Moved the step size from the teleop device to the robot; simplified the automatic takeover code

lerobot/common/robots/so100_follower_end_effector/config_so100_follower_end_effector.py

@@ -50,4 +50,12 @@ class SO100FollowerEndEffectorConfig(RobotConfig):
 
     max_gripper_pos: float = 50
 
+    end_effector_step_sizes: Dict[str, float] = field(
+        default_factory=lambda: {
+            "x": 0.02,
+            "y": 0.02,
+            "z": 0.02,
+        }
+    )
+
     urdf_path: str = "/Users/michel_aractingi/code/SO-ARM100/Simulation/SO101/so101_new_calib.urdf"

lerobot/common/robots/so100_follower_end_effector/so100_follower_end_effector.py

@@ -106,6 +106,7 @@ class SO100FollowerEndEffector(SO100Follower):
         Returns:
             The joint-space action that was sent to the motors
         """
+
         if not self.is_connected:
             raise DeviceNotConnectedError(f"{self} is not connected.")
 
@@ -113,7 +114,12 @@ class SO100FollowerEndEffector(SO100Follower):
         if isinstance(action, dict):
             if all(k in action for k in ["delta_x", "delta_y", "delta_z", "gripper"]):
                 action = np.array(
-                    [action["delta_x"], action["delta_y"], action["delta_z"], action["gripper"]],
+                    [
+                        action["delta_x"] * self.config.end_effector_step_sizes["x"],
+                        action["delta_y"] * self.config.end_effector_step_sizes["y"],
+                        action["delta_z"] * self.config.end_effector_step_sizes["z"],
+                        action["gripper"],
+                    ],
                     dtype=np.float32,
                 )
             else:

lerobot/common/teleoperators/gamepad/configuration_gamepad.py

@@ -25,8 +25,4 @@ class GamepadTeleopConfig(TeleoperatorConfig):
     # TODO(Steven): Consider setting in here the keys that we want to capture/listen
     mock: bool = False
 
-    x_step_size: float = 0.02
-    y_step_size: float = 0.02
-    z_step_size: float = 0.02
-
     use_gripper: bool = True

lerobot/common/teleoperators/gamepad/gamepad_utils.py

@@ -20,14 +20,14 @@ import time
 import numpy as np
 import torch
 
-from lerobot.common.robots.kinematics import RobotKinematics
+from lerobot.common.model.kinematics import RobotKinematics
 from lerobot.common.utils.robot_utils import busy_wait
 
 
 class InputController:
     """Base class for input controllers that generate motion deltas."""
 
-    def __init__(self, x_step_size=0.01, y_step_size=0.01, z_step_size=0.01):
+    def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0):
         """
         Initialize the controller.
 
@@ -102,7 +102,7 @@ class InputController:
 class KeyboardController(InputController):
     """Generate motion deltas from keyboard input."""
 
-    def __init__(self, x_step_size=0.01, y_step_size=0.01, z_step_size=0.01):
+    def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0):
         super().__init__(x_step_size, y_step_size, z_step_size)
         self.key_states = {
             "forward_x": False,
@@ -215,7 +215,7 @@ class KeyboardController(InputController):
 class GamepadController(InputController):
     """Generate motion deltas from gamepad input."""
 
-    def __init__(self, x_step_size=0.01, y_step_size=0.01, z_step_size=0.01, deadzone=0.1):
+    def __init__(self, x_step_size=1.0, y_step_size=1.0, z_step_size=1.0, deadzone=0.1):
         super().__init__(x_step_size, y_step_size, z_step_size)
         self.deadzone = deadzone
         self.joystick = None
@@ -330,9 +330,9 @@ class GamepadControllerHID(InputController):
 
     def __init__(
         self,
-        x_step_size=0.01,
-        y_step_size=0.01,
-        z_step_size=0.01,
+        x_step_size=1.0,
+        y_step_size=1.0,
+        z_step_size=1.0,
         deadzone=0.1,
         vendor_id=0x046D,
         product_id=0xC219,

lerobot/common/teleoperators/gamepad/teleop_gamepad.py

@@ -93,11 +93,7 @@ class GamepadTeleop(Teleoperator):
         else:
             from lerobot.scripts.server.end_effector_control_utils import GamepadController as Gamepad
 
-        self.gamepad = Gamepad(
-            x_step_size=self.config.x_step_size,
-            y_step_size=self.config.y_step_size,
-            z_step_size=self.config.z_step_size,
-        )
+        self.gamepad = Gamepad(x_step_size=1.0, y_step_size=1.0, z_step_size=1.0)
         self.gamepad.start()
 
     def calibrate(self) -> None:

lerobot/scripts/server/find_joint_limits.py

@@ -82,11 +82,10 @@ def find_joint_and_ee_bounds(cfg: FindJointLimitsConfig):
     while True:
         action = teleop.get_action()
         robot.send_action(action)
-        observation = robot.get_observation()
 
         joint_positions = robot.bus.sync_read("Present_Position")
-        joint_positions = np.array([joint_positions[key] for key in joint_positions.keys()])
-        ee_pos, ee_rot, _ = kinematics.forward_kinematics(joint_positions * np.pi / 180)
+        joint_positions = np.array([joint_positions[key] for key in joint_positions])
+        ee_pos, _, _ = kinematics.forward_kinematics(joint_positions * np.pi / 180)
         ee_list.append(ee_pos.copy())
         pos_list.append(joint_positions)
 

lerobot/scripts/server/gym_manipulator.py

@@ -37,6 +37,7 @@ from lerobot.common.robots import (  # noqa: F401
 from lerobot.common.teleoperators import (
     gamepad,  # noqa: F401
     make_teleoperator_from_config,
+    so101_leader,  # noqa: F401
 )
 from lerobot.common.teleoperators.gamepad.teleop_gamepad import GamepadTeleop
 from lerobot.common.utils.robot_utils import busy_wait
@@ -49,13 +50,13 @@ logging.basicConfig(level=logging.INFO)
 def reset_follower_position(robot_arm, target_position):
     current_position_dict = robot_arm.bus.sync_read("Present_Position")
     current_position = np.array(
-        [current_position_dict[name] for name in current_position_dict.keys()], dtype=np.float32
+        [current_position_dict[name] for name in current_position_dict], dtype=np.float32
     )
     trajectory = torch.from_numpy(
         np.linspace(current_position, target_position, 50)
     )  # NOTE: 30 is just an arbitrary number
     for pose in trajectory:
-        action_dict = {name: pose for name, pose in zip(current_position_dict.keys(), pose, strict=False)}
+        action_dict = dict(zip(current_position_dict, pose, strict=False))
         robot_arm.bus.sync_write("Goal_Position", action_dict)
         busy_wait(0.015)
 
@@ -200,6 +201,7 @@ class RobotEnv(gym.Env):
     def __init__(
         self,
         robot,
+        use_gripper: bool = False,
         display_cameras: bool = False,
     ):
         """
@@ -225,12 +227,14 @@ class RobotEnv(gym.Env):
         self.current_step = 0
         self.episode_data = None
 
-        self._joint_names = [f"{key}.pos" for key in self.robot.bus.motors.keys()]
+        self._joint_names = [f"{key}.pos" for key in self.robot.bus.motors]
         self._image_keys = self.robot.cameras.keys()
 
         # Read initial joint positions using the bus
         self.current_joint_positions = self._get_observation()["agent_pos"]
 
+        self.use_gripper = use_gripper
+
         self._setup_spaces()
 
     def _get_observation(self) -> np.ndarray:
@@ -277,10 +281,15 @@ class RobotEnv(gym.Env):
         self.observation_space = gym.spaces.Dict(observation_spaces)
 
         # Define the action space for joint positions along with setting an intervention flag.
-        action_dim = self.robot.action_features["shape"][0]
+        action_dim = 3
         bounds = {}
-        bounds["min"] = np.ones(action_dim) * -0.01
-        bounds["max"] = np.ones(action_dim) * 0.01
+        bounds["min"] = -np.ones(action_dim)
+        bounds["max"] = np.ones(action_dim)
+
+        if self.use_gripper:
+            action_dim += 1
+            bounds["min"] = np.concatenate([bounds["min"], [0]])
+            bounds["max"] = np.concatenate([bounds["max"], [2]])
 
         self.action_space = gym.spaces.Box(
             low=bounds["min"],
@@ -1174,7 +1183,14 @@ class BaseLeaderControlWrapper(gym.Wrapper):
     where the human can control a leader robot to guide the follower robot's movements.
     """
 
-    def __init__(self, env, teleop_device, use_geared_leader_arm: bool = False, use_gripper=False):
+    def __init__(
+        self,
+        env,
+        teleop_device,
+        end_effector_step_sizes,
+        use_geared_leader_arm: bool = False,
+        use_gripper=False,
+    ):
         """
         Initialize the base leader control wrapper.
 
@@ -1189,6 +1205,7 @@ class BaseLeaderControlWrapper(gym.Wrapper):
         self.robot_follower = env.unwrapped.robot
         self.use_geared_leader_arm = use_geared_leader_arm
         self.use_gripper: bool = use_gripper
+        self.end_effector_step_sizes = np.array(list(end_effector_step_sizes.values()))
 
         # Set up keyboard event tracking
         self._init_keyboard_events()
@@ -1197,8 +1214,6 @@ class BaseLeaderControlWrapper(gym.Wrapper):
         # Initialize robot control
         robot_type = getattr(env.unwrapped.robot.config, "robot_type", "so100")
         self.kinematics = RobotKinematics(robot_type)
-        self.prev_leader_ee = None
-        self.prev_leader_pos = None
         self.leader_torque_enabled = True
         self.prev_leader_gripper = None
 
@@ -1213,6 +1228,7 @@ class BaseLeaderControlWrapper(gym.Wrapper):
             self.robot_leader.bus.write("I_Coefficient", motor, 0)
             self.robot_leader.bus.write("D_Coefficient", motor, 16)
 
+        self.leader_tracking_error_queue = deque(maxlen=4)
         self._init_keyboard_listener()
 
     def _init_keyboard_events(self):
@@ -1295,24 +1311,18 @@ class BaseLeaderControlWrapper(gym.Wrapper):
         leader_pos_dict = self.robot_leader.bus.sync_read("Present_Position")
         follower_pos_dict = self.robot_follower.bus.sync_read("Present_Position")
 
-        leader_pos = np.array([leader_pos_dict[name] for name in leader_pos_dict.keys()], dtype=np.float32)
-        follower_pos = np.array(
-            [follower_pos_dict[name] for name in follower_pos_dict.keys()], dtype=np.float32
-        )
+        leader_pos = np.array([leader_pos_dict[name] for name in leader_pos_dict], dtype=np.float32)
+        follower_pos = np.array([follower_pos_dict[name] for name in follower_pos_dict], dtype=np.float32)
+
+        self.leader_tracking_error_queue.append(np.linalg.norm(follower_pos[:-1] - leader_pos[:-1]))
 
         # [:3, 3] Last column of the transformation matrix corresponds to the xyz translation
         leader_ee = self.kinematics.fk_gripper_tip(leader_pos)[:3, 3]
         follower_ee = self.kinematics.fk_gripper_tip(follower_pos)[:3, 3]
 
-        if self.prev_leader_ee is None:
-            self.prev_leader_ee = leader_ee
-
-        # NOTE: Using the leader's position delta for teleoperation is too noisy
-        # Instead, we move the follower to match the leader's absolute position,
-        # and record the leader's position changes as the intervention action
-        action = np.clip(leader_ee - follower_ee, -0.01, 0.01)
-        # action_intervention = leader_ee - self.prev_leader_ee
-        self.prev_leader_ee = leader_ee
+        action = np.clip(leader_ee - follower_ee, -self.end_effector_step_sizes, self.end_effector_step_sizes)
+        # Normalize the action to the range [-1, 1]
+        action = action / self.end_effector_step_sizes
 
         if self.use_gripper:
             if self.prev_leader_gripper is None:
@@ -1322,23 +1332,20 @@ class BaseLeaderControlWrapper(gym.Wrapper):
 
             # Get gripper action delta based on leader pose
             leader_gripper = leader_pos[-1]
-            # follower_gripper = follower_pos[-1]
             gripper_delta = leader_gripper - self.prev_leader_gripper
 
-            print(leader_gripper, self.prev_leader_gripper, gripper_delta)
             # Normalize by max angle and quantize to {0,1,2}
             normalized_delta = gripper_delta / self.robot_follower.config.max_gripper_pos
-            if normalized_delta >= 0.7:
+            if normalized_delta >= 0.3:
                 gripper_action = 2
-            elif normalized_delta <= 0.3:
+            elif normalized_delta <= 0.1:
                 gripper_action = 0
             else:
                 gripper_action = 1
 
             action = np.append(action, gripper_action)
-            # action_intervention = np.append(action_intervention, gripper_delta)
 
-        return action  # , action_intervention
+        return action
 
     def _handle_leader_teleoperation(self):
         """
@@ -1346,20 +1353,24 @@ class BaseLeaderControlWrapper(gym.Wrapper):
 
         This method synchronizes the leader robot position with the follower.
         """
+
+        prev_leader_pos_dict = self.robot_leader.bus.sync_read("Present_Position")
+        prev_leader_pos = np.array(
+            [prev_leader_pos_dict[name] for name in prev_leader_pos_dict], dtype=np.float32
+        )
+
         if not self.leader_torque_enabled:
             self.robot_leader.bus.sync_write("Torque_Enable", 1)
             self.leader_torque_enabled = True
 
         follower_pos_dict = self.robot_follower.bus.sync_read("Present_Position")
-        follower_pos = np.array(
-            [follower_pos_dict[name] for name in follower_pos_dict.keys()], dtype=np.float32
-        )
+        follower_pos = np.array([follower_pos_dict[name] for name in follower_pos_dict], dtype=np.float32)
 
-        # self.robot_leader.write("Goal_Position", follower_pos)
-        # self.robot_leader.send_action(follower_pos)
         goal_pos = {f"{motor}": follower_pos[i] for i, motor in enumerate(self.robot_leader.bus.motors)}
         self.robot_leader.bus.sync_write("Goal_Position", goal_pos)
 
+        self.leader_tracking_error_queue.append(np.linalg.norm(follower_pos[:-1] - prev_leader_pos[:-1]))
+
     def step(self, action):
         """
         Execute a step with possible human intervention.
@@ -1411,9 +1422,8 @@ class BaseLeaderControlWrapper(gym.Wrapper):
         Returns:
             The initial observation and info.
         """
-        self.prev_leader_ee = None
-        self.prev_leader_pos = None
         self.keyboard_events = dict.fromkeys(self.keyboard_events, False)
+        self.leader_tracking_error_queue.clear()
         return super().reset(**kwargs)
 
     def close(self):
@@ -1492,10 +1502,10 @@ class GearedLeaderAutomaticControlWrapper(BaseLeaderControlWrapper):
         self,
         env,
         teleop_device,
+        end_effector_step_sizes,
         use_gripper=False,
-        intervention_threshold=0.005,
-        release_threshold=5e-6,
-        queue_size=3,
+        intervention_threshold=10.0,
+        release_threshold=1e-2,
     ):
         """
         Initialize the automatic intervention wrapper.
@@ -1508,18 +1518,11 @@ class GearedLeaderAutomaticControlWrapper(BaseLeaderControlWrapper):
             release_threshold: Error threshold to release intervention.
             queue_size: Number of error measurements to track for smoothing.
         """
-        super().__init__(env, teleop_device, use_gripper=use_gripper)
+        super().__init__(env, teleop_device, end_effector_step_sizes, use_gripper=use_gripper)
 
         # Error tracking parameters
         self.intervention_threshold = intervention_threshold  # Threshold to trigger intervention
         self.release_threshold = release_threshold  # Threshold to release intervention
-        self.queue_size = queue_size  # Number of error measurements to keep
-
-        # Error tracking variables
-        self.ee_error_queue = deque(maxlen=self.queue_size)
-        self.ee_error_over_time_queue = deque(maxlen=self.queue_size)
-        self.previous_ee_error = 0.0
-        self.previous_ee_error_over_time_over_time = 0.0
         self.is_intervention_active = False
         self.start_time = time.perf_counter()
 
@@ -1534,59 +1537,32 @@ class GearedLeaderAutomaticControlWrapper(BaseLeaderControlWrapper):
         Returns:
             Boolean indicating whether intervention should be active.
         """
-        # Skip intervention logic for the first few steps to collect data
-        if time.perf_counter() - self.start_time < 1.0:  # Wait 1 second before enabling
+
+        # Condition for starting the intervention
+        # If the error in teleoperation is too high, that means the a user has grasped the leader robot and he wants to take over
+        if (
+            not self.is_intervention_active
+            and len(self.leader_tracking_error_queue) == self.leader_tracking_error_queue.maxlen
+            and np.var(list(self.leader_tracking_error_queue)[-2:]) > self.intervention_threshold
+        ):
+            self.is_intervention_active = True
+            self.leader_tracking_error_queue.clear()
+            log_say("Intervention started", play_sounds=True)
+            return True
+
+        # Track the error over time in leader_tracking_error_queue
+        # If the variance of the tracking error is too low, that means the user has let go of the leader robot and the intervention is over
+        if (
+            self.is_intervention_active
+            and len(self.leader_tracking_error_queue) == self.leader_tracking_error_queue.maxlen
+            and np.var(self.leader_tracking_error_queue) < self.release_threshold
+        ):
+            self.is_intervention_active = False
+            self.leader_tracking_error_queue.clear()
+            log_say("Intervention ended", play_sounds=True)
             return False
 
-        # Get current positions
-        leader_positions_dict = self.robot_leader.bus.sync_read("Present_Position")
-        follower_positions_dict = self.robot_follower.bus.sync_read("Present_Position")
-
-        leader_positions = np.array(
-            [leader_positions_dict[name] for name in leader_positions_dict.keys()], dtype=np.float32
-        )
-        follower_positions = np.array(
-            [follower_positions_dict[name] for name in follower_positions_dict.keys()], dtype=np.float32
-        )
-
-        leader_ee = self.kinematics.fk_gripper_tip(leader_positions)[:3, 3]
-        follower_ee = self.kinematics.fk_gripper_tip(follower_positions)[:3, 3]
-
-        ee_error = np.linalg.norm(leader_ee - follower_ee)
-        ee_error_over_time = np.abs(ee_error - self.previous_ee_error)
-
-        self.ee_error_queue.append(ee_error)
-        self.ee_error_over_time_queue.append(ee_error_over_time)
-
-        # Calculate averages if we have enough data
-        if len(self.ee_error_over_time_queue) >= self.queue_size:
-            if self.previous_ee_error_over_time_over_time is None:
-                # Initialize the previous error over time
-                self.previous_ee_error_over_time_over_time = self.ee_error_over_time_queue[-1]
-            else:
-                # Calculate the rate of change of error over time
-                avg_error_over_time = np.abs(
-                    self.ee_error_over_time_queue[-1] - self.previous_ee_error_over_time_over_time
-                )
-                self.previous_ee_error_over_time_over_time = self.ee_error_over_time_queue[-1]
-
-                self.intervention_threshold = 0.02
-
-                # Determine if intervention should start or stop based on the thresholds set in the constructor
-                if not self.is_intervention_active and avg_error_over_time > self.intervention_threshold:
-                    # Transition to intervention mode
-                    self.is_intervention_active = True
-                    logging.info(f"Starting automatic intervention: error rate {avg_error_over_time:.4f}")
-                    self.ee_error_over_time_queue.clear()
-                    self.previous_ee_error_over_time_over_time = None
-
-                elif self.is_intervention_active and avg_error_over_time < self.release_threshold:
-                    # End intervention mode
-                    self.is_intervention_active = False
-                    logging.info(f"Ending automatic intervention: error rate {avg_error_over_time:.4f}")
-                    self.ee_error_over_time_queue.clear()
-                    self.previous_ee_error_over_time_over_time = None
-
+        # If not change has happened that merits a change in the intervention state, return the current state
         return self.is_intervention_active
 
     def reset(self, **kwargs):
@@ -1600,11 +1576,6 @@ class GearedLeaderAutomaticControlWrapper(BaseLeaderControlWrapper):
             The initial observation and info.
         """
         self.is_intervention_active = False
-        self.start_time = time.perf_counter()
-        self.ee_error_queue.clear()
-        self.ee_error_over_time_queue.clear()
-        self.previous_ee_error = 0.0
-        self.old_avg_error_over_time = 0.0
         return super().reset(**kwargs)
 
 
@@ -1881,6 +1852,7 @@ def make_robot_env(cfg: EnvConfig) -> gym.Env:
     # Create base environment
     env = RobotEnv(
         robot=robot,
+        use_gripper=cfg.wrapper.use_gripper,
         display_cameras=cfg.wrapper.display_cameras if cfg.wrapper else False,
     )
 
@@ -1907,50 +1879,47 @@ def make_robot_env(cfg: EnvConfig) -> gym.Env:
     if reward_classifier is not None:
         env = RewardWrapper(env=env, reward_classifier=reward_classifier, device=cfg.device)
 
-    if cfg.wrapper:
-        env = TimeLimitWrapper(env=env, control_time_s=cfg.wrapper.control_time_s, fps=cfg.fps)
-        if cfg.wrapper.use_gripper:
-            # env = GripperActionWrapper(
-            #     env=env, quantization_threshold=cfg.wrapper.gripper_quantization_threshold
-            # )
-            if cfg.wrapper.gripper_penalty is not None:
-                env = GripperPenaltyWrapper(
-                    env=env,
-                    penalty=cfg.wrapper.gripper_penalty,
-                )
-
-        # Control mode specific wrappers
-        control_mode = cfg.wrapper.control_mode
-        if control_mode == "gamepad":
-            assert isinstance(teleop_device, GamepadTeleop), (
-                "teleop_device must be an instance of GamepadTeleop for gamepad control mode"
-            )
-            env = GamepadControlWrapper(
-                env=env,
-                teleop_device=teleop_device,
-                use_gripper=cfg.wrapper.use_gripper,
-            )
-        elif control_mode == "leader":
-            env = GearedLeaderControlWrapper(
-                env=env,
-                teleop_device=teleop_device,
-                use_gripper=cfg.wrapper.use_gripper,
-            )
-        elif control_mode == "leader_automatic":
-            env = GearedLeaderAutomaticControlWrapper(
-                env=env,
-                teleop_device=teleop_device,
-                use_gripper=cfg.wrapper.use_gripper,
-            )
-        else:
-            raise ValueError(f"Invalid control mode: {control_mode}")
-
-        env = ResetWrapper(
+    env = TimeLimitWrapper(env=env, control_time_s=cfg.wrapper.control_time_s, fps=cfg.fps)
+    if cfg.wrapper.use_gripper and cfg.wrapper.gripper_penalty is not None:
+        env = GripperPenaltyWrapper(
             env=env,
-            reset_pose=cfg.wrapper.fixed_reset_joint_positions,
-            reset_time_s=cfg.wrapper.reset_time_s,
+            penalty=cfg.wrapper.gripper_penalty,
         )
 
+    # Control mode specific wrappers
+    control_mode = cfg.wrapper.control_mode
+    if control_mode == "gamepad":
+        assert isinstance(teleop_device, GamepadTeleop), (
+            "teleop_device must be an instance of GamepadTeleop for gamepad control mode"
+        )
+        env = GamepadControlWrapper(
+            env=env,
+            teleop_device=teleop_device,
+            use_gripper=cfg.wrapper.use_gripper,
+        )
+    elif control_mode == "leader":
+        env = GearedLeaderControlWrapper(
+            env=env,
+            teleop_device=teleop_device,
+            end_effector_step_sizes=cfg.robot.end_effector_step_sizes,
+            use_gripper=cfg.wrapper.use_gripper,
+        )
+    elif control_mode == "leader_automatic":
+        env = GearedLeaderAutomaticControlWrapper(
+            env=env,
+            teleop_device=teleop_device,
+            end_effector_step_sizes=cfg.robot.end_effector_step_sizes,
+            use_gripper=cfg.wrapper.use_gripper,
+        )
+    else:
+        raise ValueError(f"Invalid control mode: {control_mode}")
+
+    env = ResetWrapper(
+        env=env,
+        reset_pose=cfg.wrapper.fixed_reset_joint_positions,
+        reset_time_s=cfg.wrapper.reset_time_s,
+    )
+
     env = BatchCompatibleWrapper(env=env)
     env = TorchActionWrapper(env=env, device=cfg.device)
 
@@ -2234,7 +2203,7 @@ def main(cfg: EnvConfig):
     while num_episode < 10:
         start_loop_s = time.perf_counter()
         # Sample a new random action from the robot's action space.
-        new_random_action = env.action_space.sample() * 0.0
+        new_random_action = env.action_space.sample()
         # Update the smoothed action using an exponential moving average.
         smoothed_action = alpha * new_random_action + (1 - alpha) * smoothed_action
 

lerobot/scripts/server/learner_server.py

@@ -91,7 +91,6 @@ from torch import nn
 from torch.multiprocessing import Queue
 from torch.optim.optimizer import Optimizer
 
-from lerobot.common.cameras import so100_follower_end_effector  # noqa: F401
 from lerobot.common.constants import (
     CHECKPOINTS_DIR,
     LAST_CHECKPOINT_LINK,