update better default training settings; update new policy and results; update readme.

README.md

@@ -73,12 +73,12 @@ It is an official [MoE-CTS](https://robogauge.github.io/static/files/arxiv.pdf)
   <img src="resources/results/robogauge_compare.png" width="100%"/>
 </p>
 
-### Algorithm Results (Best of 150k training steps)
+### Algorithm Results (Best Checkpoint Results)
 
 | Model | Score | Tracking  | Safety  | Quality  | Level |
 | --- | --- | --- | --- | --- | --- |
-| go2_moe_cts (go2_rl_robotlab) | **0.6828** | **0.6785** | 0.7552 | **0.7645** | **8.17** |
-| go2_moe_cts (go2_rl_gym) | **0.6713** | 0.6669 | **0.7857** | 0.7392 | 7.85 |
+| go2_moe_cts (go2_rl_robotlab) | **0.6984** | **0.7055** | **0.8159** | **0.7693** | **8.30** |
+| go2_moe_cts (go2_rl_gym) | 0.6713 | 0.6669 | 0.7857 | 0.7392 | 7.85 |
 | [CTS](https://arxiv.org/pdf/2405.10830) vanilla | 0.5786 | 0.5755 | 0.7066 | 0.6624 | 6.83 |
 | [HIM](https://github.com/InternRobotics/HIMLoco) | 0.5379 | 0.5453 | 0.6476 | 0.6050 | 6.19 |
 | [DreamWaQ](https://arxiv.org/abs/2301.10602) | 0.5054 | 0.5105 | 0.6149 | 0.5730 | 5.74 |
@@ -251,16 +251,17 @@ xml_path: "{ROOT_DIR}/resources/go2/your-custom-scene.xml"
 ## Differences from `go2_rl_gym`
 
 - Motor:
-  - use official unitree motor model instead of simple PD controller
+  - use official unitree motor model instead of simple PD controller.
 - Rewards:
-  - different tracking reward form (fixed sigma vs. dynamic sigma)
-  - lower joint_acc_l2 weight in Lab due to physics-step level implementation and sensitivity to outliers
-  - extra joint_pos_penalty_l1 reward in Lab due to better performance
+  - 2x tracking reward weights due to better performance.
+  - different tracking reward form (fixed sigma vs. dynamic sigma).
+  - lower joint_acc_l2 weight in Lab due to physics-step level implementation and sensitivity to outliers.
+  - extra joint_pos_penalty_l1 reward in Lab due to better performance.
 - Domain randomization: 
-  - no randomized action delay, use motor-level delay instead
+  - no randomized action delay, use motor-level delay instead.
   - no motor strength randomization due to implementation constraints in Lab.
 - History length: 10 in Lab vs. 5 in Gym, due to better performance with longer history in Lab.
-
+- Terrain Difficulty: use lab's continuous difficulty levels.
 ---
 
 ## Acknowledgements

deploy/deploy_mujoco/configs/go2.yaml

@@ -1,4 +1,4 @@
-policy_path: "{ROOT_DIR}/deploy/pre_train/go2/go2_moe_cts_185k_0.6828.pt" # policy.pt exported by running scripts/rsl_rl/play.py
+policy_path: "{ROOT_DIR}/deploy/pre_train/go2/go2_moe_cts_176k_0.6984.pt" # policy.pt exported by running scripts/rsl_rl/play.py
 
 xml_path: "{ROOT_DIR}/resources/go2/stairs_and_slope.xml"
 

source/robot_lab/robot_lab/tasks/go2/env_cfg.py

@@ -350,12 +350,12 @@ class RewardsCfg:
 
     track_lin_vel_xy_exp = RewTerm(
         func=mdp.track_lin_vel_xy_exp, 
-        weight=1.0, 
+        weight=2.0, 
         params={"command_name": "base_velocity", "std": 0.5}
     )
     track_ang_vel_z_exp = RewTerm(
         func=mdp.track_ang_vel_z_exp, 
-        weight=0.5, 
+        weight=1.0, 
         params={"command_name": "base_velocity", "std": 0.5}
     )
     lin_vel_z_l2 = RewTerm(func=mdp.lin_vel_z_l2, weight=-2.0)
@@ -466,7 +466,7 @@ class Go2EnvCfg(ManagerBasedRLEnvCfg):
     """Merged configuration for the Go2 robot on rough terrain."""
 
     # Scene settings
-    scene: Go2SceneCfg = Go2SceneCfg(num_envs=8192, env_spacing=0.5)
+    scene: Go2SceneCfg = Go2SceneCfg(num_envs=16384, env_spacing=0.5)
     # Basic settings
     observations: ObservationsCfg = ObservationsCfg()
     actions: ActionsCfg = ActionsCfg()

source/robot_lab/robot_lab/tasks/go2/mdp/terrains.py

@@ -178,7 +178,7 @@ TERRAIN_CFG = Go2TerrainGeneratorCfg(
     # slope correction = 0.75 ~ 36.9 degrees by default,
     # but recommended to set for each terrain type separately using with_slope_threshold
     slope_threshold=0.75,
-    use_gym_difficulty=True,
+    use_gym_difficulty=False,
     use_cache=False,
     sub_terrains={
         "wave": with_slope_threshold(