feat (device processor): Implement device processor

src/lerobot/processor/__init__.py

@@ -13,6 +13,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
+
+from .device_processor import DeviceProcessor
 from .normalize_processor import NormalizerProcessor, UnnormalizerProcessor
 from .observation_processor import (
     ImageProcessor,
@@ -34,6 +36,7 @@ from .rename_processor import RenameProcessor
 
 __all__ = [
     "ActionProcessor",
+    "DeviceProcessor",
     "DoneProcessor",
     "EnvTransition",
     "ImageProcessor",

src/lerobot/processor/device_processor.py

@@ -0,0 +1,62 @@
+#!/usr/bin/env python
+
+# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+import torch
+
+from lerobot.processor.pipeline import EnvTransition, TransitionIndex
+
+
+@dataclass
+class DeviceProcessor:
+    """Processes transitions by moving tensors to the specified device.
+
+    This processor ensures that all tensors in the transition are moved to the
+    specified device (CPU or GPU) before they are returned.
+    """
+
+    device: str = "cpu"
+
+    def __call__(self, transition: EnvTransition) -> EnvTransition:
+        observation: dict[str, torch.Tensor] = transition[TransitionIndex.OBSERVATION]
+        action = transition[TransitionIndex.ACTION]
+        reward = transition[TransitionIndex.REWARD]
+        done = transition[TransitionIndex.DONE]
+        truncated = transition[TransitionIndex.TRUNCATED]
+        info = transition[TransitionIndex.INFO]
+        complementary_data = transition[TransitionIndex.COMPLEMENTARY_DATA]
+
+        if observation is not None:
+            observation = {k: v.to(self.device) for k, v in observation.items()}
+        if action is not None:
+            action = action.to(self.device)
+
+        return (
+            observation,
+            action,
+            reward,
+            done,
+            truncated,
+            info,
+            complementary_data,
+        )
+
+    def get_config(self) -> dict[str, Any]:
+        """Return configuration for serialization."""
+        return {"device": self.device}