chore(processors): tokenizers raises and remove tensor conversion (#1949)

src/lerobot/processor/normalize_processor.py

@@ -209,7 +209,7 @@ class _NormalizationMixin:
                 new_observation[key] = self._apply_transform(tensor, key, feature.type, inverse=inverse)
         return new_observation
 
-    def _normalize_action(self, action: Any, inverse: bool) -> Tensor:
+    def _normalize_action(self, action: Tensor, inverse: bool) -> Tensor:
         # Convert to tensor but preserve original dtype for adaptation logic
         """
         Applies (un)normalization to an action tensor.
@@ -221,8 +221,7 @@ class _NormalizationMixin:
         Returns:
             The transformed action tensor.
         """
-        tensor = torch.as_tensor(action)
-        processed_action = self._apply_transform(tensor, "action", FeatureType.ACTION, inverse=inverse)
+        processed_action = self._apply_transform(action, "action", FeatureType.ACTION, inverse=inverse)
         return processed_action
 
     def _apply_transform(

src/lerobot/processor/tokenizer_processor.py

@@ -118,14 +118,11 @@ class TokenizerProcessorStep(ObservationProcessorStep):
         """
         complementary_data = transition.get(TransitionKey.COMPLEMENTARY_DATA)
         if complementary_data is None:
-            return None
-
-        if self.task_key not in complementary_data:
-            return None
+            raise ValueError("Complementary data is None so no task can be extracted from it")
 
         task = complementary_data[self.task_key]
         if task is None:
-            return None
+            raise ValueError("Task extracted from Complementary data is None")
 
         # Standardize to a list of strings for the tokenizer
         if isinstance(task, str):
@@ -150,7 +147,7 @@ class TokenizerProcessorStep(ObservationProcessorStep):
         """
         task = self.get_task(self.transition)
         if task is None:
-            return observation
+            raise ValueError("Task cannot be None")
 
         # Tokenize the task (this will create CPU tensors)
         tokenized_prompt = self._tokenize_text(task)