refactor: revert policy changes, keep env-only camera mapping fixes

- Revert GR00T N1.5 default_factory/default changes (transformers compat)
- Revert SmolVLA use_peft legacy field
- Apply ruff formatting fixes
- camera_name_mapping stays entirely in env/eval layer (no policy changes)

Made-with: Cursor

src/lerobot/envs/configs.py

@@ -103,6 +103,7 @@ class EnvConfig(draccus.ChoiceRegistry, abc.ABC):
 
         try:
             from gymnasium.vector import AutoresetMode
+
             vec = env_cls([_make_one for _ in range(n_envs)], autoreset_mode=AutoresetMode.SAME_STEP)
         except ImportError:
             vec = env_cls([_make_one for _ in range(n_envs)])

src/lerobot/policies/groot/groot_n1.py

@@ -176,13 +176,13 @@ N_COLOR_CHANNELS = 3
 @dataclass
 class GR00TN15Config(PretrainedConfig):
     model_type = "gr00t_n1_5"
-    backbone_cfg: dict = field(init=False, default_factory=dict, metadata={"help": "Backbone configuration."})
+    backbone_cfg: dict = field(init=False, metadata={"help": "Backbone configuration."})
 
-    action_head_cfg: dict = field(init=False, default_factory=dict, metadata={"help": "Action head configuration."})
+    action_head_cfg: dict = field(init=False, metadata={"help": "Action head configuration."})
 
-    action_horizon: int = field(init=False, default=0, metadata={"help": "Action horizon."})
+    action_horizon: int = field(init=False, metadata={"help": "Action horizon."})
 
-    action_dim: int = field(init=False, default=0, metadata={"help": "Action dimension."})
+    action_dim: int = field(init=False, metadata={"help": "Action dimension."})
     compute_dtype: str = field(default="float32", metadata={"help": "Compute dtype."})
 
     def __init__(self, **kwargs):

src/lerobot/policies/smolvla/configuration_smolvla.py

@@ -109,7 +109,6 @@ class SmolVLAConfig(PreTrainedConfig):
     compile_model: bool = False  # Whether to use torch.compile for model optimization
     compile_mode: str = "max-autotune"  # Torch compile mode
 
-
     def __post_init__(self):
         super().__post_init__()
 

src/lerobot/scripts/lerobot_eval.py

@@ -197,7 +197,9 @@ def rollout(
             successes = info["final_info"]["is_success"].tolist()
         elif "is_success" in info:
             is_success = info["is_success"]
-            successes = is_success.tolist() if hasattr(is_success, "tolist") else [bool(is_success)] * env.num_envs
+            successes = (
+                is_success.tolist() if hasattr(is_success, "tolist") else [bool(is_success)] * env.num_envs
+            )
         else:
             successes = [False] * env.num_envs