finish unitree camera integration

src/lerobot/cameras/utils.py

@@ -43,6 +43,11 @@ def make_cameras_from_configs(camera_configs: dict[str, CameraConfig]) -> dict[s
 
             cameras[key] = Reachy2Camera(cfg)
 
+        elif cfg.type == "zmq":
+            from .zmq import ZMQCamera
+
+            cameras[key] = ZMQCamera(cfg)
+
         else:
             try:
                 cameras[key] = cast(Camera, make_device_from_device_class(cfg))

src/lerobot/cameras/zmq/__init__.py

@@ -14,6 +14,3 @@
 
 from .camera_zmq import ZMQCamera
 from .configuration_zmq import ZMQCameraConfig
-
-__all__ = ["ZMQCamera", "ZMQCameraConfig"]
-

src/lerobot/robots/unitree_g1/config_unitree_g1.py

@@ -48,3 +48,5 @@ class UnitreeG1Config(RobotConfig):
     freeze_body = False
 
     gravity_compensation = True
+
+    cameras: dict[str, CameraConfig] = field(default_factory=dict)

src/lerobot/robots/unitree_g1/unitree_g1.py

@@ -72,9 +72,17 @@ class DataBuffer:
 #motor class for unitree? 
 #TODO: camera, sim 
 class UnitreeG1(Robot):
-    def __init__(self, config: UnitreeG1Config):
 
+    config_class = UnitreeG1Config
+    name = "unitree_g1"
+
+    def __init__(self, config: UnitreeG1Config):
+        super().__init__(config)
+        
         logger_mp.info("Initialize UnitreeG1...")
+
+        self.config = config
+        self.cameras = make_cameras_from_configs(config.cameras)
         self.q_target = np.zeros(14)
         self.tauff_target = np.zeros(14)
         self.motion_mode = config.motion_mode
@@ -315,14 +323,28 @@ class UnitreeG1(Robot):
         pass
 
     def connect(self, calibrate: bool = True) -> None:
-        pass
+        # Connect cameras
+        for cam in self.cameras.values():
+            cam.connect()
+        logger_mp.info(f"{self} connected with {len(self.cameras)} camera(s).")
 
     def disconnect(self):
-        pass
+        # Disconnect cameras
+        for cam in self.cameras.values():
+            cam.disconnect()
+        logger_mp.info(f"{self} disconnected.")
 
     def get_observation(self) -> dict[str, Any]:
         obs_array = self.get_current_dual_arm_q()
         obs_dict = {f"{G1_29_JointArmIndex(motor).name}.pos": val for motor, val in zip(G1_29_JointArmIndex, obs_array, strict=True)}
+        
+        # Capture images from cameras
+        for cam_key, cam in self.cameras.items():
+            start = time.perf_counter()
+            obs_dict[cam_key] = cam.async_read()
+            dt_ms = (time.perf_counter() - start) * 1e3
+            logger_mp.debug(f"{self} read {cam_key}: {dt_ms:.1f}ms")
+        
         return obs_dict
 
     @property
@@ -331,11 +353,21 @@ class UnitreeG1(Robot):
 
     @property
     def is_connected(self) -> bool:
-        pass
+        return all(cam.is_connected for cam in self.cameras.values())
+    
+    @property
+    def _motors_ft(self) -> dict[str, type]:
+        return {f"{G1_29_JointArmIndex(motor).name}.pos": float for motor in G1_29_JointArmIndex}
+    
+    @property
+    def _cameras_ft(self) -> dict[str, tuple]:
+        return {
+            cam: (self.config.cameras[cam].height, self.config.cameras[cam].width, 3) for cam in self.cameras
+        }
 
     @cached_property
     def observation_features(self) -> dict[str, type | tuple]:
-        return {f"{G1_29_JointArmIndex(motor).name}.pos": float for motor in G1_29_JointArmIndex}
+        return {**self._motors_ft, **self._cameras_ft}
 
     def send_action(self, action: dict[str, Any]) -> dict[str, Any]:
         #need a any to any teleoperator solution. i wanna teleoperate a horse with a shoe. action
@@ -343,16 +375,13 @@ class UnitreeG1(Robot):
         #also what would be fun is finding all sorts of robots and adding them to lerobot, see if people do the same.
         #then teleop them wiuth the glove hehe
         #then we get ALL THE DATA
-        print(self.calibration)
-        print(action)
         if self.is_calibrated:
             action = self.invert_calibration(action)
             #check if action is within bounds
             for key, value in action.items():
                 if value < self.calibration[key]["range_min"] or value > self.calibration[key]["range_max"]:
                     raise ValueError(f"Action value {value} for {key} is out of bounds, actions are not normalized")
-        print(action)
+
-        exit()
         if self.freeze_body:
             arm_joint_indices = set(range(15, 29))  # 15–28 are arms
             for jid in G1_29_JointIndex:

src/lerobot/scripts/lerobot_record.py

@@ -70,6 +70,7 @@ from lerobot.cameras import (  # noqa: F401
 )
 from lerobot.cameras.opencv.configuration_opencv import OpenCVCameraConfig  # noqa: F401
 from lerobot.cameras.realsense.configuration_realsense import RealSenseCameraConfig  # noqa: F401
+from lerobot.cameras.zmq.configuration_zmq import ZMQCameraConfig  # noqa: F401
 from lerobot.configs import parser
 from lerobot.configs.policies import PreTrainedConfig
 from lerobot.datasets.image_writer import safe_stop_image_writer
@@ -98,6 +99,7 @@ from lerobot.robots import (  # noqa: F401
     make_robot_from_config,
     so100_follower,
     so101_follower,
+    unitree_g1,
 )
 from lerobot.teleoperators import (  # noqa: F401
     Teleoperator,
@@ -196,7 +198,7 @@ class RecordConfig:
             self.policy.pretrained_path = policy_path
 
         if self.teleop is None and self.policy is None:
-            raise ValueError("Choose a policy, a teleoperator or both to control the robot")
+            logging.warning("No teleop or policy provided: running in idle (neutral actions) mode.")
 
     @classmethod
     def __get_path_fields__(cls) -> list[str]:
@@ -301,6 +303,9 @@ def record_loop(
         # Applies a pipeline to the raw robot observation, default is IdentityProcessor
         obs_processed = robot_observation_processor(obs)
 
+        act_processed_policy = None
+        act_processed_teleop = None
+
         if policy is not None or dataset is not None:
             observation_frame = build_dataset_frame(dataset.features, obs_processed, prefix=OBS_STR)
 
@@ -332,6 +337,11 @@ def record_loop(
             base_action = robot._from_keyboard_to_base_action(keyboard_action)
             act = {**arm_action, **base_action} if len(base_action) > 0 else arm_action
             act_processed_teleop = teleop_action_processor((act, obs))
+        elif policy is None and teleop is None:
+            # ⬅️ Neutral/idle actions: record without policy or teleop
+            # Build a zero action in the robot's action space
+            neutral = {k: 0.0 for k in robot.action_features.keys()}
+            act_processed_teleop = teleop_action_processor((neutral, obs))
         else:
             logging.info(
                 "No policy or teleoperator provided, skipping action generation."