refactor: retire motrix_legacy — owner-layer decomposition

AGENTS.md

@@ -26,7 +26,7 @@ UniLab 是一个 **高性能、模块化、contract 驱动** 的 RL infrastructu
 | 区域 | 不可破坏的不变量 |
 |------|----------------|
 | Env  | `NpEnvState.obs` 必须是 dict；`reset()` 返回 `(obs_dict, info_dict)`；`obs_groups_spec` 影响 wrapper 和 learner 维度。 |
-| Config / Reward | reward 通过 Hydra 注入；`training.sim_backend` 和 `motrix_legacy` 必须尊重显式 override。 |
+| Config / Reward | reward 通过 Hydra 注入；后端切换必须通过 `task=<task>/<backend>` 选择 owner YAML，`training.sim_backend` 只是 owner YAML 的身份字段，不能单独 override 来切后端。算法超参数直接走 YAML compose，不经 Python 层解释。 |
 | Backend | backend-specific 逻辑留在 backend / env 适配层，不向训练脚本扩散。 |
 | Async | 不绕开 runner lifecycle，也不另起 collector / learner 同步协议。 |
 

CONTRIBUTING.md

@@ -143,7 +143,7 @@ Use GitHub Issues to report bugs or propose features.
 
 UniLab uses Hydra + dataclass configuration:
 
-- **Add a new task**: create YAML under `conf/{algo}/task/` and use `# @package _global_`
+- **Add a new task entry**: create a single owner YAML under `conf/{algo}/task/...` and use `# @package _global_`
 - **Change hyperparameters**: edit the matching YAML or use CLI overrides such as `algo.num_envs=2048`
 - **Add a new algorithm**: add the dataclass in `structured_configs.py` and create the matching `conf/` directory
 

conf/appo/config.yaml

@@ -1,7 +1,6 @@
 defaults:
   - _self_
-  - reward: default
-  - task: go1_joystick
+  - task: go1_joystick/mujoco
 
 algo:
   algo: appo

conf/appo/task/allegro_inhand.yaml → conf/appo/task/allegro_inhand/mujoco.yaml

@@ -1,10 +1,7 @@
 # @package _global_
-defaults:
-  - override /reward: allegro_appo
-
 training:
   task_name: AllegroInhandRotation
-
+  sim_backend: mujoco
 algo:
   num_envs: 16384
   steps_per_env: 8
@@ -16,3 +13,14 @@ algo:
     obs_normalization: true
   critic:
     obs_normalization: true
+reward:
+  scales:
+    rotate: 1.25
+    obj_linvel: -0.3
+    pose_diff: -0.3
+    torque: -0.1
+    work: -2.0
+    drop: 0.0
+  angvel_clip_min: -0.5
+  angvel_clip_max: 0.5
+  reset_z_threshold: 0.125

conf/appo/reward/g1_motion_tracking_mujoco.yaml → conf/appo/task/g1_flip_tracking/motrix.yaml

@@ -1,4 +1,12 @@
 # @package _global_
+training:
+  task_name: G1FlipTracking
+  sim_backend: motrix
+  play_steps: 1000
+algo:
+  num_envs: 1024
+  max_iterations: 5000
+  save_interval: 500
 reward:
   scales:
     motion_global_root_pos: 0.5

conf/ppo/reward/g1_motion_tracking_mujoco.yaml → conf/appo/task/g1_flip_tracking/mujoco.yaml

@@ -1,4 +1,12 @@
 # @package _global_
+training:
+  task_name: G1FlipTracking
+  sim_backend: mujoco
+  play_steps: 1000
+algo:
+  num_envs: 1024
+  max_iterations: 5000
+  save_interval: 500
 reward:
   scales:
     motion_global_root_pos: 0.5

conf/ppo/reward/g1_ppo_mujoco.yaml → conf/appo/task/g1_joystick/mujoco.yaml

@@ -1,4 +1,10 @@
 # @package _global_
+training:
+  task_name: G1JoystickFlatTerrain
+  sim_backend: mujoco
+algo:
+  max_iterations: 500
+  save_interval: 100
 reward:
   scales:
     tracking_lin_vel: 2.0
@@ -18,4 +24,3 @@ reward:
   min_base_height: 0.55
   max_tilt_deg: 25.0
   pose_weights: [0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 0.01, 1.0, 5.0, 0.01, 5.0, 5.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0, 50.0]
-

conf/appo/task/g1_motion_tracking/motrix.yaml

@@ -0,0 +1,29 @@
+# @package _global_
+training:
+  task_name: G1MotionTracking
+  sim_backend: motrix
+  play_steps: 1000
+algo:
+  num_envs: 1024
+  max_iterations: 5000
+  save_interval: 500
+reward:
+  scales:
+    motion_global_root_pos: 0.5
+    motion_global_root_ori: 0.5
+    motion_body_pos: 1.0
+    motion_body_ori: 1.0
+    motion_body_lin_vel: 1.0
+    motion_body_ang_vel: 1.0
+    motion_joint_pos: 0.0
+    motion_joint_vel: 0.0
+    action_rate_l2: -0.1
+    joint_limit: -10.0
+  std_root_pos: 0.3
+  std_root_ori: 0.4
+  std_body_pos: 0.3
+  std_body_ori: 0.4
+  std_body_lin_vel: 1.0
+  std_body_ang_vel: 3.14
+  std_joint_pos: 0.2
+  std_joint_vel: 1.0

conf/appo/task/g1_motion_tracking/mujoco.yaml

@@ -0,0 +1,29 @@
+# @package _global_
+training:
+  task_name: G1MotionTracking
+  sim_backend: mujoco
+  play_steps: 1000
+algo:
+  num_envs: 1024
+  max_iterations: 5000
+  save_interval: 500
+reward:
+  scales:
+    motion_global_root_pos: 0.5
+    motion_global_root_ori: 0.5
+    motion_body_pos: 1.0
+    motion_body_ori: 1.0
+    motion_body_lin_vel: 1.0
+    motion_body_ang_vel: 1.0
+    motion_joint_pos: 0.0
+    motion_joint_vel: 0.0
+    action_rate_l2: -0.1
+    joint_limit: -10.0
+  std_root_pos: 0.3
+  std_root_ori: 0.4
+  std_body_pos: 0.3
+  std_body_ori: 0.4
+  std_body_lin_vel: 1.0
+  std_body_ang_vel: 3.14
+  std_joint_pos: 0.2
+  std_joint_vel: 1.0

conf/appo/reward/go2_appo_mujoco.yaml → conf/appo/task/go1_joystick/mujoco.yaml

@@ -1,4 +1,9 @@
 # @package _global_
+training:
+  task_name: Go1JoystickFlatTerrain
+  sim_backend: mujoco
+algo:
+  max_iterations: 150
 reward:
   scales:
     tracking_lin_vel: 1.0
@@ -8,8 +13,6 @@ reward:
     base_height: -100.0
     action_rate: -0.005
     similar_to_default: -0.1
-    alive: 0.0
     contact: 0.24
-    swing_feet_z: 4.0
   tracking_sigma: 0.25
   base_height_target: 0.3

conf/offpolicy/reward/go2_sac_mujoco.yaml → conf/appo/task/go2_joystick/mujoco.yaml

@@ -1,4 +1,9 @@
 # @package _global_
+training:
+  task_name: Go2JoystickFlatTerrain
+  sim_backend: mujoco
+algo:
+  max_iterations: 150
 reward:
   scales:
     tracking_lin_vel: 1.0

conf/offpolicy/config.yaml

@@ -1,8 +1,7 @@
 defaults:
   - _self_
   - algo: sac
-  - reward: default
-  - task: go1_joystick
+  - task: ${algo}/go1_joystick/mujoco
 
 training:
   task_name: Go1JoystickFlatTerrain

conf/offpolicy/task/allegro_sac.yaml → conf/offpolicy/task/sac/allegro_sac/mujoco.yaml

@@ -1,9 +1,7 @@
 # @package _global_
-defaults:
-  - override /reward: default
-
 training:
   task_name: AllegroInhandRotationSac
+  sim_backend: mujoco
 algo:
   num_envs: 2048
   batch_size: 16384
@@ -17,3 +15,15 @@ algo:
     alpha_init: 0.001
     target_entropy_ratio: 0.0
     max_grad_norm: 1.0
+reward:
+  scales:
+    rotate: 0.0
+    alive: 10.0
+    obj_linvel: -0.0
+    pose_diff: -0.0
+    torque: -0.0
+    work: -0.0
+    drop: -4000.0
+  angvel_clip_min: -0.5
+  angvel_clip_max: 0.5
+  reset_z_threshold: 0.125