feat(processor): Introduce ToBatchProcessor for handling observation batching

- Added ToBatchProcessor to ensure observations have proper batch dimensions for model processing.
- Implemented functionality to add batch dimensions to state and image observations as needed.
- Created comprehensive unit tests to validate the processor's behavior with various tensor dimensions and types.
- Ensured compatibility with existing transition keys and maintained the integrity of non-observation data.

src/lerobot/processor/__init__.py

@@ -14,6 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from .batch_processor import ToBatchProcessor
 from .device_processor import DeviceProcessor
 from .normalize_processor import NormalizerProcessor, UnnormalizerProcessor
 from .observation_processor import VanillaObservationProcessor
@@ -48,6 +49,7 @@ __all__ = [
     "RenameProcessor",
     "RewardProcessor",
     "RobotProcessor",
+    "ToBatchProcessor",
     "TransitionKey",
     "TruncatedProcessor",
     "VanillaObservationProcessor",