load from pretrained_path

src/lerobot/policies/pi05_openpi/configuration_pi05openpi.py

@@ -66,6 +66,9 @@ class PI05OpenPIConfig(PreTrainedConfig):
     compile_mode: str = "max-autotune"  # Torch compile mode
     device: str | None = None  # Device to use for the model (None = auto-detect)
 
+    # Pretrained model loading
+    pretrained_path: str | None = None  # Path or repo_id to load pretrained weights from
+
     # Optimizer settings: see openpi `AdamW` and
     optimizer_lr: float = 2.5e-5  # see openpi `CosineDecaySchedule: peak_lr`
     optimizer_betas: tuple[float, float] = (0.9, 0.95)

src/lerobot/policies/pi05_openpi/modeling_pi05openpi.py

@@ -875,8 +875,8 @@ class PI05OpenPIPolicy(PreTrainedPolicy):
         if pretrained_name_or_path is None:
             raise ValueError("pretrained_name_or_path is required")
 
-        # Create default config
+        # Use provided config if available, otherwise create default config
-        config = cls.config_class()
+        config = kwargs.get("config", cls.config_class())
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs

src/lerobot/policies/pi0_openpi/configuration_pi0openpi.py

@@ -63,6 +63,9 @@ class PI0OpenPIConfig(PreTrainedConfig):
     compile_mode: str = "max-autotune"  # Torch compile mode
     device: str | None = None  # Device to use for the model (None = auto-detect)
 
+    # Pretrained model loading
+    pretrained_path: str | None = None  # Path or repo_id to load pretrained weights from
+
     # Optimizer settings: see openpi `AdamW` and
     optimizer_lr: float = 2.5e-5  # see openpi `CosineDecaySchedule: peak_lr`
     optimizer_betas: tuple[float, float] = (0.9, 0.95)

src/lerobot/policies/pi0_openpi/modeling_pi0openpi.py

@@ -894,8 +894,8 @@ class PI0OpenPIPolicy(PreTrainedPolicy):
         if pretrained_name_or_path is None:
             raise ValueError("pretrained_name_or_path is required")
 
-        # Create default config
+        # Use provided config if available, otherwise create default config
-        config = cls.config_class()
+        config = kwargs.get("config", cls.config_class())
 
         # Initialize model without loading weights
         # Check if dataset_stats were provided in kwargs