(WIP) Add homonculus arm & glove
This commit is contained in:
Simon Alibert
parent
34afe28ae9
commit
4678e4acd6
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from .config_homonculus import HomonculusArmConfig, HomonculusGloveConfig
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from .homonculus_arm import HomonculusArm
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from .homonculus_glove import HomonculusGlove
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#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from dataclasses import dataclass
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from ..config import TeleoperatorConfig
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@TeleoperatorConfig.register_subclass("homonculus_glove")
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@dataclass
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class HomonculusGloveConfig(TeleoperatorConfig):
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port: str # Port to connect to the glove
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baud_rate: int = 115_200
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buffer_size: int = 10 # Number of past values to keep in memory
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@TeleoperatorConfig.register_subclass("homonculus_arm")
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@dataclass
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class HomonculusArmConfig(TeleoperatorConfig):
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port: str # Port to connect to the arm
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baud_rate: int = 115_200
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buffer_size: int = 10 # Number of past values to keep in memory
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#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import logging
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import os
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import pickle
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import threading
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from collections import deque
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from enum import Enum
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import numpy as np
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import serial
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from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
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from ..teleoperator import Teleoperator
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from .config_homonculus import HomonculusArmConfig
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logger = logging.getLogger(__name__)
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LOWER_BOUND_LINEAR = -100
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UPPER_BOUND_LINEAR = 200
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class CalibrationMode(Enum):
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# Joints with rotational motions are expressed in degrees in nominal range of [-180, 180]
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DEGREE = 0
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# Joints with linear motions (like gripper of Aloha) are expressed in nominal range of [0, 100]
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LINEAR = 1
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class HomonculusArm(Teleoperator):
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config_class = HomonculusArmConfig
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name = "homonculus_arml"
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def __init__(self, config: HomonculusArmConfig):
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self.config = config
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self.serial = serial.Serial(config.port, config.baud_rate, timeout=1)
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self.buffer_size = config.buffer_size
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self.joints = [
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"wrist_roll",
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"wrist_pitch",
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"wrist_yaw",
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"elbow_flex",
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"shoulder_roll",
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"shoulder_yaw",
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"shoulder_pitch",
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]
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# Initialize a buffer (deque) for each joint
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self.joints_buffer = {joint: deque(maxlen=self.buffer_size) for joint in self.joints}
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# Last read dictionary
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self.last_d = {joint: 100 for joint in self.joints}
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# For adaptive EMA, we store a "previous smoothed" state per joint
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self.adaptive_ema_state = {joint: None for joint in self.joints}
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self.kalman_state = {joint: {"x": None, "P": None} for joint in self.joints}
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self.calibration = None
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self.thread = threading.Thread(target=self.async_read, daemon=True)
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@property
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def action_feature(self) -> dict:
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return {
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"dtype": "float32",
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"shape": (len(self.joints),),
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"names": {"motors": self.joints},
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}
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@property
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def feedback_feature(self) -> dict:
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return {}
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@property
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def is_connected(self) -> bool:
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return self.thread.is_alive() and self.serial.is_open
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def connect(self) -> None:
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if self.is_connected:
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raise DeviceAlreadyConnectedError(f"{self} already connected")
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self.serial.open()
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self.thread.start()
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self.configure()
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logger.info(f"{self} connected.")
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@property
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def is_calibrated(self) -> bool:
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raise NotImplementedError # TODO
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def calibrate(self) -> None:
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raise NotImplementedError # TODO
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def configure(self) -> None:
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raise NotImplementedError # TODO
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def get_action(self) -> dict[str, float]:
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raise NotImplementedError # TODO
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def send_feedback(self, feedback: dict[str, float]) -> None:
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raise NotImplementedError
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def disconnect(self) -> None:
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if not self.is_connected:
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DeviceNotConnectedError(f"{self} is not connected.")
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self.thread.join()
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self.serial.close()
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logger.info(f"{self} disconnected.")
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### WIP below ###
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@property
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def joint_names(self):
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return list(self.last_d.keys())
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def read(self, motor_names: list[str] | None = None):
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"""
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Return the most recent (single) values from self.last_d,
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optionally applying calibration.
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"""
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if motor_names is None:
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motor_names = self.joint_names
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# Get raw (last) values
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values = np.array([self.last_d[k] for k in motor_names])
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# print(motor_names)
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print(values)
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# Apply calibration if available
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if self.calibration is not None:
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values = self.apply_calibration(values, motor_names)
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print(values)
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return values
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def read_running_average(self, motor_names: list[str] | None = None, linearize=False):
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"""
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Return the AVERAGE of the most recent self.buffer_size (or fewer, if not enough data) readings
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for each joint, optionally applying calibration.
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"""
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if motor_names is None:
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motor_names = self.joint_names
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# Gather averaged readings from buffers
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smoothed_vals = []
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for name in motor_names:
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buf = self.joint_buffer[name]
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if len(buf) == 0:
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# If no data has been read yet, fall back to last_d
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smoothed_vals.append(self.last_d[name])
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else:
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# Otherwise, average over the existing buffer
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smoothed_vals.append(np.mean(buf))
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smoothed_vals = np.array(smoothed_vals, dtype=np.float32)
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# Apply calibration if available
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if self.calibration is not None:
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if False:
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for i, joint_name in enumerate(motor_names):
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# Re-use the same raw_min / raw_max from the calibration
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calib_idx = self.calibration["motor_names"].index(joint_name)
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min_reading = self.calibration["start_pos"][calib_idx]
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max_reading = self.calibration["end_pos"][calib_idx]
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b_value = smoothed_vals[i]
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print(joint_name)
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if joint_name == "elbow_flex":
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print("elbow")
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try:
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smoothed_vals[i] = int(
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min_reading
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+ (max_reading - min_reading)
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* np.arcsin((b_value - min_reading) / (max_reading - min_reading))
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/ (np.pi / 2)
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)
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except Exception as e:
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print("not working")
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print(smoothed_vals)
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print("not working")
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smoothed_vals = self.apply_calibration(smoothed_vals, motor_names)
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return smoothed_vals
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def read_kalman_filter(
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self, Q: float = 1.0, R: float = 100.0, motor_names: list[str] | None = None
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) -> np.ndarray:
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"""
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Return a Kalman-filtered reading for each requested joint.
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We store a separate Kalman filter (x, P) per joint. For each new measurement Z:
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1) Predict:
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x_pred = x (assuming no motion model)
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P_pred = P + Q
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2) Update:
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K = P_pred / (P_pred + R)
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x = x_pred + K * (Z - x_pred)
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P = (1 - K) * P_pred
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:param Q: Process noise. Larger Q means the estimate can change more freely.
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:param R: Measurement noise. Larger R means we trust our sensor less.
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:param motor_names: If not specified, all joints are filtered.
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:return: Kalman-filtered positions as a numpy array.
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"""
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if motor_names is None:
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motor_names = self.joint_names
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current_vals = np.array([self.last_d[name] for name in motor_names], dtype=np.float32)
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filtered_vals = np.zeros_like(current_vals)
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for i, name in enumerate(motor_names):
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# Retrieve the filter state for this joint
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x = self.kalman_state[name]["x"]
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P = self.kalman_state[name]["P"]
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Z = current_vals[i]
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# If this is the first reading, initialize
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if x is None or P is None:
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x = Z
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P = 1.0 # or some large initial uncertainty
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# 1) Predict step
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x_pred = x # no velocity model, so x_pred = x
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P_pred = P + Q
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# 2) Update step
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K = P_pred / (P_pred + R) # Kalman gain
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x_new = x_pred + K * (Z - x_pred) # new state estimate
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P_new = (1 - K) * P_pred # new covariance
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# Save back
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self.kalman_state[name]["x"] = x_new
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self.kalman_state[name]["P"] = P_new
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filtered_vals[i] = x_new
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if self.calibration is not None:
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filtered_vals = self.apply_calibration(filtered_vals, motor_names)
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return filtered_vals
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def async_read(self):
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"""
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Continuously read from the serial buffer in its own thread,
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store into `self.last_d` and also append to the rolling buffer (joint_buffer).
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"""
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while True:
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if self.serial.in_waiting > 0:
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self.serial.flush()
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vals = self.serial.readline().decode("utf-8").strip()
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vals = vals.split(" ")
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if len(vals) != 7:
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continue
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try:
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vals = [int(val) for val in vals] # remove last digit
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except ValueError:
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self.serial.flush()
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vals = self.serial.readline().decode("utf-8").strip()
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vals = vals.split(" ")
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vals = [int(val) for val in vals]
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d = {
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"wrist_roll": vals[0],
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"wrist_yaw": vals[1],
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"wrist_pitch": vals[2],
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"elbow_flex": vals[3],
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"shoulder_roll": vals[4],
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"shoulder_yaw": vals[5],
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"shoulder_pitch": vals[6],
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}
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# Update the last_d dictionary
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self.last_d = d
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# Also push these new values into the rolling buffers
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for joint_name, joint_val in d.items():
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self.joint_buffer[joint_name].append(joint_val)
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# Optional: short sleep to avoid busy-loop
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# time.sleep(0.001)
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def run_calibration(self, robot):
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robot.arm_bus.write("Acceleration", 50)
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n_joints = len(self.joint_names)
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max_open_all = np.zeros(n_joints, dtype=np.float32)
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min_open_all = np.zeros(n_joints, dtype=np.float32)
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max_closed_all = np.zeros(n_joints, dtype=np.float32)
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min_closed_all = np.zeros(n_joints, dtype=np.float32)
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for i, jname in enumerate(self.joint_names):
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print(f"\n--- Calibrating joint '{jname}' ---")
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joint_idx = robot.arm_calib_dict["motor_names"].index(jname)
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open_val = robot.arm_calib_dict["start_pos"][joint_idx]
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print(f"Commanding {jname} to OPEN position {open_val}...")
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robot.arm_bus.write("Goal_Position", [open_val], [jname])
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input("Physically verify or adjust the joint. Press Enter when ready to capture...")
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open_pos_list = []
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for _ in range(100):
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all_joints_vals = self.read() # read entire arm
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open_pos_list.append(all_joints_vals[i]) # store only this joint
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time.sleep(0.01)
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# Convert to numpy and track min/max
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open_array = np.array(open_pos_list, dtype=np.float32)
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max_open_all[i] = open_array.max()
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min_open_all[i] = open_array.min()
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closed_val = robot.arm_calib_dict["end_pos"][joint_idx]
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if jname == "elbow_flex":
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closed_val = closed_val - 700
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closed_val = robot.arm_calib_dict["end_pos"][joint_idx]
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print(f"Commanding {jname} to CLOSED position {closed_val}...")
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robot.arm_bus.write("Goal_Position", [closed_val], [jname])
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input("Physically verify or adjust the joint. Press Enter when ready to capture...")
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closed_pos_list = []
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for _ in range(100):
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all_joints_vals = self.read()
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closed_pos_list.append(all_joints_vals[i])
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time.sleep(0.01)
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closed_array = np.array(closed_pos_list, dtype=np.float32)
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# Some thresholding for closed positions
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# closed_array[closed_array < 1000] = 60000
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max_closed_all[i] = closed_array.max()
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min_closed_all[i] = closed_array.min()
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robot.arm_bus.write("Goal_Position", [int((closed_val + open_val) / 2)], [jname])
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open_pos = np.maximum(max_open_all, max_closed_all)
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closed_pos = np.minimum(min_open_all, min_closed_all)
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for i, jname in enumerate(self.joint_names):
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if jname not in ["wrist_pitch", "shoulder_pitch"]:
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# Swap open/closed for these joints
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tmp_pos = open_pos[i]
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open_pos[i] = closed_pos[i]
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closed_pos[i] = tmp_pos
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# Debug prints
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print("\nFinal open/closed arrays after any swaps/inversions:")
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print(f"open_pos={open_pos}")
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print(f"closed_pos={closed_pos}")
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homing_offset = [0] * n_joints
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drive_mode = [0] * n_joints
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calib_modes = [CalibrationMode.LINEAR.name] * n_joints
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calib_dict = {
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"homing_offset": homing_offset,
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"drive_mode": drive_mode,
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"start_pos": open_pos,
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"end_pos": closed_pos,
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"calib_mode": calib_modes,
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"motor_names": self.joint_names,
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}
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file_path = "examples/hopejr/settings/arm_calib.pkl"
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if not os.path.exists(file_path):
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with open(file_path, "wb") as f:
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pickle.dump(calib_dict, f)
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print(f"Dictionary saved to {file_path}")
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self.set_calibration(calib_dict)
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def set_calibration(self, calibration: dict[str, list]):
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self.calibration = calibration
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def apply_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
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"""
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Example calibration that linearly maps [start_pos, end_pos] to [0,100].
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Extend or modify for your needs.
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"""
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if motor_names is None:
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motor_names = self.joint_names
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values = values.astype(np.float32)
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for i, name in enumerate(motor_names):
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calib_idx = self.calibration["motor_names"].index(name)
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calib_mode = self.calibration["calib_mode"][calib_idx]
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if CalibrationMode[calib_mode] == CalibrationMode.LINEAR:
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start_pos = self.calibration["start_pos"][calib_idx]
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end_pos = self.calibration["end_pos"][calib_idx]
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# Rescale the present position to [0, 100]
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values[i] = (values[i] - start_pos) / (end_pos - start_pos) * 100
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# Check boundaries
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if (values[i] < LOWER_BOUND_LINEAR) or (values[i] > UPPER_BOUND_LINEAR):
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# If you want to handle out-of-range differently:
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# raise JointOutOfRangeError(msg)
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msg = (
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f"Wrong motor position range detected for {name}. "
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f"Value = {values[i]} %, expected within [{LOWER_BOUND_LINEAR}, {UPPER_BOUND_LINEAR}]"
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)
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print(msg)
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return values
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@ -0,0 +1,348 @@
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#!/usr/bin/env python
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# Copyright 2025 The HuggingFace Inc. team. All rights reserved.
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#
|
||||
# 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
|
||||
Loading…
Reference in New Issue