fix: enable SmolVLA eval on LIBERO with custom camera mappings

- Thread camera_name_mapping from LiberoEnv config through to gym envs
- Sync features_map with camera_name_mapping in LiberoEnv.__post_init__
- Fix render() to use first available camera instead of hardcoded "image"
- Handle non-dict final_info in rollout by falling back to info["is_success"]
- Add use_peft legacy field to SmolVLAConfig for checkpoint compat
- Add defaults to GR00TN15Config init=False fields for transformers 5.3

Made-with: Cursor

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, metadata={"help": "Backbone configuration."})
+    backbone_cfg: dict = field(init=False, default_factory=dict, metadata={"help": "Backbone configuration."})
 
-    action_head_cfg: dict = field(init=False, metadata={"help": "Action head configuration."})
+    action_head_cfg: dict = field(init=False, default_factory=dict, metadata={"help": "Action head configuration."})
 
-    action_horizon: int = field(init=False, metadata={"help": "Action horizon."})
+    action_horizon: int = field(init=False, default=0, metadata={"help": "Action horizon."})
 
-    action_dim: int = field(init=False, metadata={"help": "Action dimension."})
+    action_dim: int = field(init=False, default=0, 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

@@ -109,6 +109,7 @@ 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__()