chore(dependencies): update uv.lock

* fix(images/videos): fixing aggregate_pipeline_dataset_features to avoid unwanted images features deletion when videos are not used

* fix(docstrings): improving docstrings

Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>

---------

Signed-off-by: Caroline Pascal <caroline8.pascal@gmail.com>

.github/workflows/benchmark_tests.yml

@@ -167,9 +167,9 @@ jobs:
 
       # ── LIBERO TRAIN+EVAL SMOKE ──────────────────────────────────────────────
       # Train SmolVLA for 1 step (batch_size=1, dataset episode 0 only) then
-      # immediately runs eval inside the training loop (env_eval_freq=1, 1 episode).
+      # immediately runs eval inside the training loop (eval_freq=1, 1 episode).
       # Tests the full train→eval-within-training pipeline end-to-end.
-      - name: Run Libero train+eval smoke (1 step, env_eval_freq=1)
+      - name: Run Libero train+eval smoke (1 step, eval_freq=1)
         if: env.HF_USER_TOKEN != ''
         run: |
           docker run --name libero-train-smoke --gpus all \
@@ -196,7 +196,7 @@ jobs:
                 --output_dir=/tmp/train-smoke \
                 --steps=1 \
                 --batch_size=1 \
-                --env_eval_freq=1 \
+                --eval_freq=1 \
                 --eval.n_episodes=1 \
                 --eval.batch_size=1 \
                 --eval.use_async_envs=false \

Makefile

@@ -58,7 +58,7 @@ test-act-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=4 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_freq=2 \
@@ -96,7 +96,7 @@ test-diffusion-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=2 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -126,7 +126,7 @@ test-tdmpc-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=2 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_checkpoint=true \
@@ -161,7 +161,7 @@ test-smolvla-ete-train:
 		--dataset.episodes="[0]" \
 		--batch_size=2 \
 		--steps=4 \
-		--env_eval_freq=2 \
+		--eval_freq=2 \
 		--eval.n_episodes=1 \
 		--eval.batch_size=1 \
 		--save_freq=2 \

docs/source/hil_data_collection.mdx

@@ -57,11 +57,11 @@ The `lerobot-rollout --strategy.type=dagger` mode requires **teleoperators with
 
 **Compatible teleoperators:**
 
-- `openarm_mini` - OpenArm Mini
+- `bi_openarm_mini` - Bimanual OpenArm Mini
 - `so_leader` - SO100 / SO101 leader arm
 
 > [!IMPORTANT]
-> The provided commands default to `bi_openarm_follower` + `openarm_mini`.
+> The provided commands default to `bi_openarm_follower` + `bi_openarm_mini`.
 > `so_follower` + `so_leader` configs are also registered and can be used via CLI flags.
 
 ---
@@ -104,9 +104,9 @@ lerobot-rollout --strategy.type=dagger \
     --robot.right_arm_config.port=can0 \
     --robot.right_arm_config.side=right \
     --robot.cameras='{left_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}, right_wrist: {type: opencv, index_or_path: "/dev/video4", width: 1280, height: 720, fps: 30}, base: {type: opencv, index_or_path: "/dev/video2", width: 640, height: 480, fps: 30}}' \
-    --teleop.type=openarm_mini \
-    --teleop.port_left=/dev/ttyACM0 \
-    --teleop.port_right=/dev/ttyACM1 \
+    --teleop.type=bi_openarm_mini \
+    --teleop.left_arm_config.port=/dev/ttyACM0 \
+    --teleop.right_arm_config.port=/dev/ttyACM1 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
     --dataset.repo_id=your-username/rollout_hil_dataset \
     --dataset.single_task="Fold the T-shirt properly" \
@@ -131,9 +131,9 @@ lerobot-rollout --strategy.type=dagger \
     --robot.right_arm_config.port=can0 \
     --robot.right_arm_config.side=right \
     --robot.cameras='{left_wrist: {type: opencv, index_or_path: "/dev/video0", width: 1280, height: 720, fps: 30}, right_wrist: {type: opencv, index_or_path: "/dev/video4", width: 1280, height: 720, fps: 30}, base: {type: opencv, index_or_path: "/dev/video2", width: 640, height: 480, fps: 30}}' \
-    --teleop.type=openarm_mini \
-    --teleop.port_left=/dev/ttyACM0 \
-    --teleop.port_right=/dev/ttyACM1 \
+    --teleop.type=bi_openarm_mini \
+    --teleop.left_arm_config.port=/dev/ttyACM0 \
+    --teleop.right_arm_config.port=/dev/ttyACM1 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
     --dataset.repo_id=your-username/rollout_hil_rtc_dataset \
     --dataset.single_task="Fold the T-shirt properly" \

docs/source/hilserl.mdx

@@ -719,7 +719,7 @@ Example configuration for training the [reward classifier](https://huggingface.c
   "num_workers": 4,
   "steps": 5000,
   "log_freq": 10,
-  "env_eval_freq": 1000,
+  "eval_freq": 1000,
   "save_freq": 1000,
   "save_checkpoint": true,
   "seed": 2,

docs/source/inference.mdx

@@ -117,7 +117,7 @@ lerobot-rollout \
     --strategy.num_episodes=20 \
     --policy.path=outputs/pretrain/checkpoints/last/pretrained_model \
     --robot.type=bi_openarm_follower \
-    --teleop.type=openarm_mini \
+    --teleop.type=bi_openarm_mini \
     --dataset.repo_id=${HF_USER}/rollout_hil_data \
     --dataset.single_task="Fold the T-shirt"
 ```

docs/source/libero.mdx

@@ -143,7 +143,7 @@ lerobot-train \
   --batch_size=4 \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
-  --env_eval_freq=1000
+  --eval_freq=1000
 ```
 
 ## Reproducing published results

docs/source/libero_plus.mdx

@@ -173,7 +173,7 @@ lerobot-train \
     --batch_size=4 \
     --eval.batch_size=1 \
     --eval.n_episodes=1 \
-    --env_eval_freq=1000
+    --eval_freq=1000
 ```
 
 ## Relationship to LIBERO

docs/source/metaworld.mdx

@@ -120,11 +120,11 @@ lerobot-train \
   --batch_size=4 \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
-  --env_eval_freq=1000
+  --eval_freq=1000
 ```
 
 ## Practical tips
 
 - Use the one-hot task conditioning for multi-task training (MT10/MT50 conventions) so policies have explicit task context.
 - Inspect the dataset task descriptions and the `info["is_success"]` keys when writing post-processing or logging so your success metrics line up with the benchmark.
-- Adjust `batch_size`, `steps`, and `env_eval_freq` to match your compute budget.
+- Adjust `batch_size`, `steps`, and `eval_freq` to match your compute budget.

docs/source/molmoact2.mdx

@@ -103,7 +103,7 @@ accelerate launch \
   --batch_size=32 \
   --num_workers=4 \
   --log_freq=20 \
-  --env_eval_freq=-1 \
+  --eval_freq=-1 \
   --save_checkpoint=true \
   --save_freq=2000
 ```
@@ -142,7 +142,7 @@ accelerate launch \
   --batch_size=32 \
   --num_workers=4 \
   --log_freq=20 \
-  --env_eval_freq=-1 \
+  --eval_freq=-1 \
   --save_checkpoint=true \
   --save_freq=2000
 ```

docs/source/multi_task_dit.mdx

@@ -314,7 +314,7 @@ lerobot-train \
   --steps=30000 \
   --save_freq=1000 \
   --log_freq=100 \
-  --env_eval_freq=1000 \
+  --eval_freq=1000 \
   --policy.type=multi_task_dit \
   --policy.device=cuda \
   --policy.horizon=32 \

docs/source/robocasa.mdx

@@ -166,7 +166,7 @@ lerobot-train \
   --output_dir=./outputs/smolvla_robocasa_CloseFridge \
   --steps=100000 \
   --batch_size=4 \
-  --env_eval_freq=5000 \
+  --eval_freq=5000 \
   --eval.batch_size=1 \
   --eval.n_episodes=5 \
   --save_freq=10000

docs/source/vlabench.mdx

@@ -165,7 +165,7 @@ lerobot-train \
   --output_dir=./outputs/smolvla_vlabench_primitive \
   --steps=100000 \
   --batch_size=4 \
-  --env_eval_freq=5000 \
+  --eval_freq=5000 \
   --eval.batch_size=1 \
   --eval.n_episodes=1 \
   --save_freq=10000

src/lerobot/configs/default.py

@@ -39,8 +39,6 @@ class DatasetConfig:
     # This reduces memory and speeds up DataLoader IPC. The training pipeline handles the conversion.
     return_uint8: bool = False
     streaming: bool = False
-    # Fraction of episodes held out per task for offline evaluation (0.0 = disabled).
-    eval_split: float = 0.0
 
     def __post_init__(self) -> None:
         if self.episodes is not None:

src/lerobot/configs/train.py

@@ -100,13 +100,8 @@ class TrainPipelineConfig(HubMixin):
     prefetch_factor: int = 4
     persistent_workers: bool = True
     steps: int = 100_000
-    # Run policy in the simulation environment every N steps to measure reward/success (0 = disabled).
-    env_eval_freq: int = 20_000
+    eval_freq: int = 20_000
     log_freq: int = 200
-    # Compute eval loss on held-out episodes every N steps (0 = disabled). Requires eval_split > 0.
-    eval_steps: int = 0
-    # Cap on total eval samples, split uniformly across tasks (0 = use all held-out data).
-    max_eval_samples: int = 0
     tolerance_s: float = 1e-4
     save_checkpoint: bool = True
     # Checkpoint is saved every `save_freq` training iterations and after the last training step.

src/lerobot/datasets/__init__.py

@@ -35,7 +35,7 @@ from .dataset_tools import (
     remove_feature,
     split_dataset,
 )
-from .factory import make_dataset, make_train_eval_datasets, resolve_delta_timestamps
+from .factory import make_dataset, resolve_delta_timestamps
 from .image_writer import safe_stop_image_writer
 from .io_utils import load_episodes, write_stats
 from .language import (
@@ -89,7 +89,6 @@ __all__ = [
     "get_feature_stats",
     "load_episodes",
     "make_dataset",
-    "make_train_eval_datasets",
     "merge_datasets",
     "modify_features",
     "modify_tasks",

src/lerobot/datasets/factory.py

@@ -14,7 +14,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 import logging
-import math
 from pprint import pformat
 
 import torch
@@ -131,81 +130,3 @@ def make_dataset(cfg: TrainPipelineConfig) -> LeRobotDataset | MultiLeRobotDatas
                 dataset.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
 
     return dataset
-
-
-def make_train_eval_datasets(
-    cfg: TrainPipelineConfig,
-) -> tuple[LeRobotDataset | MultiLeRobotDataset, LeRobotDataset | None]:
-    """Create train and optional eval datasets by splitting episodes based on eval_split.
-
-    The last ceil(n_episodes * eval_split) episodes per task are held out for evaluation.
-    If eval_split == 0.0, returns (full_dataset, None).
-    """
-    full_dataset = make_dataset(cfg)
-
-    if cfg.dataset.eval_split == 0.0:
-        return full_dataset, None
-
-    base_episodes = (
-        full_dataset.episodes if full_dataset.episodes is not None else list(range(full_dataset.num_episodes))
-    )
-
-    episode_tasks = full_dataset.meta.episodes["tasks"]
-    task_to_episodes: dict[str, list[int]] = {}
-    for ep_idx in base_episodes:
-        task_key = episode_tasks[ep_idx][0] if episode_tasks[ep_idx] else ""
-        task_to_episodes.setdefault(task_key, []).append(ep_idx)
-
-    train_episodes, eval_episodes = [], []
-    for eps in task_to_episodes.values():
-        n_eval = math.ceil(len(eps) * cfg.dataset.eval_split)
-        train_episodes.extend(eps[: len(eps) - n_eval])
-        eval_episodes.extend(eps[len(eps) - n_eval :])
-
-    if not train_episodes:
-        raise ValueError(
-            f"eval_split={cfg.dataset.eval_split} leaves 0 training episodes from {len(base_episodes)} total."
-        )
-
-    logging.info(
-        f"Train/eval split: {len(train_episodes)} train, {len(eval_episodes)} eval "
-        f"(eval_split={cfg.dataset.eval_split}, {len(task_to_episodes)} tasks)"
-    )
-
-    delta_timestamps = resolve_delta_timestamps(cfg.trainable_config, full_dataset.meta)
-
-    train_image_transforms = (
-        ImageTransforms(cfg.dataset.image_transforms) if cfg.dataset.image_transforms.enable else None
-    )
-
-    train_dataset = LeRobotDataset(
-        cfg.dataset.repo_id,
-        root=cfg.dataset.root,
-        episodes=train_episodes,
-        delta_timestamps=delta_timestamps,
-        image_transforms=train_image_transforms,
-        revision=cfg.dataset.revision,
-        video_backend=cfg.dataset.video_backend,
-        return_uint8=True,
-        tolerance_s=cfg.tolerance_s,
-    )
-
-    eval_dataset = LeRobotDataset(
-        cfg.dataset.repo_id,
-        root=cfg.dataset.root,
-        episodes=eval_episodes,
-        delta_timestamps=delta_timestamps,
-        image_transforms=None,
-        revision=cfg.dataset.revision,
-        video_backend=cfg.dataset.video_backend,
-        return_uint8=True,
-        tolerance_s=cfg.tolerance_s,
-    )
-
-    if cfg.dataset.use_imagenet_stats:
-        for ds in (train_dataset, eval_dataset):
-            for key in ds.meta.camera_keys:
-                for stats_type, stats in IMAGENET_STATS.items():
-                    ds.meta.stats[key][stats_type] = torch.tensor(stats, dtype=torch.float32)
-
-    return train_dataset, eval_dataset

src/lerobot/datasets/pipeline_features.py

@@ -70,19 +70,21 @@ def aggregate_pipeline_dataset_features(
     initial_features: dict[PipelineFeatureType, dict[str, Any]],
     *,
     use_videos: bool = True,
+    exclude_images: bool = False,
     patterns: Sequence[str] | None = None,
 ) -> dict[str, dict]:
     """
     Aggregates and filters pipeline features to create a dataset-ready features dictionary.
 
     This function transforms initial features using the pipeline, categorizes them as action or observations
-    (image or state), filters them based on `use_videos` and `patterns`, and finally
+    (image or state), filters them based on `exclude_images` and `patterns`, and finally
     formats them for use with a Hugging Face LeRobot Dataset.
 
     Args:
         pipeline: The DataProcessorPipeline to apply.
         initial_features: A dictionary of raw feature specs for actions and observations.
-        use_videos: If False, image features are excluded.
+        use_videos: Controls the storage dtype for image features. If True, images are stored as "video"; if False, they are stored as "image".
+        exclude_images: If True, image features are dropped entirely from the output.
         patterns: A sequence of regex patterns to filter action and state features.
                   Image features are not affected by this filter.
 
@@ -120,7 +122,7 @@ def aggregate_pipeline_dataset_features(
             )
 
             # 2. Apply filtering rules.
-            if is_image and not use_videos:
+            if is_image and exclude_images:
                 continue
             if not is_image and not should_keep(key, compiled_patterns):
                 continue

src/lerobot/robots/bi_openarm_follower/bi_openarm_follower.py

@@ -18,7 +18,8 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..openarm_follower import OpenArmFollower, OpenArmFollowerConfig
 from ..robot import Robot
@@ -27,7 +28,7 @@ from .config_bi_openarm_follower import BiOpenArmFollowerConfig
 logger = logging.getLogger(__name__)
 
 
-class BiOpenArmFollower(Robot):
+class BiOpenArmFollower(BimanualMixin, Robot):
     """
     Bimanual OpenArm Follower Arms
     """
@@ -39,15 +40,17 @@ class BiOpenArmFollower(Robot):
         super().__init__(config)
         self.config = config
 
-        # Top-level cameras are distributed evenly: each arm's OpenArmFollower
-        # will only open the cameras assigned to it. Per-arm cameras are used
-        # as fallback when top-level cameras are empty.
-        if config.cameras:
-            left_cameras = config.cameras
-            right_cameras = {}
-        else:
-            left_cameras = config.left_arm_config.cameras
-            right_cameras = config.right_arm_config.cameras
+        # Top-level cameras are opened by `left_arm` for convenience, but their
+        # keys stay unprefixed in observations (tracked via `_top_level_cam_keys`).
+        self._top_level_cam_keys = set(config.cameras)
+        _collisions = self._top_level_cam_keys & set(
+            config.left_arm_config.cameras
+        ) | self._top_level_cam_keys & set(config.right_arm_config.cameras)
+        if _collisions:
+            raise ValueError(
+                f"Top-level camera names collide with per-arm camera names: {sorted(_collisions)}"
+            )
+        left_arm_cameras = {**config.left_arm_config.cameras, **config.cameras}
 
         left_arm_config = OpenArmFollowerConfig(
             id=f"{config.id}_left" if config.id else None,
@@ -56,7 +59,7 @@ class BiOpenArmFollower(Robot):
             disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
             use_velocity_and_torque=config.left_arm_config.use_velocity_and_torque,
             max_relative_target=config.left_arm_config.max_relative_target,
-            cameras=left_cameras,
+            cameras=left_arm_cameras,
             side=config.left_arm_config.side,
             can_interface=config.left_arm_config.can_interface,
             use_can_fd=config.left_arm_config.use_can_fd,
@@ -75,7 +78,7 @@ class BiOpenArmFollower(Robot):
             disable_torque_on_disconnect=config.right_arm_config.disable_torque_on_disconnect,
             use_velocity_and_torque=config.right_arm_config.use_velocity_and_torque,
             max_relative_target=config.right_arm_config.max_relative_target,
-            cameras=right_cameras,
+            cameras=config.right_arm_config.cameras,
             side=config.right_arm_config.side,
             can_interface=config.right_arm_config.can_interface,
             use_can_fd=config.right_arm_config.use_can_fd,
@@ -95,22 +98,19 @@ class BiOpenArmFollower(Robot):
 
     @property
     def _motors_ft(self) -> dict[str, type]:
-        left_arm_motors_ft = self.left_arm._motors_ft
-        right_arm_motors_ft = self.right_arm._motors_ft
-
-        # Right first, then left — matches the teleoperator (OpenArmMini) ordering
-        # and the dataset feature names recorded during data collection.
         return {
-            **{f"right_{k}": v for k, v in right_arm_motors_ft.items()},
-            **{f"left_{k}": v for k, v in left_arm_motors_ft.items()},
+            **{f"left_{k}": v for k, v in self.left_arm._motors_ft.items()},
+            **{f"right_{k}": v for k, v in self.right_arm._motors_ft.items()},
         }
 
     @property
     def _cameras_ft(self) -> dict[str, tuple]:
-        # Cameras already have unique user-chosen names (e.g. "left_wrist", "base",
-        # "right_wrist"), so we merge them directly — unlike motors which need the
-        # left_/right_ prefix to disambiguate identical per-arm joint names.
-        return {**self.left_arm._cameras_ft, **self.right_arm._cameras_ft}
+        out: dict[str, tuple] = {}
+        for k, v in self.left_arm._cameras_ft.items():
+            out[k if k in self._top_level_cam_keys else f"left_{k}"] = v
+        for k, v in self.right_arm._cameras_ft.items():
+            out[f"right_{k}"] = v
+        return out
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -120,27 +120,6 @@ class BiOpenArmFollower(Robot):
     def action_features(self) -> dict[str, type]:
         return self._motors_ft
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     def setup_motors(self) -> None:
         raise NotImplementedError(
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
@@ -148,21 +127,15 @@ class BiOpenArmFollower(Robot):
 
     @check_if_not_connected
     def get_observation(self) -> RobotObservation:
-        obs_dict = {}
+        obs_dict: RobotObservation = {}
 
-        # Camera keys that should NOT get the arm prefix (they already have unique names)
-        left_cam_keys = set(self.left_arm.cameras.keys())
-        right_cam_keys = set(self.right_arm.cameras.keys())
+        # Add "left_" prefix to per-arm keys; keep top-level camera keys unprefixed.
+        for key, value in self.left_arm.get_observation().items():
+            obs_dict[key if key in self._top_level_cam_keys else f"left_{key}"] = value
 
-        # Right first, then left — matches the teleoperator (OpenArmMini) ordering
-        # and the dataset feature names recorded during data collection.
-        right_obs = self.right_arm.get_observation()
-        for key, value in right_obs.items():
-            obs_dict[key if key in right_cam_keys else f"right_{key}"] = value
-
-        left_obs = self.left_arm.get_observation()
-        for key, value in left_obs.items():
-            obs_dict[key if key in left_cam_keys else f"left_{key}"] = value
+        # Add "right_" prefix
+        for key, value in self.right_arm.get_observation().items():
+            obs_dict[f"right_{key}"] = value
 
         return obs_dict
 
@@ -189,9 +162,4 @@ class BiOpenArmFollower(Robot):
         prefixed_sent_action_left = {f"left_{key}": value for key, value in sent_action_left.items()}
         prefixed_sent_action_right = {f"right_{key}": value for key, value in sent_action_right.items()}
 
-        return {**prefixed_sent_action_right, **prefixed_sent_action_left}
-
-    @check_if_not_connected
-    def disconnect(self):
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()
+        return {**prefixed_sent_action_left, **prefixed_sent_action_right}

src/lerobot/robots/bi_openarm_follower/config_bi_openarm_follower.py

@@ -32,5 +32,7 @@ class BiOpenArmFollowerConfig(RobotConfig):
     left_arm_config: OpenArmFollowerConfigBase
     right_arm_config: OpenArmFollowerConfigBase
 
-    # Top-level cameras shared across both arms.
+    # Top-level cameras not attached to a specific side. Keys are kept as-is in
+    # observations (no `left_`/`right_` prefix). Per-arm cameras (declared on
+    # `{left,right}_arm_config.cameras`) are prefixed.
     cameras: dict[str, CameraConfig] = field(default_factory=dict)

src/lerobot/robots/bi_rebot_b601_follower/bi_rebot_b601_follower.py

@@ -18,7 +18,8 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..rebot_b601_follower import RebotB601Follower, RebotB601FollowerRobotConfig
 from ..robot import Robot
@@ -27,7 +28,7 @@ from .config_bi_rebot_b601_follower import BiRebotB601FollowerConfig
 logger = logging.getLogger(__name__)
 
 
-class BiRebotB601Follower(Robot):
+class BiRebotB601Follower(BimanualMixin, Robot):
     """Bimanual Seeed Studio reBot B601-DM follower.
 
     Composes two single-arm :class:`RebotB601Follower` instances. Observation and
@@ -41,6 +42,18 @@ class BiRebotB601Follower(Robot):
         super().__init__(config)
         self.config = config
 
+        # Top-level cameras are opened by `left_arm` for convenience, but their
+        # keys stay unprefixed in observations (tracked via `_top_level_cam_keys`).
+        self._top_level_cam_keys = set(config.cameras)
+        _collisions = self._top_level_cam_keys & set(
+            config.left_arm_config.cameras
+        ) | self._top_level_cam_keys & set(config.right_arm_config.cameras)
+        if _collisions:
+            raise ValueError(
+                f"Top-level camera names collide with per-arm camera names: {sorted(_collisions)}"
+            )
+        left_arm_cameras = {**config.left_arm_config.cameras, **config.cameras}
+
         left_arm_config = RebotB601FollowerRobotConfig(
             id=f"{config.id}_left" if config.id else None,
             calibration_dir=config.calibration_dir,
@@ -49,7 +62,7 @@ class BiRebotB601Follower(Robot):
             dm_serial_baud=config.left_arm_config.dm_serial_baud,
             disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
             max_relative_target=config.left_arm_config.max_relative_target,
-            cameras=config.left_arm_config.cameras,
+            cameras=left_arm_cameras,
             motor_can_ids=config.left_arm_config.motor_can_ids,
             pos_vel_velocity=config.left_arm_config.pos_vel_velocity,
             gripper_torque_ratio=config.left_arm_config.gripper_torque_ratio,
@@ -86,10 +99,12 @@ class BiRebotB601Follower(Robot):
 
     @property
     def _cameras_ft(self) -> dict[str, tuple]:
-        return {
-            **{f"left_{k}": v for k, v in self.left_arm._cameras_ft.items()},
-            **{f"right_{k}": v for k, v in self.right_arm._cameras_ft.items()},
-        }
+        out: dict[str, tuple] = {}
+        for k, v in self.left_arm._cameras_ft.items():
+            out[k if k in self._top_level_cam_keys else f"left_{k}"] = v
+        for k, v in self.right_arm._cameras_ft.items():
+            out[f"right_{k}"] = v
+        return out
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -99,32 +114,13 @@ class BiRebotB601Follower(Robot):
     def action_features(self) -> dict[str, type]:
         return self._motors_ft
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     @check_if_not_connected
     def get_observation(self) -> RobotObservation:
-        obs_dict = {}
-        obs_dict.update({f"left_{k}": v for k, v in self.left_arm.get_observation().items()})
-        obs_dict.update({f"right_{k}": v for k, v in self.right_arm.get_observation().items()})
+        obs_dict: RobotObservation = {}
+        for k, v in self.left_arm.get_observation().items():
+            obs_dict[k if k in self._top_level_cam_keys else f"left_{k}"] = v
+        for k, v in self.right_arm.get_observation().items():
+            obs_dict[f"right_{k}"] = v
         return obs_dict
 
     @check_if_not_connected
@@ -143,8 +139,3 @@ class BiRebotB601Follower(Robot):
             **{f"left_{k}": v for k, v in sent_action_left.items()},
             **{f"right_{k}": v for k, v in sent_action_right.items()},
         }
-
-    @check_if_not_connected
-    def disconnect(self) -> None:
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/robots/bi_rebot_b601_follower/config_bi_rebot_b601_follower.py

@@ -14,7 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
+
+from lerobot.cameras import CameraConfig
 
 from ..config import RobotConfig
 from ..rebot_b601_follower import RebotB601FollowerConfig
@@ -27,3 +29,8 @@ class BiRebotB601FollowerConfig(RobotConfig):
 
     left_arm_config: RebotB601FollowerConfig
     right_arm_config: RebotB601FollowerConfig
+
+    # Top-level cameras not attached to a specific side. Keys are kept as-is in
+    # observations (no `left_`/`right_` prefix). Per-arm cameras (declared on
+    # `{left,right}_arm_config.cameras`) are prefixed.
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)

src/lerobot/robots/bi_so_follower/bi_so_follower.py

@@ -18,7 +18,8 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction, RobotObservation
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..robot import Robot
 from ..so_follower import SOFollower, SOFollowerRobotConfig
@@ -27,7 +28,7 @@ from .config_bi_so_follower import BiSOFollowerConfig
 logger = logging.getLogger(__name__)
 
 
-class BiSOFollower(Robot):
+class BiSOFollower(BimanualMixin, Robot):
     """
     [Bimanual SO Follower Arms](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio
     """
@@ -39,6 +40,18 @@ class BiSOFollower(Robot):
         super().__init__(config)
         self.config = config
 
+        # Top-level cameras are opened by `left_arm` for convenience, but their
+        # keys stay unprefixed in observations (tracked via `_top_level_cam_keys`).
+        self._top_level_cam_keys = set(config.cameras)
+        _collisions = self._top_level_cam_keys & set(
+            config.left_arm_config.cameras
+        ) | self._top_level_cam_keys & set(config.right_arm_config.cameras)
+        if _collisions:
+            raise ValueError(
+                f"Top-level camera names collide with per-arm camera names: {sorted(_collisions)}"
+            )
+        left_arm_cameras = {**config.left_arm_config.cameras, **config.cameras}
+
         left_arm_config = SOFollowerRobotConfig(
             id=f"{config.id}_left" if config.id else None,
             calibration_dir=config.calibration_dir,
@@ -46,7 +59,7 @@ class BiSOFollower(Robot):
             disable_torque_on_disconnect=config.left_arm_config.disable_torque_on_disconnect,
             max_relative_target=config.left_arm_config.max_relative_target,
             use_degrees=config.left_arm_config.use_degrees,
-            cameras=config.left_arm_config.cameras,
+            cameras=left_arm_cameras,
         )
 
         right_arm_config = SOFollowerRobotConfig(
@@ -77,13 +90,12 @@ class BiSOFollower(Robot):
 
     @property
     def _cameras_ft(self) -> dict[str, tuple]:
-        left_arm_cameras_ft = self.left_arm._cameras_ft
-        right_arm_cameras_ft = self.right_arm._cameras_ft
-
-        return {
-            **{f"left_{k}": v for k, v in left_arm_cameras_ft.items()},
-            **{f"right_{k}": v for k, v in right_arm_cameras_ft.items()},
-        }
+        out: dict[str, tuple] = {}
+        for k, v in self.left_arm._cameras_ft.items():
+            out[k if k in self._top_level_cam_keys else f"left_{k}"] = v
+        for k, v in self.right_arm._cameras_ft.items():
+            out[f"right_{k}"] = v
+        return out
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
@@ -93,42 +105,21 @@ class BiSOFollower(Robot):
     def action_features(self) -> dict[str, type]:
         return self._motors_ft
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     def setup_motors(self) -> None:
         self.left_arm.setup_motors()
         self.right_arm.setup_motors()
 
     @check_if_not_connected
     def get_observation(self) -> RobotObservation:
-        obs_dict = {}
+        obs_dict: RobotObservation = {}
 
-        # Add "left_" prefix
-        left_obs = self.left_arm.get_observation()
-        obs_dict.update({f"left_{key}": value for key, value in left_obs.items()})
+        # Add "left_" prefix to per-arm keys; keep top-level camera keys unprefixed.
+        for key, value in self.left_arm.get_observation().items():
+            obs_dict[key if key in self._top_level_cam_keys else f"left_{key}"] = value
 
         # Add "right_" prefix
-        right_obs = self.right_arm.get_observation()
-        obs_dict.update({f"right_{key}": value for key, value in right_obs.items()})
+        for key, value in self.right_arm.get_observation().items():
+            obs_dict[f"right_{key}"] = value
 
         return obs_dict
 
@@ -151,8 +142,3 @@ class BiSOFollower(Robot):
         prefixed_sent_action_right = {f"right_{key}": value for key, value in sent_action_right.items()}
 
         return {**prefixed_sent_action_left, **prefixed_sent_action_right}
-
-    @check_if_not_connected
-    def disconnect(self):
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/robots/bi_so_follower/config_bi_so_follower.py

@@ -14,7 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from dataclasses import dataclass
+from dataclasses import dataclass, field
+
+from lerobot.cameras import CameraConfig
 
 from ..config import RobotConfig
 from ..so_follower import SOFollowerConfig
@@ -27,3 +29,8 @@ class BiSOFollowerConfig(RobotConfig):
 
     left_arm_config: SOFollowerConfig
     right_arm_config: SOFollowerConfig
+
+    # Top-level cameras not attached to a specific side. Keys are kept as-is in
+    # observations (no `left_`/`right_` prefix). Per-arm cameras (declared on
+    # `{left,right}_arm_config.cameras`) are prefixed.
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)

src/lerobot/scripts/lerobot_calibrate.py

@@ -54,6 +54,7 @@ from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     homunculus,

src/lerobot/scripts/lerobot_find_joint_limits.py

@@ -57,6 +57,7 @@ from lerobot.robots import (  # noqa: F401
 from lerobot.teleoperators import (  # noqa: F401
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     gamepad,

src/lerobot/scripts/lerobot_record.py

@@ -137,6 +137,7 @@ from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     homunculus,

src/lerobot/scripts/lerobot_rollout.py

@@ -174,6 +174,7 @@ from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     homunculus,

src/lerobot/scripts/lerobot_setup_motors.py

@@ -41,6 +41,7 @@ from lerobot.robots import (  # noqa: F401
 )
 from lerobot.teleoperators import (  # noqa: F401
     TeleoperatorConfig,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     koch_leader,

src/lerobot/scripts/lerobot_teleoperate.py

@@ -89,6 +89,7 @@ from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
     TeleoperatorConfig,
     bi_openarm_leader,
+    bi_openarm_mini,
     bi_rebot_102_leader,
     bi_so_leader,
     gamepad,

src/lerobot/scripts/lerobot_train.py

@@ -45,8 +45,7 @@ from lerobot.common.train_utils import (
 from lerobot.common.wandb_utils import WandBLogger
 from lerobot.configs import parser
 from lerobot.configs.train import TrainPipelineConfig
-from lerobot.datasets import EpisodeAwareSampler, compute_sampler_state
-from lerobot.datasets.factory import make_train_eval_datasets
+from lerobot.datasets import EpisodeAwareSampler, compute_sampler_state, make_dataset
 from lerobot.envs import close_envs, make_env, make_env_pre_post_processors
 from lerobot.optim.factory import make_optimizer_and_scheduler
 from lerobot.policies import PreTrainedPolicy, make_policy, make_pre_post_processors
@@ -245,19 +244,19 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
     # LeRobotDataset skips its snapshot_download when try_load() succeeds, so no rank re-downloads.
     if is_main_process:
         logging.info("Creating dataset")
-        dataset, eval_dataset = make_train_eval_datasets(cfg)
+        dataset = make_dataset(cfg)
 
     accelerator.wait_for_everyone()
 
     # Other ranks read from the shared copy populated by the main process.
     if not is_main_process:
-        dataset, eval_dataset = make_train_eval_datasets(cfg)
+        dataset = make_dataset(cfg)
 
     # Create environment used for evaluating checkpoints during training on simulation data.
     # On real-world data, no need to create an environment as evaluations are done outside train.py,
     # using the eval.py instead, with gym_dora environment and dora-rs.
     eval_env = None
-    if cfg.env_eval_freq > 0 and cfg.env is not None and is_main_process:
+    if cfg.eval_freq > 0 and cfg.env is not None and is_main_process:
         logging.info("Creating env")
         eval_env = make_env(cfg.env, n_envs=cfg.eval.batch_size, use_async_envs=cfg.eval.use_async_envs)
 
@@ -435,8 +434,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                     f"Resuming data order at epoch {sampler_state['epoch']}, "
                     f"sample {sampler_state['start_index']}"
                 )
-        if dataset.reader._absolute_to_relative_idx is not None:
-            sampler.indices = [dataset.reader._absolute_to_relative_idx[i] for i in sampler.indices]
     else:
         shuffle = True
         sampler = None
@@ -458,31 +455,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         persistent_workers=cfg.persistent_workers and cfg.num_workers > 0,
     )
 
-    # Build eval dataloader if a held-out split exists
-    eval_dataloader = None
-    if eval_dataset is not None:
-        eval_ds = eval_dataset
-        if cfg.max_eval_samples > 0 and hasattr(eval_dataset, "hf_dataset"):
-            task_indices = eval_dataset.hf_dataset["task_index"]
-            unique_tasks = sorted(set(task_indices))
-            per_task = max(1, cfg.max_eval_samples // len(unique_tasks))
-            selected: list[int] = []
-            for t in unique_tasks:
-                frames = [i for i, ti in enumerate(task_indices) if ti == t][:per_task]
-                selected.extend(frames)
-            eval_ds = torch.utils.data.Subset(eval_dataset, selected)
-
-        eval_collate_fn = lerobot_collate_fn if dataset.meta.has_language_columns else None
-        eval_dataloader = torch.utils.data.DataLoader(
-            eval_ds,
-            batch_size=cfg.batch_size,
-            shuffle=False,
-            num_workers=cfg.num_workers,
-            pin_memory=device.type == "cuda",
-            drop_last=False,
-            collate_fn=eval_collate_fn,
-        )
-
     # Prepare everything with accelerator
     accelerator.wait_for_everyone()
     policy, optimizer, dataloader, lr_scheduler = accelerator.prepare(
@@ -562,8 +534,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
         train_tracker.step()
         is_log_step = cfg.log_freq > 0 and step % cfg.log_freq == 0
         is_saving_step = step % cfg.save_freq == 0 or step == cfg.steps
-        is_env_eval_step = cfg.env_eval_freq > 0 and step % cfg.env_eval_freq == 0
-        is_eval_step = cfg.eval_steps > 0 and eval_dataloader is not None and step % cfg.eval_steps == 0
+        is_eval_step = cfg.eval_freq > 0 and step % cfg.eval_freq == 0
 
         if is_log_step:
             # Collective reduce must run on every rank, before the main-process gate below.
@@ -586,27 +557,6 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
                     wandb_logger.log_dict(wandb_log_dict, step)
             train_tracker.reset_averages()
 
-        if is_eval_step:
-            policy.eval()
-            eval_loss_sum = 0.0
-            n_eval_batches = 0
-            with torch.no_grad(), accelerator.autocast():
-                for eval_batch in eval_dataloader:
-                    for cam_key in dataset.meta.camera_keys:
-                        if cam_key in eval_batch and eval_batch[cam_key].dtype == torch.uint8:
-                            eval_batch[cam_key] = eval_batch[cam_key].to(dtype=torch.float32) / 255.0
-                    eval_batch = preprocessor(eval_batch)
-                    loss, _ = policy.forward(eval_batch)
-                    eval_loss_sum += loss.item()
-                    n_eval_batches += 1
-            eval_loss = eval_loss_sum / max(n_eval_batches, 1)
-            policy.train()
-
-            if is_main_process:
-                logging.info(f"step {step}: eval_loss={eval_loss:.4f}")
-                if wandb_logger:
-                    wandb_logger.log_dict({"eval_loss": eval_loss}, step=step, mode="eval")
-
         if cfg.save_checkpoint and is_saving_step:
             if is_main_process:
                 logging.info(f"Checkpoint policy after step {step}")
@@ -629,7 +579,7 @@ def train(cfg: TrainPipelineConfig, accelerator: "Accelerator | None" = None):
 
             accelerator.wait_for_everyone()
 
-        if cfg.env and is_env_eval_step:
+        if cfg.env and is_eval_step:
             if is_main_process:
                 step_id = get_step_identifier(step, cfg.steps)
                 logging.info(f"Eval policy at step {step}")

src/lerobot/teleoperators/bi_openarm_leader/bi_openarm_leader.py

@@ -18,7 +18,8 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..openarm_leader import OpenArmLeader, OpenArmLeaderConfig
 from ..teleoperator import Teleoperator
@@ -27,7 +28,7 @@ from .config_bi_openarm_leader import BiOpenArmLeaderConfig
 logger = logging.getLogger(__name__)
 
 
-class BiOpenArmLeader(Teleoperator):
+class BiOpenArmLeader(BimanualMixin, Teleoperator):
     """
     Bimanual OpenArm Leader Arms
     """
@@ -86,27 +87,6 @@ class BiOpenArmLeader(Teleoperator):
     def feedback_features(self) -> dict[str, type]:
         return {}
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     def setup_motors(self) -> None:
         raise NotImplementedError(
             "Motor ID configuration is typically done via manufacturer tools for CAN motors."
@@ -129,8 +109,3 @@ class BiOpenArmLeader(Teleoperator):
     def send_feedback(self, feedback: dict[str, float]) -> None:
         # TODO: Implement force feedback
         raise NotImplementedError
-
-    @check_if_not_connected
-    def disconnect(self) -> None:
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/teleoperators/bi_openarm_leader/config_bi_openarm_leader.py

@@ -23,7 +23,7 @@ from ..openarm_leader import OpenArmLeaderConfigBase
 @TeleoperatorConfig.register_subclass("bi_openarm_leader")
 @dataclass
 class BiOpenArmLeaderConfig(TeleoperatorConfig):
-    """Configuration class for Bi OpenArm Follower robots."""
+    """Configuration class for Bi OpenArm Leader teleoperators."""
 
     left_arm_config: OpenArmLeaderConfigBase
     right_arm_config: OpenArmLeaderConfigBase

src/lerobot/teleoperators/bi_openarm_mini/__init__.py

@@ -0,0 +1,20 @@
+#!/usr/bin/env python
+
+# Copyright 2026 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 .bi_openarm_mini import BiOpenArmMini
+from .config_bi_openarm_mini import BiOpenArmMiniConfig
+
+__all__ = ["BiOpenArmMini", "BiOpenArmMiniConfig"]

src/lerobot/teleoperators/bi_openarm_mini/bi_openarm_mini.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+
+# Copyright 2026 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.
+
+import logging
+from functools import cached_property
+
+from lerobot.types import RobotAction
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
+
+from ..openarm_mini import OpenArmMini, OpenArmMiniConfig
+from ..teleoperator import Teleoperator
+from .config_bi_openarm_mini import BiOpenArmMiniConfig
+
+logger = logging.getLogger(__name__)
+
+
+class BiOpenArmMini(BimanualMixin, Teleoperator):
+    """Bimanual OpenArm Mini teleoperator.
+
+    Composes two single-arm :class:`OpenArmMini` instances. Action and feedback
+    keys of each arm are namespaced with a ``left_`` / ``right_`` prefix, so a
+    bimanual leader can teleoperate a bimanual OpenArm follower.
+    """
+
+    config_class = BiOpenArmMiniConfig
+    name = "bi_openarm_mini"
+
+    def __init__(self, config: BiOpenArmMiniConfig):
+        super().__init__(config)
+        self.config = config
+
+        # `side` is forced to match left/right regardless of what the user passed
+        # on the per-arm base config — the bimanual wrapper owns the side semantics.
+        left_arm_config = OpenArmMiniConfig(
+            id=f"{config.id}_left" if config.id else None,
+            calibration_dir=config.calibration_dir,
+            port=config.left_arm_config.port,
+            side="left",
+            use_degrees=config.left_arm_config.use_degrees,
+        )
+
+        right_arm_config = OpenArmMiniConfig(
+            id=f"{config.id}_right" if config.id else None,
+            calibration_dir=config.calibration_dir,
+            port=config.right_arm_config.port,
+            side="right",
+            use_degrees=config.right_arm_config.use_degrees,
+        )
+
+        self.left_arm = OpenArmMini(left_arm_config)
+        self.right_arm = OpenArmMini(right_arm_config)
+
+    @cached_property
+    def action_features(self) -> dict[str, type]:
+        return {
+            **{f"left_{k}": v for k, v in self.left_arm.action_features.items()},
+            **{f"right_{k}": v for k, v in self.right_arm.action_features.items()},
+        }
+
+    @cached_property
+    def feedback_features(self) -> dict[str, type]:
+        return {
+            **{f"left_{k}": v for k, v in self.left_arm.feedback_features.items()},
+            **{f"right_{k}": v for k, v in self.right_arm.feedback_features.items()},
+        }
+
+    def setup_motors(self) -> None:
+        self.left_arm.setup_motors()
+        self.right_arm.setup_motors()
+
+    @check_if_not_connected
+    def get_action(self) -> RobotAction:
+        action: RobotAction = {}
+        for k, v in self.left_arm.get_action().items():
+            action[f"left_{k}"] = v
+        for k, v in self.right_arm.get_action().items():
+            action[f"right_{k}"] = v
+        return action
+
+    @check_if_not_connected
+    def send_feedback(self, feedback: dict[str, float]) -> None:
+        left_fb = {k.removeprefix("left_"): v for k, v in feedback.items() if k.startswith("left_")}
+        right_fb = {k.removeprefix("right_"): v for k, v in feedback.items() if k.startswith("right_")}
+        if left_fb:
+            self.left_arm.send_feedback(left_fb)
+        if right_fb:
+            self.right_arm.send_feedback(right_fb)

src/lerobot/teleoperators/bi_openarm_mini/config_bi_openarm_mini.py

@@ -0,0 +1,29 @@
+#!/usr/bin/env python
+
+# Copyright 2026 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 dataclasses import dataclass
+
+from ..config import TeleoperatorConfig
+from ..openarm_mini import OpenArmMiniConfigBase
+
+
+@TeleoperatorConfig.register_subclass("bi_openarm_mini")
+@dataclass
+class BiOpenArmMiniConfig(TeleoperatorConfig):
+    """Configuration class for Bi OpenArm Mini teleoperators."""
+
+    left_arm_config: OpenArmMiniConfigBase
+    right_arm_config: OpenArmMiniConfigBase

src/lerobot/teleoperators/bi_rebot_102_leader/__init__.py

@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .bi_rebot_102_leader import BiRebotArm102Leader
-from .config_bi_rebot_102_leader import BiRebotArm102LeaderConfig
+from .bi_rebot_102_leader import BiRebot102Leader
+from .config_bi_rebot_102_leader import BiRebot102LeaderConfig
 
-__all__ = ["BiRebotArm102Leader", "BiRebotArm102LeaderConfig"]
+__all__ = ["BiRebot102Leader", "BiRebot102LeaderConfig"]

src/lerobot/teleoperators/bi_rebot_102_leader/bi_rebot_102_leader.py

@@ -18,16 +18,17 @@ import logging
 from functools import cached_property
 
 from lerobot.types import RobotAction
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..rebot_102_leader import RebotArm102Leader, RebotArm102LeaderTeleopConfig
 from ..teleoperator import Teleoperator
-from .config_bi_rebot_102_leader import BiRebotArm102LeaderConfig
+from .config_bi_rebot_102_leader import BiRebot102LeaderConfig
 
 logger = logging.getLogger(__name__)
 
 
-class BiRebotArm102Leader(Teleoperator):
+class BiRebot102Leader(BimanualMixin, Teleoperator):
     """Bimanual Seeed Studio StarArm102 / reBot Arm 102 leader.
 
     Composes two single-arm :class:`RebotArm102Leader` instances. Action keys of
@@ -35,10 +36,10 @@ class BiRebotArm102Leader(Teleoperator):
     leader can teleoperate a bimanual reBot B601 follower.
     """
 
-    config_class = BiRebotArm102LeaderConfig
+    config_class = BiRebot102LeaderConfig
     name = "bi_rebot_102_leader"
 
-    def __init__(self, config: BiRebotArm102LeaderConfig):
+    def __init__(self, config: BiRebot102LeaderConfig):
         super().__init__(config)
         self.config = config
 
@@ -76,27 +77,6 @@ class BiRebotArm102Leader(Teleoperator):
     def feedback_features(self) -> dict[str, type]:
         return {}
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     @check_if_not_connected
     def get_action(self) -> RobotAction:
         action_dict = {}
@@ -106,8 +86,3 @@ class BiRebotArm102Leader(Teleoperator):
 
     def send_feedback(self, feedback: dict[str, float]) -> None:
         raise NotImplementedError("Feedback is not implemented for the reBot Arm 102 leader.")
-
-    @check_if_not_connected
-    def disconnect(self) -> None:
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/teleoperators/bi_rebot_102_leader/config_bi_rebot_102_leader.py

@@ -22,7 +22,7 @@ from ..rebot_102_leader import RebotArm102LeaderConfig
 
 @TeleoperatorConfig.register_subclass("bi_rebot_102_leader")
 @dataclass
-class BiRebotArm102LeaderConfig(TeleoperatorConfig):
+class BiRebot102LeaderConfig(TeleoperatorConfig):
     """Configuration class for the bimanual reBot Arm 102 leader teleoperator."""
 
     left_arm_config: RebotArm102LeaderConfig

src/lerobot/teleoperators/bi_so_leader/bi_so_leader.py

@@ -17,7 +17,9 @@
 import logging
 from functools import cached_property
 
-from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+from lerobot.types import RobotAction
+from lerobot.utils.bimanual import BimanualMixin
+from lerobot.utils.decorators import check_if_not_connected
 
 from ..so_leader import SOLeader, SOLeaderTeleopConfig
 from ..teleoperator import Teleoperator
@@ -26,7 +28,7 @@ from .config_bi_so_leader import BiSOLeaderConfig
 logger = logging.getLogger(__name__)
 
 
-class BiSOLeader(Teleoperator):
+class BiSOLeader(BimanualMixin, Teleoperator):
     """
     [Bimanual SO Leader Arms](https://github.com/TheRobotStudio/SO-ARM100) designed by TheRobotStudio
     """
@@ -67,33 +69,12 @@ class BiSOLeader(Teleoperator):
     def feedback_features(self) -> dict[str, type]:
         return {}
 
-    @property
-    def is_connected(self) -> bool:
-        return self.left_arm.is_connected and self.right_arm.is_connected
-
-    @check_if_already_connected
-    def connect(self, calibrate: bool = True) -> None:
-        self.left_arm.connect(calibrate)
-        self.right_arm.connect(calibrate)
-
-    @property
-    def is_calibrated(self) -> bool:
-        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
-
-    def calibrate(self) -> None:
-        self.left_arm.calibrate()
-        self.right_arm.calibrate()
-
-    def configure(self) -> None:
-        self.left_arm.configure()
-        self.right_arm.configure()
-
     def setup_motors(self) -> None:
         self.left_arm.setup_motors()
         self.right_arm.setup_motors()
 
     @check_if_not_connected
-    def get_action(self) -> dict[str, float]:
+    def get_action(self) -> RobotAction:
         action_dict = {}
 
         # Add "left_" prefix
@@ -109,8 +90,3 @@ class BiSOLeader(Teleoperator):
     def send_feedback(self, feedback: dict[str, float]) -> None:
         # TODO: Implement force feedback
         raise NotImplementedError
-
-    @check_if_not_connected
-    def disconnect(self) -> None:
-        self.left_arm.disconnect()
-        self.right_arm.disconnect()

src/lerobot/teleoperators/openarm_mini/__init__.py

@@ -1,6 +1,6 @@
 #!/usr/bin/env python
 
-# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
+# Copyright 2026 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.
@@ -14,7 +14,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from .config_openarm_mini import OpenArmMiniConfig
+from .config_openarm_mini import OpenArmMiniConfig, OpenArmMiniConfigBase
 from .openarm_mini import OpenArmMini
 
-__all__ = ["OpenArmMini", "OpenArmMiniConfig"]
+__all__ = ["OpenArmMini", "OpenArmMiniConfig", "OpenArmMiniConfigBase"]

src/lerobot/teleoperators/openarm_mini/config_openarm_mini.py

@@ -19,12 +19,21 @@ from dataclasses import dataclass
 from ..config import TeleoperatorConfig
 
 
-@TeleoperatorConfig.register_subclass("openarm_mini")
 @dataclass
-class OpenArmMiniConfig(TeleoperatorConfig):
-    """Configuration for OpenArm Mini teleoperator with Feetech motors (dual arms)."""
+class OpenArmMiniConfigBase:
+    """Base configuration for the OpenArm Mini teleoperator (Feetech STS3215, 7DOF + gripper)."""
 
-    port_right: str = "/dev/ttyUSB0"
-    port_left: str = "/dev/ttyUSB1"
+    # Serial port for the Feetech bus (e.g., "/dev/ttyUSB0").
+    port: str
+
+    # Side of the arm: "left" or "right". Controls per-joint direction flips applied
+    # during readout. If `None`, no flipping is applied.
+    side: str | None = None
 
     use_degrees: bool = True
+
+
+@TeleoperatorConfig.register_subclass("openarm_mini")
+@dataclass
+class OpenArmMiniConfig(TeleoperatorConfig, OpenArmMiniConfigBase):
+    pass

src/lerobot/teleoperators/openarm_mini/openarm_mini.py

@@ -31,22 +31,22 @@ from .config_openarm_mini import OpenArmMiniConfig
 
 logger = logging.getLogger(__name__)
 
-# Motors whose direction is inverted during readout
-RIGHT_MOTORS_TO_FLIP = ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"]
-LEFT_MOTORS_TO_FLIP = ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"]
+# Per-side motor direction flips applied during readout.
+SIDE_MOTORS_TO_FLIP: dict[str, list[str]] = {
+    "left": ["joint_1", "joint_3", "joint_4", "joint_5", "joint_6", "joint_7"],
+    "right": ["joint_1", "joint_2", "joint_3", "joint_4", "joint_5", "joint_7"],
+}
 
-# Leader joint 6 maps to follower joint 7 and vice versa
+# Leader joint 6 ↔ follower joint 7 (symmetric — its own inverse).
 JOINT_REMAP = {"joint_6": "joint_7", "joint_7": "joint_6"}
-JOINT_REMAP_REVERSE = {"joint_7": "joint_6", "joint_6": "joint_7"}
 
 GRIPPER_TELEOP_TO_DEGREES = -0.65
 
 
 class OpenArmMini(Teleoperator):
-    """
-    OpenArm Mini Teleoperator with dual Feetech-based arms (8 motors per arm).
+    """OpenArm Mini single-arm teleoperator (Feetech STS3215, 7DOF + gripper).
 
-    Each arm has 7 joints plus a gripper, using Feetech STS3215 servos.
+    For the bimanual setup, see :class:`BiOpenArmMini` which composes two of these.
     """
 
     config_class = OpenArmMiniConfig
@@ -56,9 +56,12 @@ class OpenArmMini(Teleoperator):
         super().__init__(config)
         self.config = config
 
+        if config.side is not None and config.side not in SIDE_MOTORS_TO_FLIP:
+            raise ValueError(f"Invalid side '{config.side}'; expected 'left', 'right', or None.")
+        self._motors_to_flip: list[str] = SIDE_MOTORS_TO_FLIP.get(config.side, []) if config.side else []
+
         norm_mode_body = MotorNormMode.DEGREES
-
-        motors_right = {
+        motors = {
             "joint_1": Motor(1, "sts3215", norm_mode_body),
             "joint_2": Motor(2, "sts3215", norm_mode_body),
             "joint_3": Motor(3, "sts3215", norm_mode_body),
@@ -69,46 +72,15 @@ class OpenArmMini(Teleoperator):
             "gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
         }
 
-        motors_left = {
-            "joint_1": Motor(1, "sts3215", norm_mode_body),
-            "joint_2": Motor(2, "sts3215", norm_mode_body),
-            "joint_3": Motor(3, "sts3215", norm_mode_body),
-            "joint_4": Motor(4, "sts3215", norm_mode_body),
-            "joint_5": Motor(5, "sts3215", norm_mode_body),
-            "joint_6": Motor(6, "sts3215", norm_mode_body),
-            "joint_7": Motor(7, "sts3215", norm_mode_body),
-            "gripper": Motor(8, "sts3215", MotorNormMode.RANGE_0_100),
-        }
-
-        cal_right = {
-            k.replace("right_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("right_")
-        }
-        cal_left = {
-            k.replace("left_", ""): v for k, v in (self.calibration or {}).items() if k.startswith("left_")
-        }
-
-        self.bus_right = FeetechMotorsBus(
-            port=self.config.port_right,
-            motors=motors_right,
-            calibration=cal_right,
-        )
-
-        self.bus_left = FeetechMotorsBus(
-            port=self.config.port_left,
-            motors=motors_left,
-            calibration=cal_left,
+        self.bus = FeetechMotorsBus(
+            port=self.config.port,
+            motors=motors,
+            calibration=self.calibration,
         )
 
     @property
     def action_features(self) -> dict[str, type]:
-        # Right first, then left — matches the robot (BiOpenArmFollower) ordering
-        # and the dataset feature names recorded during data collection.
-        features: dict[str, type] = {}
-        for motor in self.bus_right.motors:
-            features[f"right_{motor}.pos"] = float
-        for motor in self.bus_left.motors:
-            features[f"left_{motor}.pos"] = float
-        return features
+        return {f"{motor}.pos": float for motor in self.bus.motors}
 
     @property
     def feedback_features(self) -> dict[str, type]:
@@ -116,14 +88,12 @@ class OpenArmMini(Teleoperator):
 
     @property
     def is_connected(self) -> bool:
-        return self.bus_right.is_connected and self.bus_left.is_connected
+        return self.bus.is_connected
 
     @check_if_already_connected
     def connect(self, calibrate: bool = True) -> None:
-        logger.info(f"Connecting right arm on {self.config.port_right}...")
-        self.bus_right.connect()
-        logger.info(f"Connecting left arm on {self.config.port_left}...")
-        self.bus_left.connect()
+        logger.info(f"Connecting arm on {self.config.port}...")
+        self.bus.connect()
 
         if calibrate:
             self.calibrate()
@@ -133,14 +103,14 @@ class OpenArmMini(Teleoperator):
 
     @property
     def is_calibrated(self) -> bool:
-        return self.bus_right.is_calibrated and self.bus_left.is_calibrated
+        return self.bus.is_calibrated
 
     def calibrate(self) -> None:
         """
-        Run calibration procedure for OpenArm Mini.
+        Run calibration procedure for a single OpenArm Mini arm.
 
         1. Disable torque
-        2. Ask user to position arms in hanging position with grippers closed
+        2. Ask user to position arm in hanging position with gripper closed
         3. Set this as zero position via half-turn homing
         4. Interactive gripper calibration (open/close positions)
         5. Save calibration
@@ -152,70 +122,51 @@ class OpenArmMini(Teleoperator):
             )
             if user_input.strip().lower() != "c":
                 logger.info(f"Using existing calibration for {self.id}")
-                cal_right = {
-                    k.replace("right_", ""): v for k, v in self.calibration.items() if k.startswith("right_")
-                }
-                cal_left = {
-                    k.replace("left_", ""): v for k, v in self.calibration.items() if k.startswith("left_")
-                }
-                self.bus_right.write_calibration(cal_right)
-                self.bus_left.write_calibration(cal_left)
+                self.bus.write_calibration(self.calibration)
                 return
 
         logger.info(f"\nRunning calibration for {self}")
 
-        self._calibrate_arm("right", self.bus_right)
-        self._calibrate_arm("left", self.bus_left)
+        self.bus.disable_torque()
 
-        self._save_calibration()
-        print(f"\nCalibration complete and saved to {self.calibration_fpath}")
+        logger.info("Setting Phase to 12 for all motors...")
+        for motor in self.bus.motors:
+            self.bus.write("Phase", motor, 12)
 
-    def _calibrate_arm(self, arm_name: str, bus: FeetechMotorsBus) -> None:
-        """Calibrate a single arm with Feetech motors."""
-        logger.info(f"\n=== Calibrating {arm_name.upper()} arm ===")
-
-        bus.disable_torque()
-
-        logger.info(f"Setting Phase to 12 for all motors in {arm_name.upper()} arm...")
-        for motor in bus.motors:
-            bus.write("Phase", motor, 12)
-
-        for motor in bus.motors:
-            bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+        for motor in self.bus.motors:
+            self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
 
         input(
-            f"\nCalibration: Zero Position ({arm_name.upper()} arm)\n"
+            "\nCalibration: Zero Position\n"
             "Position the arm in the following configuration:\n"
             "  - Arm hanging straight down\n"
             "  - Gripper closed\n"
             "Press ENTER when ready..."
         )
 
-        homing_offsets = bus.set_half_turn_homings()
-        logger.info(f"{arm_name.capitalize()} arm zero position set.")
+        homing_offsets = self.bus.set_half_turn_homings()
+        logger.info("Arm zero position set.")
 
-        print(f"\nSetting motor ranges for {arm_name.upper()} arm\n")
+        print("\nSetting motor ranges\n")
 
         if self.calibration is None:
             self.calibration = {}
 
-        motor_resolution = bus.model_resolution_table[list(bus.motors.values())[0].model]
+        motor_resolution = self.bus.model_resolution_table[list(self.bus.motors.values())[0].model]
         max_res = motor_resolution - 1
 
-        for motor_name, motor in bus.motors.items():
-            prefixed_name = f"{arm_name}_{motor_name}"
-
+        for motor_name, motor in self.bus.motors.items():
             if motor_name == "gripper":
                 input(
-                    f"\nGripper Calibration ({arm_name.upper()} arm)\n"
-                    f"Step 1: CLOSE the gripper fully\n"
-                    f"Press ENTER when gripper is closed..."
+                    "\nGripper Calibration\n"
+                    "Step 1: CLOSE the gripper fully\n"
+                    "Press ENTER when gripper is closed..."
                 )
-                closed_pos = bus.read("Present_Position", motor_name, normalize=False)
+                closed_pos = self.bus.read("Present_Position", motor_name, normalize=False)
                 logger.info(f"  Gripper closed position recorded: {closed_pos}")
 
                 input("\nStep 2: OPEN the gripper fully\nPress ENTER when gripper is fully open...")
-                open_pos = bus.read("Present_Position", motor_name, normalize=False)
+                open_pos = self.bus.read("Present_Position", motor_name, normalize=False)
                 logger.info(f"  Gripper open position recorded: {open_pos}")
 
                 if closed_pos < open_pos:
@@ -228,16 +179,16 @@ class OpenArmMini(Teleoperator):
                     drive_mode = 1
 
                 logger.info(
-                    f"  {prefixed_name}: range set to [{range_min}, {range_max}] "
+                    f"  {motor_name}: range set to [{range_min}, {range_max}] "
                     f"(0=closed, 100=open, drive_mode={drive_mode})"
                 )
             else:
                 range_min = 0
                 range_max = max_res
                 drive_mode = 0
-                logger.info(f"  {prefixed_name}: range set to [0, {max_res}] (full motor range)")
+                logger.info(f"  {motor_name}: range set to [0, {max_res}] (full motor range)")
 
-            self.calibration[prefixed_name] = MotorCalibration(
+            self.calibration[motor_name] = MotorCalibration(
                 id=motor.id,
                 drive_mode=drive_mode,
                 homing_offset=homing_offsets[motor_name],
@@ -245,108 +196,68 @@ class OpenArmMini(Teleoperator):
                 range_max=range_max,
             )
 
-        cal_for_bus = {
-            k.replace(f"{arm_name}_", ""): v
-            for k, v in self.calibration.items()
-            if k.startswith(f"{arm_name}_")
-        }
-        bus.write_calibration(cal_for_bus)
+        self.bus.write_calibration(self.calibration)
+        self._save_calibration()
+        print(f"\nCalibration complete and saved to {self.calibration_fpath}")
 
     def configure(self) -> None:
-        self.bus_right.disable_torque()
-        self.bus_right.configure_motors()
-        for motor in self.bus_right.motors:
-            self.bus_right.write("Operating_Mode", motor, OperatingMode.POSITION.value)
-
-        self.bus_left.disable_torque()
-        self.bus_left.configure_motors()
-        for motor in self.bus_left.motors:
-            self.bus_left.write("Operating_Mode", motor, OperatingMode.POSITION.value)
+        self.bus.disable_torque()
+        self.bus.configure_motors()
+        for motor in self.bus.motors:
+            self.bus.write("Operating_Mode", motor, OperatingMode.POSITION.value)
 
     def setup_motors(self) -> None:
-        print("\nSetting up RIGHT arm motors...")
-        for motor in reversed(self.bus_right.motors):
-            input(f"Connect the controller board to the RIGHT '{motor}' motor only and press enter.")
-            self.bus_right.setup_motor(motor)
-            print(f"RIGHT '{motor}' motor id set to {self.bus_right.motors[motor].id}")
-
-        print("\nSetting up LEFT arm motors...")
-        for motor in reversed(self.bus_left.motors):
-            input(f"Connect the controller board to the LEFT '{motor}' motor only and press enter.")
-            self.bus_left.setup_motor(motor)
-            print(f"LEFT '{motor}' motor id set to {self.bus_left.motors[motor].id}")
+        for motor in reversed(self.bus.motors):
+            input(f"Connect the controller board to the '{motor}' motor only and press enter.")
+            self.bus.setup_motor(motor)
+            print(f"'{motor}' motor id set to {self.bus.motors[motor].id}")
 
     @check_if_not_connected
     def get_action(self) -> RobotAction:
-        """Get current action from both arms (read positions from all motors)."""
+        """Get current action (read positions from all motors)."""
         start = time.perf_counter()
 
-        right_positions = self.bus_right.sync_read("Present_Position")
-        left_positions = self.bus_left.sync_read("Present_Position")
+        positions = self.bus.sync_read("Present_Position")
 
-        # Right first, then left — matches the robot (BiOpenArmFollower) ordering
-        # and the dataset feature names recorded during data collection.
         # Joint 6↔7 remap: leader joint_6 → follower joint_7 and vice versa.
+        # Per-side direction flip is applied based on the configured `side`.
         action: dict[str, Any] = {}
-        for motor, val in right_positions.items():
+        for motor, val in positions.items():
             target = JOINT_REMAP.get(motor, motor)
             if motor == "gripper":
                 # Convert gripper from teleop 0-100 to openarms degrees: 0→0°, 100→-65°
-                action[f"right_{target}.pos"] = val * GRIPPER_TELEOP_TO_DEGREES
+                action[f"{target}.pos"] = val * GRIPPER_TELEOP_TO_DEGREES
             else:
-                action[f"right_{target}.pos"] = -val if motor in RIGHT_MOTORS_TO_FLIP else val
-        for motor, val in left_positions.items():
-            target = JOINT_REMAP.get(motor, motor)
-            if motor == "gripper":
-                action[f"left_{target}.pos"] = val * GRIPPER_TELEOP_TO_DEGREES
-            else:
-                action[f"left_{target}.pos"] = -val if motor in LEFT_MOTORS_TO_FLIP else val
+                action[f"{target}.pos"] = -val if motor in self._motors_to_flip else val
 
         dt_ms = (time.perf_counter() - start) * 1e3
         logger.debug(f"{self} read action: {dt_ms:.1f}ms")
         return action
 
     def enable_torque(self) -> None:
-        """Enable torque on both arms for position control."""
-        self.bus_right.enable_torque()
-        self.bus_left.enable_torque()
+        self.bus.enable_torque()
 
     def disable_torque(self) -> None:
-        """Disable torque on both arms for free movement."""
-        self.bus_right.disable_torque()
-        self.bus_left.disable_torque()
+        self.bus.disable_torque()
 
     def write_goal_positions(self, positions: dict[str, float]) -> None:
         """Write goal positions to motors (inverse of get_action flip/gripper/remap logic)."""
-        right_goals: dict[str, float] = {}
-        left_goals: dict[str, float] = {}
-
+        goals: dict[str, float] = {}
         for key, val in positions.items():
             if not key.endswith(".pos"):
                 continue
-            motor_name = key.removesuffix(".pos")
-            if motor_name.startswith("right_"):
-                base = motor_name.removeprefix("right_")
-                # Reverse remap: follower joint_7 → leader joint_6 and vice versa
-                target = JOINT_REMAP_REVERSE.get(base, base)
-                if base == "gripper":
-                    # Convert robot degrees to teleop 0-100: 0°→0, -65°→100
-                    right_goals[target] = val / GRIPPER_TELEOP_TO_DEGREES
-                else:
-                    # Un-flip using the ORIGINAL motor name (target = leader motor)
-                    right_goals[target] = -val if target in RIGHT_MOTORS_TO_FLIP else val
-            elif motor_name.startswith("left_"):
-                base = motor_name.removeprefix("left_")
-                target = JOINT_REMAP_REVERSE.get(base, base)
-                if base == "gripper":
-                    left_goals[target] = val / GRIPPER_TELEOP_TO_DEGREES
-                else:
-                    left_goals[target] = -val if target in LEFT_MOTORS_TO_FLIP else val
+            base = key.removesuffix(".pos")
+            # JOINT_REMAP is symmetric (its own inverse).
+            target = JOINT_REMAP.get(base, base)
+            if base == "gripper":
+                # Convert robot degrees to teleop 0-100: 0°→0, -65°→100
+                goals[target] = val / GRIPPER_TELEOP_TO_DEGREES
+            else:
+                # Un-flip using the ORIGINAL motor name (target = leader motor)
+                goals[target] = -val if target in self._motors_to_flip else val
 
-        if right_goals:
-            self.bus_right.sync_write("Goal_Position", right_goals)
-        if left_goals:
-            self.bus_left.sync_write("Goal_Position", left_goals)
+        if goals:
+            self.bus.sync_write("Goal_Position", goals)
 
     @check_if_not_connected
     def send_feedback(self, feedback: dict[str, float]) -> None:
@@ -354,6 +265,5 @@ class OpenArmMini(Teleoperator):
 
     @check_if_not_connected
     def disconnect(self) -> None:
-        self.bus_right.disconnect()
-        self.bus_left.disconnect()
+        self.bus.disconnect()
         logger.info(f"{self} disconnected.")

src/lerobot/teleoperators/utils.py

@@ -99,14 +99,18 @@ def make_teleoperator_from_config(config: TeleoperatorConfig) -> "Teleoperator":
         from .openarm_mini import OpenArmMini
 
         return OpenArmMini(config)
+    elif config.type == "bi_openarm_mini":
+        from .bi_openarm_mini import BiOpenArmMini
+
+        return BiOpenArmMini(config)
     elif config.type == "rebot_102_leader":
         from .rebot_102_leader import RebotArm102Leader
 
         return RebotArm102Leader(config)
     elif config.type == "bi_rebot_102_leader":
-        from .bi_rebot_102_leader import BiRebotArm102Leader
+        from .bi_rebot_102_leader import BiRebot102Leader
 
-        return BiRebotArm102Leader(config)
+        return BiRebot102Leader(config)
     else:
         try:
             return cast("Teleoperator", make_device_from_device_class(config))

src/lerobot/utils/bimanual.py

@@ -0,0 +1,63 @@
+#!/usr/bin/env python
+
+# Copyright 2026 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 typing import Any
+
+from lerobot.utils.decorators import check_if_already_connected, check_if_not_connected
+
+
+class BimanualMixin:
+    """Lifecycle delegation for bimanual robots and teleoperators.
+
+    Concrete subclasses must populate ``self.left_arm`` and ``self.right_arm`` in
+    their own ``__init__``. They retain ownership of feature dicts and the
+    data-routing methods (``get_action`` / ``send_action`` / ``get_observation`` /
+    ``send_feedback``), which vary per-embodiment.
+
+    Inherit before the ``Robot`` / ``Teleoperator`` base so the mixin's methods
+    take precedence in the MRO::
+
+        class BiFooFollower(BimanualMixin, Robot): ...
+    """
+
+    left_arm: Any
+    right_arm: Any
+
+    @property
+    def is_connected(self) -> bool:
+        return self.left_arm.is_connected and self.right_arm.is_connected
+
+    @property
+    def is_calibrated(self) -> bool:
+        return self.left_arm.is_calibrated and self.right_arm.is_calibrated
+
+    @check_if_already_connected
+    def connect(self, calibrate: bool = True) -> None:
+        self.left_arm.connect(calibrate)
+        self.right_arm.connect(calibrate)
+
+    def calibrate(self) -> None:
+        self.left_arm.calibrate()
+        self.right_arm.calibrate()
+
+    def configure(self) -> None:
+        self.left_arm.configure()
+        self.right_arm.configure()
+
+    @check_if_not_connected
+    def disconnect(self) -> None:
+        self.left_arm.disconnect()
+        self.right_arm.disconnect()

tests/processor/test_pipeline.py

@@ -2370,14 +2370,32 @@ def test_aggregate_images_when_use_videos_false():
     out = aggregate_pipeline_dataset_features(
         pipeline=rp,
         initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
-        use_videos=False,  # expect "image" dtype
+        use_videos=False,  # images kept, stored as "image" dtype
         patterns=None,
     )
 
     key = f"{OBS_IMAGES}.back"
     key_front = f"{OBS_IMAGES}.front"
-    assert key not in out
-    assert key_front not in out
+    assert key in out
+    assert key_front in out
+    assert out[key]["dtype"] == "image"
+    assert out[key_front]["dtype"] == "image"
+    assert out[key]["shape"] == initial["back"]
+
+
+def test_aggregate_images_excluded():
+    rp = DataProcessorPipeline([AddObservationStateFeatures(add_front_image=True)])
+    initial = {"back": (480, 640, 3)}
+
+    out = aggregate_pipeline_dataset_features(
+        pipeline=rp,
+        initial_features={PipelineFeatureType.ACTION: {}, PipelineFeatureType.OBSERVATION: initial},
+        exclude_images=True,
+        patterns=None,
+    )
+
+    assert f"{OBS_IMAGES}.back" not in out
+    assert f"{OBS_IMAGES}.front" not in out
 
 
 def test_aggregate_images_when_use_videos_true():

tests/teleoperators/test_rebot_102_leader.py

@@ -18,7 +18,7 @@ from unittest.mock import MagicMock, patch
 
 import pytest
 
-from lerobot.teleoperators.bi_rebot_102_leader import BiRebotArm102Leader, BiRebotArm102LeaderConfig
+from lerobot.teleoperators.bi_rebot_102_leader import BiRebot102Leader, BiRebot102LeaderConfig
 from lerobot.teleoperators.rebot_102_leader import (
     RebotArm102Leader,
     RebotArm102LeaderConfig,
@@ -91,11 +91,11 @@ def test_send_feedback_not_implemented(leader):
 
 def test_bimanual_prefixes_features():
     with patch(f"{_MODULE}.require_package", lambda *a, **kw: None):
-        cfg = BiRebotArm102LeaderConfig(
+        cfg = BiRebot102LeaderConfig(
             left_arm_config=RebotArm102LeaderConfig(port="/dev/null0"),
             right_arm_config=RebotArm102LeaderConfig(port="/dev/null1"),
         )
-        teleop = BiRebotArm102Leader(cfg)
+        teleop = BiRebot102Leader(cfg)
     assert any(k.startswith("left_") for k in teleop.action_features)
     assert any(k.startswith("right_") for k in teleop.action_features)
     assert "left_gripper.pos" in teleop.action_features

tests/training/test_multi_gpu.py

@@ -134,7 +134,7 @@ class TestMultiGPUTraining:
                 f"--output_dir={output_dir}",
                 "--batch_size=4",
                 "--steps=10",
-                "--env_eval_freq=-1",
+                "--eval_freq=-1",
                 "--log_freq=5",
                 "--save_freq=10",
                 "--seed=42",
@@ -177,7 +177,7 @@ class TestMultiGPUTraining:
                 f"--output_dir={output_dir}",
                 "--batch_size=4",
                 "--steps=20",
-                "--env_eval_freq=-1",
+                "--eval_freq=-1",
                 "--log_freq=5",
                 "--save_freq=10",
                 "--seed=42",