feat(WIP): adding support for multi-process camera video capture

.github/workflows/release.yml

@@ -83,11 +83,11 @@ jobs:
           fi
 
       - name: Remove Tags with Git dependencies
-        # TODO(Steven): Temporary patch to remove pi from PyPi 0.4.0 release due to its reliance on git dependencies.
+        # TODO(Steven): Temporary patch to remove libero and pi from PyPi 0.4.0 release due to its reliance on git dependencies.
         run: |
           echo "::info:: Checking for Git dependencies to remove from pyproject.toml..."
-          grep -E '@ git\+https|lerobot\[pi\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
-          sed -E -i '/@ git\+https|lerobot\[pi\]/d' pyproject.toml
+          grep -E '@ git\+https|lerobot\[pi\]|lerobot\[libero\]' pyproject.toml | sed 's/^/::warning:: Removing line: /' || true
+          sed -E -i '/@ git\+https|lerobot\[pi\]|lerobot\[libero\]/d' pyproject.toml
           echo "::info:: Git dependencies removed. Proceeding with build."
 
       - name: Install build dependencies

.github/workflows/unbound_deps_tests.yml

@@ -70,7 +70,7 @@ jobs:
           echo "Dependencies unbound:" && cat pyproject.toml
 
       - name: Install lerobot with all extras
-        run: uv sync --all-extras --no-extra groot # TODO(Steven): Make flash-attn optional
+        run: uv sync --all-extras
 
       - name: Run pytest (all extras)
         run: uv run pytest tests -vv

README.md

@@ -186,7 +186,7 @@ For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
 > [!NOTE]
-> For lerobot 0.4.0, if you want to install pi tags, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`.
+> For lerobot 0.4.0, if you want to install libero or pi tags, you will have to do: `pip install "lerobot[pi,libero]@git+https://github.com/huggingface/lerobot.git"`.
 >
 > This will be solved in the next patch release
 

docs/source/installation.mdx

@@ -82,7 +82,7 @@ For a full list of optional dependencies, see:
 https://pypi.org/project/lerobot/
 
 > [!NOTE]
-> For lerobot 0.4.0, if you want to install pi, you will have to do: `pip install "lerobot[pi]@git+https://github.com/huggingface/lerobot.git"`
+> For lerobot 0.4.0, if you want to install libero or pi, you will have to do: `pip install "lerobot[pi,libero]@git+https://github.com/huggingface/lerobot.git"`
 
 ### Troubleshooting
 

docs/source/libero.mdx

@@ -28,6 +28,11 @@ LIBERO is now part of our **multi-eval supported simulation**, meaning you can b
 To Install LIBERO, after following LeRobot official instructions, just do:
 `pip install -e ".[libero]"`
 
+> [!NOTE]
+> For lerobot 0.4.0, if you want to install libero tag, you will have to do: `pip install "lerobot[libero]@git+https://github.com/huggingface/lerobot.git"`.
+>
+> This will be solved in the next patch release
+
 ### Single-suite evaluation
 
 Evaluate a policy on one LIBERO suite:

src/lerobot/cameras/opencv/camera_opencv.py

@@ -23,6 +23,8 @@ import platform
 import time
 from pathlib import Path
 from threading import Event, Lock, Thread
+from multiprocessing import Process, Event as EventProcess, JoinableQueue as Queue
+from queue import Empty
 from typing import Any
 
 from numpy.typing import NDArray  # type: ignore  # TODO: add type stubs for numpy.typing
@@ -119,11 +121,10 @@ class OpenCVCamera(Camera):
 
         self.videocapture: cv2.VideoCapture | None = None
 
-        self.thread: Thread | None = None
-        self.stop_event: Event | None = None
-        self.frame_lock: Lock = Lock()
+        self.process: Process | None = None
+        self.stop_event: EventProcess | None = None
+        self.frame_queue: Queue = Queue()
         self.latest_frame: NDArray[Any] | None = None
-        self.new_frame_event: Event = Event()
 
         self.rotation: int | None = get_cv2_rotation(config.rotation)
         self.backend: int = get_cv2_backend()
@@ -442,37 +443,36 @@ class OpenCVCamera(Camera):
         while not self.stop_event.is_set():
             try:
                 color_image = self.read()
-
-                with self.frame_lock:
-                    self.latest_frame = color_image
-                self.new_frame_event.set()
+                self.frame_queue.put_nowait(color_image)
 
             except DeviceNotConnectedError:
                 break
             except Exception as e:
                 logger.warning(f"Error reading frame in background thread for {self}: {e}")
 
-    def _start_read_thread(self) -> None:
+    def _start_read_process(self) -> None:
         """Starts or restarts the background read thread if it's not running."""
-        if self.thread is not None and self.thread.is_alive():
-            self.thread.join(timeout=0.1)
+        if self.process is not None and self.process.is_alive():
+            self.frame_queue.join()
+            self.process.join()
         if self.stop_event is not None:
             self.stop_event.set()
 
         self.stop_event = Event()
-        self.thread = Thread(target=self._read_loop, args=(), name=f"{self}_read_loop")
-        self.thread.daemon = True
-        self.thread.start()
+        self.process = Process(target=self._read_loop, args=(), name=f"{self}_read_loop")
+        self.process.daemon = True
+        self.process.start()
 
     def _stop_read_thread(self) -> None:
         """Signals the background read thread to stop and waits for it to join."""
         if self.stop_event is not None:
             self.stop_event.set()
 
-        if self.thread is not None and self.thread.is_alive():
-            self.thread.join(timeout=2.0)
+        if self.process is not None and self.process.is_alive():
+            self.frame_queue.join()
+            self.process.join()
 
-        self.thread = None
+        self.process = None
         self.stop_event = None
 
     def async_read(self, timeout_ms: float = 200) -> NDArray[Any]:
@@ -499,24 +499,32 @@ class OpenCVCamera(Camera):
         if not self.is_connected:
             raise DeviceNotConnectedError(f"{self} is not connected.")
 
-        if self.thread is None or not self.thread.is_alive():
-            self._start_read_thread()
+        if self.process is None or not self.process.is_alive():
+            self._start_read_process()
 
-        if not self.new_frame_event.wait(timeout=timeout_ms / 1000.0):
-            thread_alive = self.thread is not None and self.thread.is_alive()
-            raise TimeoutError(
-                f"Timed out waiting for frame from camera {self} after {timeout_ms} ms. "
-                f"Read thread alive: {thread_alive}."
-            )
+        if self.latest_frame is None:
+            self.latest_frame = self.frame_queue.get()
+            self.frame_queue.task_done()
+            return self.latest_frame
 
-        with self.frame_lock:
-            frame = self.latest_frame
-            self.new_frame_event.clear()
+        try:
+            frame = self.frame_queue.get(timeout=timeout_ms / 1000.0)
+            self.frame_queue.task_done()
+        except Empty:
+            process_alive = self.process is not None and self.process.is_alive()
+            if process_alive:
+                logger.warning(f"{self} async_read timed out after {timeout_ms} ms but camera is still running.")
+                return self.latest_frame
+            else:
+                raise TimeoutError(
+                    f"{self} async_read timed out after {timeout_ms} ms: camera is not responding !"
+                )
 
         if frame is None:
             raise RuntimeError(f"Internal error: Event set but no frame available for {self}.")
-
-        return frame
+        else:
+            self.latest_frame = frame
+            return self.latest_frame
 
     def disconnect(self) -> None:
         """

src/lerobot/datasets/lerobot_dataset.py

@@ -940,26 +940,11 @@ class LeRobotDataset(torch.utils.data.Dataset):
         return query_timestamps
 
     def _query_hf_dataset(self, query_indices: dict[str, list[int]]) -> dict:
-        """
-        Query dataset for indices across keys, skipping video keys.
-
-        Tries column-first [key][indices] for speed, falls back to row-first.
-
-        Args:
-            query_indices: Dict mapping keys to index lists to retrieve
-
-        Returns:
-            Dict with stacked tensors of queried data (video keys excluded)
-        """
-        result: dict = {}
-        for key, q_idx in query_indices.items():
-            if key in self.meta.video_keys:
-                continue
-            try:
-                result[key] = torch.stack(self.hf_dataset[key][q_idx])
-            except (KeyError, TypeError, IndexError):
-                result[key] = torch.stack(self.hf_dataset[q_idx][key])
-        return result
+        return {
+            key: torch.stack(self.hf_dataset[q_idx][key])
+            for key, q_idx in query_indices.items()
+            if key not in self.meta.video_keys
+        }
 
     def _query_videos(self, query_timestamps: dict[str, list[float]], ep_idx: int) -> dict[str, torch.Tensor]:
         """Note: When using data workers (e.g. DataLoader with num_workers>0), do not call this function