diff --git a/lerobot/common/teleoperators/homonculus/__init__.py b/lerobot/common/teleoperators/homonculus/__init__.py new file mode 100644 index 00000000..2749104d --- /dev/null +++ b/lerobot/common/teleoperators/homonculus/__init__.py @@ -0,0 +1,3 @@ +from .config_homonculus import HomonculusArmConfig, HomonculusGloveConfig +from .homonculus_arm import HomonculusArm +from .homonculus_glove import HomonculusGlove diff --git a/lerobot/common/teleoperators/homonculus/config_homonculus.py b/lerobot/common/teleoperators/homonculus/config_homonculus.py new file mode 100644 index 00000000..55c0e1ed --- /dev/null +++ b/lerobot/common/teleoperators/homonculus/config_homonculus.py @@ -0,0 +1,35 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from dataclasses import dataclass + +from ..config import TeleoperatorConfig + + +@TeleoperatorConfig.register_subclass("homonculus_glove") +@dataclass +class HomonculusGloveConfig(TeleoperatorConfig): + port: str # Port to connect to the glove + baud_rate: int = 115_200 + buffer_size: int = 10 # Number of past values to keep in memory + + +@TeleoperatorConfig.register_subclass("homonculus_arm") +@dataclass +class HomonculusArmConfig(TeleoperatorConfig): + port: str # Port to connect to the arm + baud_rate: int = 115_200 + buffer_size: int = 10 # Number of past values to keep in memory diff --git a/lerobot/common/teleoperators/homonculus/homonculus_arm.py b/lerobot/common/teleoperators/homonculus/homonculus_arm.py new file mode 100644 index 00000000..a75b5924 --- /dev/null +++ b/lerobot/common/teleoperators/homonculus/homonculus_arm.py @@ -0,0 +1,419 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import pickle +import threading +from collections import deque +from enum import Enum + +import numpy as np +import serial + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError + +from ..teleoperator import Teleoperator +from .config_homonculus import HomonculusArmConfig + +logger = logging.getLogger(__name__) + +LOWER_BOUND_LINEAR = -100 +UPPER_BOUND_LINEAR = 200 + + +class CalibrationMode(Enum): + # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180] + DEGREE = 0 + # Joints with linear motions (like gripper of Aloha) are expressed in nominal range of [0, 100] + LINEAR = 1 + + +class HomonculusArm(Teleoperator): + config_class = HomonculusArmConfig + name = "homonculus_arml" + + def __init__(self, config: HomonculusArmConfig): + self.config = config + self.serial = serial.Serial(config.port, config.baud_rate, timeout=1) + self.buffer_size = config.buffer_size + + self.joints = [ + "wrist_roll", + "wrist_pitch", + "wrist_yaw", + "elbow_flex", + "shoulder_roll", + "shoulder_yaw", + "shoulder_pitch", + ] + # Initialize a buffer (deque) for each joint + self.joints_buffer = {joint: deque(maxlen=self.buffer_size) for joint in self.joints} + + # Last read dictionary + self.last_d = {joint: 100 for joint in self.joints} + + # For adaptive EMA, we store a "previous smoothed" state per joint + self.adaptive_ema_state = {joint: None for joint in self.joints} + self.kalman_state = {joint: {"x": None, "P": None} for joint in self.joints} + + self.calibration = None + self.thread = threading.Thread(target=self.async_read, daemon=True) + + @property + def action_feature(self) -> dict: + return { + "dtype": "float32", + "shape": (len(self.joints),), + "names": {"motors": self.joints}, + } + + @property + def feedback_feature(self) -> dict: + return {} + + @property + def is_connected(self) -> bool: + return self.thread.is_alive() and self.serial.is_open + + def connect(self) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.serial.open() + self.thread.start() + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + raise NotImplementedError # TODO + + def calibrate(self) -> None: + raise NotImplementedError # TODO + + def configure(self) -> None: + raise NotImplementedError # TODO + + def get_action(self) -> dict[str, float]: + raise NotImplementedError # TODO + + def send_feedback(self, feedback: dict[str, float]) -> None: + raise NotImplementedError + + def disconnect(self) -> None: + if not self.is_connected: + DeviceNotConnectedError(f"{self} is not connected.") + + self.thread.join() + self.serial.close() + logger.info(f"{self} disconnected.") + + ### WIP below ### + + @property + def joint_names(self): + return list(self.last_d.keys()) + + def read(self, motor_names: list[str] | None = None): + """ + Return the most recent (single) values from self.last_d, + optionally applying calibration. + """ + if motor_names is None: + motor_names = self.joint_names + + # Get raw (last) values + values = np.array([self.last_d[k] for k in motor_names]) + + # print(motor_names) + print(values) + + # Apply calibration if available + if self.calibration is not None: + values = self.apply_calibration(values, motor_names) + print(values) + return values + + def read_running_average(self, motor_names: list[str] | None = None, linearize=False): + """ + Return the AVERAGE of the most recent self.buffer_size (or fewer, if not enough data) readings + for each joint, optionally applying calibration. + """ + if motor_names is None: + motor_names = self.joint_names + + # Gather averaged readings from buffers + smoothed_vals = [] + for name in motor_names: + buf = self.joint_buffer[name] + if len(buf) == 0: + # If no data has been read yet, fall back to last_d + smoothed_vals.append(self.last_d[name]) + else: + # Otherwise, average over the existing buffer + smoothed_vals.append(np.mean(buf)) + + smoothed_vals = np.array(smoothed_vals, dtype=np.float32) + + # Apply calibration if available + if self.calibration is not None: + if False: + for i, joint_name in enumerate(motor_names): + # Re-use the same raw_min / raw_max from the calibration + calib_idx = self.calibration["motor_names"].index(joint_name) + min_reading = self.calibration["start_pos"][calib_idx] + max_reading = self.calibration["end_pos"][calib_idx] + + b_value = smoothed_vals[i] + print(joint_name) + if joint_name == "elbow_flex": + print("elbow") + try: + smoothed_vals[i] = int( + min_reading + + (max_reading - min_reading) + * np.arcsin((b_value - min_reading) / (max_reading - min_reading)) + / (np.pi / 2) + ) + except Exception as e: + print("not working") + print(smoothed_vals) + print("not working") + smoothed_vals = self.apply_calibration(smoothed_vals, motor_names) + return smoothed_vals + + def read_kalman_filter( + self, Q: float = 1.0, R: float = 100.0, motor_names: list[str] | None = None + ) -> np.ndarray: + """ + Return a Kalman-filtered reading for each requested joint. + + We store a separate Kalman filter (x, P) per joint. For each new measurement Z: + 1) Predict: + x_pred = x (assuming no motion model) + P_pred = P + Q + 2) Update: + K = P_pred / (P_pred + R) + x = x_pred + K * (Z - x_pred) + P = (1 - K) * P_pred + + :param Q: Process noise. Larger Q means the estimate can change more freely. + :param R: Measurement noise. Larger R means we trust our sensor less. + :param motor_names: If not specified, all joints are filtered. + :return: Kalman-filtered positions as a numpy array. + """ + if motor_names is None: + motor_names = self.joint_names + + current_vals = np.array([self.last_d[name] for name in motor_names], dtype=np.float32) + filtered_vals = np.zeros_like(current_vals) + + for i, name in enumerate(motor_names): + # Retrieve the filter state for this joint + x = self.kalman_state[name]["x"] + P = self.kalman_state[name]["P"] + Z = current_vals[i] + + # If this is the first reading, initialize + if x is None or P is None: + x = Z + P = 1.0 # or some large initial uncertainty + + # 1) Predict step + x_pred = x # no velocity model, so x_pred = x + P_pred = P + Q + + # 2) Update step + K = P_pred / (P_pred + R) # Kalman gain + x_new = x_pred + K * (Z - x_pred) # new state estimate + P_new = (1 - K) * P_pred # new covariance + + # Save back + self.kalman_state[name]["x"] = x_new + self.kalman_state[name]["P"] = P_new + + filtered_vals[i] = x_new + + if self.calibration is not None: + filtered_vals = self.apply_calibration(filtered_vals, motor_names) + + return filtered_vals + + def async_read(self): + """ + Continuously read from the serial buffer in its own thread, + store into `self.last_d` and also append to the rolling buffer (joint_buffer). + """ + while True: + if self.serial.in_waiting > 0: + self.serial.flush() + vals = self.serial.readline().decode("utf-8").strip() + vals = vals.split(" ") + + if len(vals) != 7: + continue + try: + vals = [int(val) for val in vals] # remove last digit + except ValueError: + self.serial.flush() + vals = self.serial.readline().decode("utf-8").strip() + vals = vals.split(" ") + vals = [int(val) for val in vals] + d = { + "wrist_roll": vals[0], + "wrist_yaw": vals[1], + "wrist_pitch": vals[2], + "elbow_flex": vals[3], + "shoulder_roll": vals[4], + "shoulder_yaw": vals[5], + "shoulder_pitch": vals[6], + } + + # Update the last_d dictionary + self.last_d = d + + # Also push these new values into the rolling buffers + for joint_name, joint_val in d.items(): + self.joint_buffer[joint_name].append(joint_val) + + # Optional: short sleep to avoid busy-loop + # time.sleep(0.001) + + def run_calibration(self, robot): + robot.arm_bus.write("Acceleration", 50) + n_joints = len(self.joint_names) + + max_open_all = np.zeros(n_joints, dtype=np.float32) + min_open_all = np.zeros(n_joints, dtype=np.float32) + max_closed_all = np.zeros(n_joints, dtype=np.float32) + min_closed_all = np.zeros(n_joints, dtype=np.float32) + + for i, jname in enumerate(self.joint_names): + print(f"\n--- Calibrating joint '{jname}' ---") + + joint_idx = robot.arm_calib_dict["motor_names"].index(jname) + open_val = robot.arm_calib_dict["start_pos"][joint_idx] + print(f"Commanding {jname} to OPEN position {open_val}...") + robot.arm_bus.write("Goal_Position", [open_val], [jname]) + + input("Physically verify or adjust the joint. Press Enter when ready to capture...") + + open_pos_list = [] + for _ in range(100): + all_joints_vals = self.read() # read entire arm + open_pos_list.append(all_joints_vals[i]) # store only this joint + time.sleep(0.01) + + # Convert to numpy and track min/max + open_array = np.array(open_pos_list, dtype=np.float32) + max_open_all[i] = open_array.max() + min_open_all[i] = open_array.min() + closed_val = robot.arm_calib_dict["end_pos"][joint_idx] + if jname == "elbow_flex": + closed_val = closed_val - 700 + closed_val = robot.arm_calib_dict["end_pos"][joint_idx] + print(f"Commanding {jname} to CLOSED position {closed_val}...") + robot.arm_bus.write("Goal_Position", [closed_val], [jname]) + + input("Physically verify or adjust the joint. Press Enter when ready to capture...") + + closed_pos_list = [] + for _ in range(100): + all_joints_vals = self.read() + closed_pos_list.append(all_joints_vals[i]) + time.sleep(0.01) + + closed_array = np.array(closed_pos_list, dtype=np.float32) + # Some thresholding for closed positions + # closed_array[closed_array < 1000] = 60000 + + max_closed_all[i] = closed_array.max() + min_closed_all[i] = closed_array.min() + + robot.arm_bus.write("Goal_Position", [int((closed_val + open_val) / 2)], [jname]) + + open_pos = np.maximum(max_open_all, max_closed_all) + closed_pos = np.minimum(min_open_all, min_closed_all) + + for i, jname in enumerate(self.joint_names): + if jname not in ["wrist_pitch", "shoulder_pitch"]: + # Swap open/closed for these joints + tmp_pos = open_pos[i] + open_pos[i] = closed_pos[i] + closed_pos[i] = tmp_pos + + # Debug prints + print("\nFinal open/closed arrays after any swaps/inversions:") + print(f"open_pos={open_pos}") + print(f"closed_pos={closed_pos}") + + homing_offset = [0] * n_joints + drive_mode = [0] * n_joints + calib_modes = [CalibrationMode.LINEAR.name] * n_joints + + calib_dict = { + "homing_offset": homing_offset, + "drive_mode": drive_mode, + "start_pos": open_pos, + "end_pos": closed_pos, + "calib_mode": calib_modes, + "motor_names": self.joint_names, + } + file_path = "examples/hopejr/settings/arm_calib.pkl" + + if not os.path.exists(file_path): + with open(file_path, "wb") as f: + pickle.dump(calib_dict, f) + print(f"Dictionary saved to {file_path}") + + self.set_calibration(calib_dict) + + def set_calibration(self, calibration: dict[str, list]): + self.calibration = calibration + + def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None): + """ + Example calibration that linearly maps [start_pos, end_pos] to [0,100]. + Extend or modify for your needs. + """ + if motor_names is None: + motor_names = self.joint_names + + values = values.astype(np.float32) + + for i, name in enumerate(motor_names): + calib_idx = self.calibration["motor_names"].index(name) + calib_mode = self.calibration["calib_mode"][calib_idx] + + if CalibrationMode[calib_mode] == CalibrationMode.LINEAR: + start_pos = self.calibration["start_pos"][calib_idx] + end_pos = self.calibration["end_pos"][calib_idx] + + # Rescale the present position to [0, 100] + values[i] = (values[i] - start_pos) / (end_pos - start_pos) * 100 + + # Check boundaries + if (values[i] < LOWER_BOUND_LINEAR) or (values[i] > UPPER_BOUND_LINEAR): + # If you want to handle out-of-range differently: + # raise JointOutOfRangeError(msg) + msg = ( + f"Wrong motor position range detected for {name}. " + f"Value = {values[i]} %, expected within [{LOWER_BOUND_LINEAR}, {UPPER_BOUND_LINEAR}]" + ) + print(msg) + + return values diff --git a/lerobot/common/teleoperators/homonculus/homonculus_glove.py b/lerobot/common/teleoperators/homonculus/homonculus_glove.py new file mode 100644 index 00000000..2ba2f712 --- /dev/null +++ b/lerobot/common/teleoperators/homonculus/homonculus_glove.py @@ -0,0 +1,348 @@ +#!/usr/bin/env python + +# Copyright 2025 The HuggingFace Inc. team. All rights reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging +import os +import pickle +import threading +from collections import deque +from enum import Enum + +import numpy as np +import serial + +from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError + +from ..teleoperator import Teleoperator +from .config_homonculus import HomonculusGloveConfig + +logger = logging.getLogger(__name__) + +LOWER_BOUND_LINEAR = -100 +UPPER_BOUND_LINEAR = 200 + + +class CalibrationMode(Enum): + # Joints with rotational motions are expressed in degrees in nominal range of [-180, 180] + DEGREE = 0 + # Joints with linear motions (like gripper of Aloha) are expressed in nominal range of [0, 100] + LINEAR = 1 + + +class HomonculusGlove(Teleoperator): + config_class = HomonculusGloveConfig + name = "homonculus_glove" + + def __init__(self, config: HomonculusGloveConfig): + self.config = config + self.serial = serial.Serial(config.port, config.baud_rate, timeout=1) + self.buffer_size = config.buffer_size + + self.joints = [ + "thumb_0", + "thumb_1", + "thumb_2", + "thumb_3", + "index_0", + "index_1", + "index_2", + "middle_0", + "middle_1", + "middle_2", + "ring_0", + "ring_1", + "ring_2", + "pinky_0", + "pinky_1", + "pinky_2", + "battery_voltage", # TODO(aliberts): Should this be in joints? + ] + # Initialize a buffer (deque) for each joint + self.joints_buffer = {joint: deque(maxlen=self.buffer_size) for joint in self.joints} + # Last read dictionary + self.last_d = {joint: 100 for joint in self.joints} + + self.calibration = None + self.thread = threading.Thread(target=self.async_read, daemon=True) + + @property + def action_feature(self) -> dict: + return { + "dtype": "float32", + "shape": (len(self.joints),), + "names": {"motors": self.joints}, + } + + @property + def feedback_feature(self) -> dict: + return {} + + @property + def is_connected(self) -> bool: + return self.thread.is_alive() and self.serial.is_open + + def connect(self) -> None: + if self.is_connected: + raise DeviceAlreadyConnectedError(f"{self} already connected") + + self.serial.open() + self.thread.start() + self.configure() + logger.info(f"{self} connected.") + + @property + def is_calibrated(self) -> bool: + raise NotImplementedError # TODO + + def calibrate(self) -> None: + raise NotImplementedError # TODO + + def configure(self) -> None: + raise NotImplementedError # TODO + + def get_action(self) -> dict[str, float]: + raise NotImplementedError # TODO + + def send_feedback(self, feedback: dict[str, float]) -> None: + raise NotImplementedError + + def disconnect(self) -> None: + if not self.is_connected: + DeviceNotConnectedError(f"{self} is not connected.") + + self.thread.join() + self.serial.close() + logger.info(f"{self} disconnected.") + + ### WIP below ### + + @property + def joint_names(self): + return list(self.last_d.keys()) + + def read(self, motor_names: list[str] | None = None): + """ + Return the most recent (single) values from self.last_d, + optionally applying calibration. + """ + if motor_names is None: + motor_names = self.joint_names + + # Get raw (last) values + values = np.array([self.last_d[k] for k in motor_names]) + + print(values) + + # Apply calibration if available + if self.calibration is not None: + values = self.apply_calibration(values, motor_names) + print(values) + return values + + def read_running_average(self, motor_names: list[str] | None = None, linearize=False): + """ + Return the AVERAGE of the most recent self.buffer_size (or fewer, if not enough data) readings + for each joint, optionally applying calibration. + """ + if motor_names is None: + motor_names = self.joint_names + + # Gather averaged readings from buffers + smoothed_vals = [] + for name in motor_names: + buf = self.joint_buffer[name] + if len(buf) == 0: + # If no data has been read yet, fall back to last_d + smoothed_vals.append(self.last_d[name]) + else: + # Otherwise, average over the existing buffer + smoothed_vals.append(np.mean(buf)) + + smoothed_vals = np.array(smoothed_vals, dtype=np.float32) + + # Apply calibration if available + if self.calibration is not None: + smoothed_vals = self.apply_calibration(smoothed_vals, motor_names) + + return smoothed_vals + + def async_read(self): + """ + Continuously read from the serial buffer in its own thread, + store into `self.last_d` and also append to the rolling buffer (joint_buffer). + """ + while True: + if self.serial.in_waiting > 0: + self.serial.flush() + vals = self.serial.readline().decode("utf-8").strip() + vals = vals.split(" ") + if len(vals) != 17: + continue + vals = [int(val) for val in vals] + + d = { + "thumb_0": vals[0], + "thumb_1": vals[1], + "thumb_2": vals[2], + "thumb_3": vals[3], + "index_0": vals[4], + "index_1": vals[5], + "index_2": vals[6], + "middle_0": vals[7], + "middle_1": vals[8], + "middle_2": vals[9], + "ring_0": vals[10], + "ring_1": vals[11], + "ring_2": vals[12], + "pinky_0": vals[13], + "pinky_1": vals[14], + "pinky_2": vals[15], + "battery_voltage": vals[16], + } + + # Update the last_d dictionary + self.last_d = d + + # Also push these new values into the rolling buffers + for joint_name, joint_val in d.items(): + self.joint_buffer[joint_name].append(joint_val) + + def run_calibration(self): + print("\nMove arm to open position") + input("Press Enter to continue...") + open_pos_list = [] + for _ in range(100): + open_pos = self.read() + open_pos_list.append(open_pos) + time.sleep(0.01) + open_pos = np.array(open_pos_list) + max_open_pos = open_pos.max(axis=0) + min_open_pos = open_pos.min(axis=0) + + print(f"{max_open_pos=}") + print(f"{min_open_pos=}") + + print("\nMove arm to closed position") + input("Press Enter to continue...") + closed_pos_list = [] + for _ in range(100): + closed_pos = self.read() + closed_pos_list.append(closed_pos) + time.sleep(0.01) + closed_pos = np.array(closed_pos_list) + max_closed_pos = closed_pos.max(axis=0) + closed_pos[closed_pos < 1000] = 60000 + min_closed_pos = closed_pos.min(axis=0) + + print(f"{max_closed_pos=}") + print(f"{min_closed_pos=}") + + open_pos = np.array([max_open_pos, max_closed_pos]).max(axis=0) + closed_pos = np.array([min_open_pos, min_closed_pos]).min(axis=0) + + # INVERSION + for i, jname in enumerate(self.joint_names): + if jname in [ + "thumb_0", + "thumb_3", + "index_2", + "middle_2", + "ring_2", + "pinky_2", + "index_0", + ]: + tmp_pos = open_pos[i] + open_pos[i] = closed_pos[i] + closed_pos[i] = tmp_pos + + print() + print(f"{open_pos=}") + print(f"{closed_pos=}") + + homing_offset = [0] * len(self.joint_names) + drive_mode = [0] * len(self.joint_names) + calib_modes = [CalibrationMode.LINEAR.name] * len(self.joint_names) + + calib_dict = { + "homing_offset": homing_offset, + "drive_mode": drive_mode, + "start_pos": open_pos, + "end_pos": closed_pos, + "calib_mode": calib_modes, + "motor_names": self.joint_names, + } + + file_path = "examples/hopejr/settings/hand_calib.pkl" + + if not os.path.exists(file_path): + with open(file_path, "wb") as f: + pickle.dump(calib_dict, f) + print(f"Dictionary saved to {file_path}") + + # return calib_dict + self.set_calibration(calib_dict) + + def set_calibration(self, calibration: dict[str, list]): + self.calibration = calibration + + def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None): + """Convert from unsigned int32 joint position range [0, 2**32[ to the universal float32 nominal degree range ]-180.0, 180.0[ with + a "zero position" at 0 degree. + + Note: We say "nominal degree range" since the motors can take values outside this range. For instance, 190 degrees, if the motor + rotate more than a half a turn from the zero position. However, most motors can't rotate more than 180 degrees and will stay in this range. + + Joints values are original in [0, 2**32[ (unsigned int32). Each motor are expected to complete a full rotation + when given a goal position that is + or - their resolution. For instance, feetech xl330-m077 have a resolution of 4096, and + at any position in their original range, let's say the position 56734, they complete a full rotation clockwise by moving to 60830, + or anticlockwise by moving to 52638. The position in the original range is arbitrary and might change a lot between each motor. + To harmonize between motors of the same model, different robots, or even models of different brands, we propose to work + in the centered nominal degree range ]-180, 180[. + """ + if motor_names is None: + motor_names = self.motor_names + + # Convert from unsigned int32 original range [0, 2**32] to signed float32 range + values = values.astype(np.float32) + + for i, name in enumerate(motor_names): + calib_idx = self.calibration["motor_names"].index(name) + calib_mode = self.calibration["calib_mode"][calib_idx] + + if CalibrationMode[calib_mode] == CalibrationMode.LINEAR: + start_pos = self.calibration["start_pos"][calib_idx] + end_pos = self.calibration["end_pos"][calib_idx] + + # Rescale the present position to a nominal range [0, 100] %, + # useful for joints with linear motions like Aloha gripper + values[i] = (values[i] - start_pos) / (end_pos - start_pos) * 100 + + if (values[i] < LOWER_BOUND_LINEAR) or (values[i] > UPPER_BOUND_LINEAR): + if name == "pinky_1" and (values[i] < LOWER_BOUND_LINEAR): + values[i] = end_pos + else: + msg = ( + f"Wrong motor position range detected for {name}. " + f"Expected to be in nominal range of [0, 100] % (a full linear translation), " + f"with a maximum range of [{LOWER_BOUND_LINEAR}, {UPPER_BOUND_LINEAR}] % to account for some imprecision during calibration, " + f"but present value is {values[i]} %. " + "This might be due to a cable connection issue creating an artificial jump in motor values. " + "You need to recalibrate by running: `python lerobot/scripts/control_robot.py calibrate`" + ) + print(msg) + # raise JointOutOfRangeError(msg) + + return values