refactor(pipeline): Remove model card generation and streamline processor methods

- Eliminated the _generate_model_card method from RobotProcessor, which was responsible for generating README.md files from a template.
- Updated save_pretrained method to remove model card generation, focusing on serialization of processor definitions and parameters.
- Added default implementations for get_config, state_dict, load_state_dict, reset, and feature_contract methods in various processor classes to enhance consistency and usability.

src/lerobot/processor/pipeline.py

@@ -409,23 +409,6 @@ class RobotProcessor(ModelHubMixin):
         config_filename = kwargs.pop("config_filename", None)
         self.save_pretrained(destination_path, config_filename=config_filename)
 
-    def _generate_model_card(self, destination_path: str) -> None:
-        """Generate README.md from the RobotProcessor model card template."""
-        # Read the template
-        template_path = Path(__file__).parent.parent / "templates" / "robotprocessor_modelcard_template.md"
-
-        if not template_path.exists():
-            # Fallback: if template doesn't exist, skip model card generation
-            return
-
-        with open(template_path) as f:
-            model_card_content = f.read()
-
-        # Write the README.md
-        readme_path = os.path.join(destination_path, "README.md")
-        with open(readme_path, "w") as f:
-            f.write(model_card_content)
-
     def save_pretrained(self, destination_path: str, config_filename: str | None = None, **kwargs):
         """Serialize the processor definition and parameters to *destination_path*.
 
@@ -500,9 +483,6 @@ class RobotProcessor(ModelHubMixin):
         with open(os.path.join(destination_path, config_filename), "w") as file_pointer:
             json.dump(config, file_pointer, indent=2)
 
-        # Generate README.md from template
-        self._generate_model_card(destination_path)
-
     @classmethod
     def from_pretrained(
         cls, source: str, *, config_filename: str | None = None, overrides: dict[str, Any] | None = None
@@ -910,6 +890,21 @@ class ObservationProcessor:
         new_transition[TransitionKey.OBSERVATION] = processed_observation
         return new_transition
 
+    def get_config(self) -> dict[str, Any]:
+        return {}
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        return {}
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        pass
+
+    def reset(self) -> None:
+        pass
+
+    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+        return features
+
 
 class ActionProcessor:
     """Base class for processors that modify only the action component of a transition.
@@ -952,6 +947,21 @@ class ActionProcessor:
         new_transition[TransitionKey.ACTION] = processed_action
         return new_transition
 
+    def get_config(self) -> dict[str, Any]:
+        return {}
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        return {}
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        pass
+
+    def reset(self) -> None:
+        pass
+
+    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+        return features
+
 
 class RewardProcessor:
     """Base class for processors that modify only the reward component of a transition.
@@ -993,6 +1003,21 @@ class RewardProcessor:
         new_transition[TransitionKey.REWARD] = processed_reward
         return new_transition
 
+    def get_config(self) -> dict[str, Any]:
+        return {}
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        return {}
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        pass
+
+    def reset(self) -> None:
+        pass
+
+    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+        return features
+
 
 class DoneProcessor:
     """Base class for processors that modify only the done flag of a transition.
@@ -1039,6 +1064,21 @@ class DoneProcessor:
         new_transition[TransitionKey.DONE] = processed_done
         return new_transition
 
+    def get_config(self) -> dict[str, Any]:
+        return {}
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        return {}
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        pass
+
+    def reset(self) -> None:
+        pass
+
+    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+        return features
+
 
 class TruncatedProcessor:
     """Base class for processors that modify only the truncated flag of a transition.
@@ -1081,6 +1121,21 @@ class TruncatedProcessor:
         new_transition[TransitionKey.TRUNCATED] = processed_truncated
         return new_transition
 
+    def get_config(self) -> dict[str, Any]:
+        return {}
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        return {}
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        pass
+
+    def reset(self) -> None:
+        pass
+
+    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+        return features
+
 
 class InfoProcessor:
     """Base class for processors that modify only the info dictionary of a transition.
@@ -1128,6 +1183,21 @@ class InfoProcessor:
         new_transition[TransitionKey.INFO] = processed_info
         return new_transition
 
+    def get_config(self) -> dict[str, Any]:
+        return {}
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        return {}
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        pass
+
+    def reset(self) -> None:
+        pass
+
+    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+        return features
+
 
 class ComplementaryDataProcessor:
     """Base class for processors that modify only the complementary data of a transition.
@@ -1156,6 +1226,21 @@ class ComplementaryDataProcessor:
         new_transition[TransitionKey.COMPLEMENTARY_DATA] = processed_complementary_data
         return new_transition
 
+    def get_config(self) -> dict[str, Any]:
+        return {}
+
+    def state_dict(self) -> dict[str, torch.Tensor]:
+        return {}
+
+    def load_state_dict(self, state: dict[str, torch.Tensor]) -> None:
+        pass
+
+    def reset(self) -> None:
+        pass
+
+    def feature_contract(self, features: dict[str, PolicyFeature]) -> dict[str, PolicyFeature]:
+        return features
+
 
 class IdentityProcessor:
     """Identity processor that does nothing."""