style: apply ruff format and fix type errors from make check

- ruff format: reformat train_rsl_rl.py and rotation.py
- rotation.py: fix reset() return type annotation to match NpEnv base
  class signature (Tuple[np.ndarray, ...] → Tuple[dict[str, np.ndarray], ...])
  after structured-obs-dict migration changed obs type
- benchmark_fast_sac_backends.py: remove unused collector_device variable
  (F841 ruff lint error)

Author: TATP-233

benchmark/benchmark_fast_sac_backends.py

@@ -34,13 +34,9 @@ def run_backend(task, max_iterations, backend, num_envs):
     # Set collector device based on backend
     device = "mps"
     if backend == "torch_mps":
-        collector_device = "mps"
-    elif backend == "torch_cpu":
-        collector_device = "cpu"
-    elif backend == "numpy":
-        collector_device = "cpu"
-    else:
-        collector_device = "cpu"
+        pass
+    elif backend in ("torch_cpu", "numpy"):
+        pass
 
     timestamp = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
     log_dir = os.path.join(ROOT_DIR, "logs", "benchmark", f"{backend}_{timestamp}")

scripts/train_appo.py

@@ -186,8 +186,6 @@ def play_appo(cfg: DictConfig, rl_cfg: dict):
 def main(cfg: DictConfig) -> None:
     ensure_registries()
 
-    from omegaconf import OmegaConf
-
     # Build env_cfg_override from reward config
     env_cfg_override = {}
     if hasattr(cfg, "reward") and cfg.reward:

scripts/train_mlx_ppo.py

@@ -153,13 +153,15 @@ def _get_physics_state_snapshot(env) -> np.ndarray:
     raise AttributeError("Env backend does not expose physics state for video rendering")
 
 
-def play_mlx_ppo(cfg: DictConfig, dtype, use_fp16: bool, resolved_sim_backend: str,
-                 task_log_root: Path):
+def play_mlx_ppo(
+    cfg: DictConfig, dtype, use_fp16: bool, resolved_sim_backend: str, task_log_root: Path
+):
     """Play mode for MLX PPO."""
     play_model_dtype = mx.float32 if use_fp16 else dtype
     play_env_num = cfg.training.play_env_num
-    env = registry.make(cfg.training.task_name, num_envs=play_env_num,
-                        sim_backend=resolved_sim_backend)
+    env = registry.make(
+        cfg.training.task_name, num_envs=play_env_num, sim_backend=resolved_sim_backend
+    )
     obs_dim = sum(env.obs_groups_spec.values())
     action_dim = env.action_space.shape[0]
     model = build_model(cfg.algo, obs_dim, action_dim, dtype=play_model_dtype)
@@ -478,9 +480,7 @@ def log(msg: str) -> None:
     )
     rich_logger.start()
 
-    episode_returns = np.zeros(
-        (cfg.algo.num_envs,), dtype=(np.float16 if use_fp16 else np.float32)
-    )
+    episode_returns = np.zeros((cfg.algo.num_envs,), dtype=(np.float16 if use_fp16 else np.float32))
     episode_lengths = np.zeros((cfg.algo.num_envs,), dtype=np.int32)
     reward_window = deque(maxlen=100)
     length_window = deque(maxlen=100)

scripts/train_offpolicy.py

@@ -83,6 +83,7 @@ def build_runner(algo_name: str, cfg: DictConfig):
     env_cfg_override = None
     if hasattr(cfg, "reward") and cfg.reward:
         from omegaconf import OmegaConf
+
         reward_dict = OmegaConf.to_container(cfg.reward, resolve=True)
         env_cfg_override = {"reward_config": reward_dict}
 

scripts/train_rsl_rl.py

@@ -264,9 +264,10 @@ def play_rsl_rl(cfg: DictConfig, device: str):
 def main(cfg: DictConfig) -> None:
     ensure_registries()
 
-    from unilab.base import registry
     from omegaconf import OmegaConf
 
+    from unilab.base import registry
+
     # Build env_cfg_override from reward config
     env_cfg_override = {}
     if hasattr(cfg, "reward") and cfg.reward:

src/unilab/algos/torch/fast_sac/runner.py

@@ -45,7 +45,9 @@ def __init__(
         from unilab.utils.algo_utils import ensure_registries
 
         ensure_registries()
-        env: Any = registry.make(env_name, num_envs=1, sim_backend=sim_backend, env_cfg_override=env_cfg_override)
+        env: Any = registry.make(
+            env_name, num_envs=1, sim_backend=sim_backend, env_cfg_override=env_cfg_override
+        )
         obs_dim = sum(env.obs_groups_spec.values())
         act_space_shape = env.action_space.shape
         assert act_space_shape is not None

src/unilab/algos/torch/offpolicy/worker.py

@@ -103,7 +103,9 @@ def _run_collector(
     ensure_registries()
 
     # Initialize environment
-    env = registry.make(env_name, num_envs=num_envs, sim_backend=sim_backend, env_cfg_override=env_cfg_override)
+    env = registry.make(
+        env_name, num_envs=num_envs, sim_backend=sim_backend, env_cfg_override=env_cfg_override
+    )
     if env.state is None:
         env.init_state()
 

src/unilab/envs/manipulation/inhand_rot_allegro/rotation.py

@@ -263,7 +263,9 @@ def _get_obs(self, info: dict) -> dict[str, np.ndarray]:
 
     # ── Reset ────────────────────────────────────────────────────────
 
-    def reset(self, env_indices: np.ndarray) -> Tuple[np.ndarray, np.ndarray, dict]:
+    def reset(
+        self, env_indices: np.ndarray
+    ) -> Tuple[dict[str, np.ndarray], dict[str, np.ndarray], dict]:
         num_reset = len(env_indices)
         dr = self._cfg.domain_rand
 

tests/base/test_reward_override.py

@@ -1,6 +1,7 @@
 """Test reward config override through registry."""
 
 import pytest
+
 from unilab.base import registry
 from unilab.utils.algo_utils import ensure_registries
 
@@ -21,7 +22,7 @@ def test_reward_override_go1():
         "Go1JoystickFlatTerrain",
         num_envs=1,
         sim_backend="mujoco",
-        env_cfg_override={"reward_config": override_config}
+        env_cfg_override={"reward_config": override_config},
     )
 
     assert env._cfg.reward_config.scales["tracking_lin_vel"] == 999.0
@@ -44,7 +45,7 @@ def test_reward_override_g1():
         "G1WalkTaskMjSAC",
         num_envs=1,
         sim_backend="mujoco",
-        env_cfg_override={"reward_config": override_config}
+        env_cfg_override={"reward_config": override_config},
     )
 
     assert env._cfg.reward_config.scales["tracking_lin_vel"] == 888.0

tests/config/test_reward_injection.py

@@ -1,7 +1,7 @@
 """Test reward config injection system."""
 
 import pytest
-from hydra import initialize, compose
+from hydra import compose, initialize
 from omegaconf import OmegaConf
 
 
@@ -44,7 +44,7 @@ def test_reward_config_conversion():
         "G1WalkTaskMjSAC",
         num_envs=1,
         sim_backend="mujoco",
-        env_cfg_override={"reward_config": g1_dict}
+        env_cfg_override={"reward_config": g1_dict},
     )
     assert hasattr(env._cfg.reward_config, "scales")
     assert env._cfg.reward_config.scales["tracking_lin_vel"] == 2.0
@@ -60,7 +60,7 @@ def test_reward_config_conversion():
         "Go1JoystickFlatTerrain",
         num_envs=1,
         sim_backend="mujoco",
-        env_cfg_override={"reward_config": go1_dict}
+        env_cfg_override={"reward_config": go1_dict},
     )
     assert hasattr(env._cfg.reward_config, "scales")
     assert env._cfg.reward_config.scales["tracking_lin_vel"] == 1.0