more refactor

eval.sh

@@ -5,4 +5,5 @@ lerobot-eval \
   --env.control_mode=absolute \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
+  --env.episode_length=800 \
   --seed=142

src/lerobot/configs/types.py

@@ -37,7 +37,6 @@ class NormalizationMode(str, Enum):
     IDENTITY = "IDENTITY"
     QUANTILES = "QUANTILES"
     QUANTILE10 = "QUANTILE10"
-    IMAGENET = "IMAGENET"
 
 
 @dataclass

src/lerobot/envs/configs.py

@@ -245,7 +245,7 @@ class HILSerlRobotEnvConfig(EnvConfig):
 class LiberoEnv(EnvConfig):
     task: str = "libero_10"  # can also choose libero_spatial, libero_object, etc.
     fps: int = 30
-    episode_length: int = 520
+    episode_length: int | None = None
     obs_type: str = "pixels_agent_pos"
     render_mode: str = "rgb_array"
     camera_name: str = "agentview_image,robot0_eye_in_hand_image"

src/lerobot/envs/factory.py

@@ -144,6 +144,7 @@ def make_env(
             gym_kwargs=cfg.gym_kwargs,
             env_cls=env_cls,
             control_mode=cfg.control_mode,
+            episode_length=cfg.episode_length,
         )
     elif "metaworld" in cfg.type:
         from lerobot.envs.metaworld import create_metaworld_envs

src/lerobot/envs/libero.py

@@ -80,14 +80,11 @@ def get_libero_dummy_action():
     return [0, 0, 0, 0, 0, 0, -1]
 
 
-OBS_STATE_DIM = 8
 ACTION_DIM = 7
-AGENT_POS_LOW = -1000.0
-AGENT_POS_HIGH = 1000.0
 ACTION_LOW = -1.0
 ACTION_HIGH = 1.0
 TASK_SUITE_MAX_STEPS: dict[str, int] = {
-    "libero_spatial": 800,  # longest training demo has 193 steps
+    "libero_spatial": 280,  # longest training demo has 193 steps
     "libero_object": 280,  # longest training demo has 254 steps
     "libero_goal": 300,  # longest training demo has 270 steps
     "libero_10": 520,  # longest training demo has 505 steps
@@ -103,6 +100,7 @@ class LiberoEnv(gym.Env):
         task_suite: Any,
         task_id: int,
         task_suite_name: str,
+        episode_length: int | None = None,
         camera_name: str | Sequence[str] = "agentview_image,robot0_eye_in_hand_image",
         obs_type: str = "pixels",
         render_mode: str = "rgb_array",
@@ -142,13 +140,18 @@ class LiberoEnv(gym.Env):
         self.camera_name_mapping = camera_name_mapping
         self.num_steps_wait = num_steps_wait
         self.episode_index = episode_index
+        self.episode_length = episode_length
         # Load once and keep
         self._init_states = get_task_init_states(task_suite, self.task_id) if self.init_states else None
         self._init_state_id = self.episode_index  # tie each sub-env to a fixed init state
 
         self._env = self._make_envs_task(task_suite, self.task_id)
         default_steps = 500
-        self._max_episode_steps = TASK_SUITE_MAX_STEPS.get(task_suite_name, default_steps)
+        self._max_episode_steps = (
+            TASK_SUITE_MAX_STEPS.get(task_suite_name, default_steps)
+            if self.episode_length is None
+            else self.episode_length
+        )
         self.control_mode = control_mode
         images = {}
         for cam in self.camera_name:
@@ -351,6 +354,7 @@ def _make_env_fns(
     task_id: int,
     n_envs: int,
     camera_names: list[str],
+    episode_length: int | None,
     init_states: bool,
     gym_kwargs: Mapping[str, Any],
     control_mode: str,
@@ -365,6 +369,7 @@ def _make_env_fns(
             task_suite_name=suite_name,
             camera_name=camera_names,
             init_states=init_states,
+            episode_length=episode_length,
             episode_index=episode_index,
             control_mode=control_mode,
             **local_kwargs,
@@ -387,6 +392,7 @@ def create_libero_envs(
     init_states: bool = True,
     env_cls: Callable[[Sequence[Callable[[], Any]]], Any] | None = None,
     control_mode: str = "relative",
+    episode_length: int | None = None,
 ) -> dict[str, dict[int, Any]]:
     """
     Create vectorized LIBERO environments with a consistent return shape.
@@ -428,6 +434,7 @@ def create_libero_envs(
         for tid in selected:
             fns = _make_env_fns(
                 suite=suite,
+                episode_length=episode_length,
                 suite_name=suite_name,
                 task_id=tid,
                 n_envs=n_envs,

src/lerobot/scripts/lerobot_eval.py

@@ -175,7 +175,6 @@ def rollout(
         with torch.inference_mode():
             action = policy.select_action(observation)
         action = postprocessor(action)
-        
         action_transition = {"action": action}
         action_transition = env_postprocessor(action_transition)
         action = action_transition["action"]