),
"random_rough": terrain_gen.HfRandomUniformTerrainCfg(
proportion=0.15, noise_range=(0.01, 0.1), noise_step=0.01, border_width=0.25
proportion=0.15, noise_range=(0.02, 0.1), noise_step=0.02, border_width=0.25
),
"flat": terrain_gen.MeshPlaneTerrainCfg(proportion=0.15),
"hf_pyramid_slope": terrain_gen.HfPyramidSlopedTerrainCfg(
@@ -143,7 +143,7 @@ class CommandsCfg:
resampling_time_range=(5.0, 5.0),
rel_standing_envs=0.1,
rel_heading_envs=1.0,
heading_command=False, # note here, no heading command!
heading_command=False,
heading_control_stiffness=0.5,
debug_vis=True,
cycle_time=0.5,
@@ -405,7 +405,30 @@ class EventCfg:
"make_consistent": True
},
)
reset_base = EventTerm(
func=mdp.reset_root_state_uniform,
mode="reset",
params={
"pose_range": {"x": (-0.5, 0.5), "y": (-0.5, 0.5), "z": (0.0, 0.2), "yaw": (-3.14, 3.14)},
"velocity_range": {
"x": (-0.5, 0.5),
"y": (-0.5, 0.5),
"z": (-0.5, 0.5),
"roll": (-0.5, 0.5),
"pitch": (-0.5, 0.5),
"yaw": (-0.5, 0.5),
},
},
)
randomize_apply_external_force_torque = EventTerm(
func=mdp.apply_external_force_torque,
mode="reset",
params={
"asset_cfg": SceneEntityCfg("robot", body_names=BASE_LINK_NAME),
"force_range": (-5.0, 5.0),
"torque_range": (-5.0, 5.0),
},
)
@configclass
class RewardsCfg:
@@ -416,47 +439,38 @@ class RewardsCfg:
weight=1.0,
params={"command_name": "base_velocity", "std": 0.5}
)
track_ang_vel_z_exp = RewTerm(
func=mdp.track_ang_vel_z_exp,
weight=0.5,
params={"command_name": "base_velocity", "std": 0.5}
)
lin_vel_z_l2 = RewTerm(func=mdp.lin_vel_z_l2, weight=-2.0)
ang_vel_xy_l2 = RewTerm(func=mdp.ang_vel_xy_l2, weight=-0.05)
flat_orientation_l2 = RewTerm(
func=mdp.flat_orientation_l2,
weight=-3.0
)
base_height_l2 = RewTerm(
func=mdp.base_height_l2,
weight=-1.0,
params={
"asset_cfg": SceneEntityCfg("robot", body_names=BASE_LINK_NAME),
"target_height": 0.38
"target_height": 0.38,
"sensor_cfg": SceneEntityCfg("height_scanner"),
}
)
dof_acc_l2 = RewTerm(
func=mdp.joint_acc_l2,
weight=-2.0e-7,
params={"asset_cfg": SceneEntityCfg("robot", joint_names=JOINT_NAMES)}
)
action_rate_l2 = RewTerm(func=mdp.action_rate_l2, weight=-0.01)
action_smoothness_l2 = RewTerm(func=mdp.action_smoothness_l2, weight=-0.01)
undesired_contacts = RewTerm(
func=mdp.undesired_contacts,
weight=-1.0,
params={"sensor_cfg": SceneEntityCfg("contact_forces", body_names=".*_thigh|.*_calf"), "threshold": 5.0},
)
hip_pos_penalty = RewTerm(
func=mdp.hip_pos_penalty,
weight=-0.05,
@@ -467,23 +481,42 @@ class RewardsCfg:
"command_threshold": 0.1,
},
)
joint_torques_l2 = RewTerm(
func=mdp.joint_torques_l2,
weight=-1e-4,
params={"asset_cfg": SceneEntityCfg("robot", joint_names=JOINT_NAMES)}
)
joint_power = RewTerm(
func=mdp.joint_power,
weight=-2e-5,
params={"asset_cfg": SceneEntityCfg("robot", joint_names=JOINT_NAMES)}
)
feet_regulation = RewTerm(
func=mdp.feet_regulation,
weight=-0.05,
params={
"base_height_target": 0.38,
"asset_cfg": SceneEntityCfg("robot", body_names=FOOT_LINK_NAME),
"sensor_cfg": SceneEntityCfg("height_scanner"),
},
)
dof_pos_limits = RewTerm(
func=mdp.dof_pos_limits,
weight=-2.0,
params={"asset_cfg": SceneEntityCfg("robot")},
)
@configclass
class TerminationsCfg:
"""Termination terms for the MDP."""
time_out = DoneTerm(func=mdp.time_out, time_out=True)
illegal_contact = DoneTerm(
func=mdp.illegal_contact,
params={
"sensor_cfg": SceneEntityCfg("contact_forces", body_names=BASE_LINK_NAME),
"threshold": 1.0
},
)
@configclass
class CurriculumCfg:
@@ -492,7 +525,7 @@ class CurriculumCfg:
base_linear_velocity = CurrTerm(mdp.gradual_reward_weight_modification, params={
"term_name": "lin_vel_z_l2", "initial_weight": -2.0, "final_weight": -0.0, "start_it": 0, "end_it": 1500
})
base_linear_velocity = CurrTerm(mdp.gradual_reward_weight_modification, params={
base_height_l2 = CurrTerm(mdp.gradual_reward_weight_modification, params={
"term_name": "base_height_l2", "initial_weight": -1.0, "final_weight": -10.0, "start_it": 0, "end_it": 5000
})
ref_stand_envs = CurrTerm(mdp.gradual_ref_stand_modification, params={
@@ -535,7 +568,7 @@ class Go2EnvCfg(ManagerBasedRLEnvCfg):
# Physics material settings from subclass
self.sim.physics_material = self.scene.terrain.physics_material
self.sim.physx.gpu_max_rigid_patch_count = 10 * 2**15
self.sim.physx.gpu_collision_stack_size = int(128 * 1024 * 1024) # 128 MB
self.sim.physx.gpu_collision_stack_size = int(64 * 1024 * 1024) # 128 MB
self.sim.physx.enable_external_forces_every_iteration = True
# Update sensor periods

68
source/robot_lab/robot_lab/tasks/go2/mdp/rewards.py
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@@ -555,3 +555,71 @@ def hip_pos_penalty(
stand_still_scale * running_reward
)
return reward
def feet_regulation(
env: ManagerBasedRLEnv,
base_height_target: float,
asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
sensor_cfg: SceneEntityCfg | None = None,
) -> torch.Tensor:
asset: RigidObject = env.scene[asset_cfg.name]
feet_ids = asset_cfg.body_ids
feet_pos_w = asset.data.body_link_pos_w[:, feet_ids, :]
base_pos_w = asset.data.root_link_pos_w.unsqueeze(1)
feet_xy_vel_w = asset.data.body_lin_vel_w[:, feet_ids, :2]
base_z = asset.data.root_link_pos_w[:, 2]
if sensor_cfg is not None:
sensor: RayCaster = env.scene[sensor_cfg.name]
ray_hits_z = sensor.data.ray_hits_w[..., 2]
invalid = (
torch.isnan(ray_hits_z).any(dim=1)
| torch.isinf(ray_hits_z).any(dim=1)
| (torch.max(torch.abs(ray_hits_z), dim=1).values > 1e6)
)
estimated_ground_z = torch.mean(ray_hits_z, dim=1)
fallback_ground_z = base_z - base_height_target
estimated_ground_z = torch.where(invalid, fallback_ground_z, estimated_ground_z)
else:
estimated_ground_z = base_z - base_height_target
base_height = base_z - estimated_ground_z
gravity_w = torch.tensor(env.sim.cfg.gravity, device=env.device, dtype=feet_pos_w.dtype)
up_w = -gravity_w / torch.norm(gravity_w)
delta_feet_w = feet_pos_w - base_pos_w
feet2base_height = torch.sum(delta_feet_w * up_w.view(1, 1, 3), dim=-1)
feet_height = torch.clamp(base_height.unsqueeze(1) - feet2base_height, min=0.0)
reward = (
feet_xy_vel_w.pow(2).sum(dim=-1)
* torch.exp(-feet_height / (0.025 * base_height_target))
).sum(dim=-1)
reward *= torch.clamp(-env.scene["robot"].data.projected_gravity_b[:, 2], 0, 0.7) / 0.7
return reward
def dof_pos_limits(
env: ManagerBasedRLEnv,
asset_cfg: SceneEntityCfg = SceneEntityCfg("robot"),
) -> torch.Tensor:
asset = env.scene[asset_cfg.name]
joint_pos = asset.data.joint_pos
joint_limits = asset.data.soft_joint_pos_limits
lower = joint_limits[..., 0]
upper = joint_limits[..., 1]
out_of_limits = -(joint_pos - lower).clamp(max=0.0)
out_of_limits += (joint_pos - upper).clamp(min=0.0)
return torch.sum(out_of_limits, dim=1)

4
source/robot_lab/robot_lab/tasks/go2/rsl_rl_cfg.py
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@@ -39,8 +39,8 @@ class RslRlMoeCtsActorCriticCfg(RslRlPpoActorCriticCfg):
expert_num = 8 # number of experts in the student model
latent_dim = 32
norm_type = 'l2norm' # normalization type for encoders: l2norm, simnorm
teacher_encoder_hidden_dims = [512, 256, 128]
student_encoder_hidden_dims = [512, 256, 128]
teacher_encoder_hidden_dims = [512, 256]
student_encoder_hidden_dims = [512, 256, 256]
actor_hidden_dims=[512, 256, 128]
critic_hidden_dims=[512, 256, 128]
activation="elu"