forked from tangger/lerobot
(WIP) Update arm
This commit is contained in:
Simon Alibert
@@ -15,71 +15,53 @@
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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 # nosec
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import threading
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import time
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from collections import deque
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from enum import Enum
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from pprint import pformat
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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 lerobot.common.motors.motors_bus import MotorCalibration, MotorNormMode
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from lerobot.common.utils.utils import enter_pressed, move_cursor_up
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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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"""
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Homonculus Arm designed by Hugging Face.
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"""
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config_class = HomonculusArmConfig
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name = "homonculus_arm"
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def __init__(self, config: HomonculusArmConfig):
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super().__init__(config)
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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 = dict.fromkeys(self.joints, 100)
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# For adaptive EMA, we store a "previous smoothed" state per joint
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self.adaptive_ema_state = dict.fromkeys(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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self.joints = {
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"wrist_roll": MotorNormMode.RANGE_M100_100,
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"wrist_pitch": MotorNormMode.RANGE_M100_100,
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"wrist_yaw": MotorNormMode.RANGE_M100_100,
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"elbow_flex": MotorNormMode.RANGE_M100_100,
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"shoulder_roll": MotorNormMode.RANGE_M100_100,
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"shoulder_yaw": MotorNormMode.RANGE_M100_100,
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"shoulder_pitch": MotorNormMode.RANGE_M100_100,
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}
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self.thread = threading.Thread(target=self._async_read, daemon=True, name=f"{self} _async_read")
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self._lock = threading.Lock()
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@property
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def action_feature(self) -> dict:
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def action_features(self) -> dict:
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return {f"{joint}.pos": float for joint in self.joints}
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@property
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def feedback_feature(self) -> dict:
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def feedback_features(self) -> dict:
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return {}
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@property
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@@ -90,23 +72,152 @@ class HomonculusArm(Teleoperator):
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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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if not self.serial.is_open:
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self.serial.open()
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self.thread.start()
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self.configure()
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time.sleep(1) # gives time for the thread to ramp up
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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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return self.calibration_fpath.is_file()
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def calibrate(self) -> None:
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raise NotImplementedError # TODO
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print(
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"\nMove all joints through their entire range of motion."
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"\nRecording positions. Press ENTER to stop..."
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)
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range_mins, range_maxes = self._record_ranges_of_motion()
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self.calibration = {}
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for id_, joint in enumerate(self.joints):
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self.calibration[joint] = MotorCalibration(
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id=id_,
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drive_mode=0,
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homing_offset=0,
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range_min=range_mins[joint],
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range_max=range_maxes[joint],
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)
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self._save_calibration()
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print("Calibration saved to", self.calibration_fpath)
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def _record_ranges_of_motion(
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self, joints: list[str] | None = None, display_values: bool = True
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) -> tuple[dict[str, int], dict[str, int]]:
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"""Interactively record the min/max encoder values of each joint.
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Move the joints while the method streams live positions. Press :kbd:`Enter` to finish.
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Args:
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joints (list[str] | None, optional): Joints to record. Defaults to every joint (`None`).
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display_values (bool, optional): When `True` (default) a live table is printed to the console.
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Raises:
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TypeError: `joints` is not `None` or a list.
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ValueError: any joint's recorded min and max are the same.
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Returns:
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tuple[dict[str, int], dict[str, int]]: Two dictionaries *mins* and *maxes* with the extreme values
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observed for each joint.
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"""
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if joints is None:
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joints = list(self.joints)
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elif not isinstance(joints, list):
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raise TypeError(joints)
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start_positions = self._read(joints, normalize=False)
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mins = start_positions.copy()
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maxes = start_positions.copy()
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while True:
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positions = self._read(joints, normalize=False)
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mins = {joint: min(positions[joint], min_) for joint, min_ in mins.items()}
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maxes = {joint: max(positions[joint], max_) for joint, max_ in maxes.items()}
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if display_values:
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print("\n-------------------------------------------")
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print(f"{'NAME':<15} | {'MIN':>6} | {'POS':>6} | {'MAX':>6}")
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for joint in joints:
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print(f"{joint:<15} | {mins[joint]:>6} | {positions[joint]:>6} | {maxes[joint]:>6}")
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if enter_pressed():
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break
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if display_values:
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# Move cursor up to overwrite the previous output
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move_cursor_up(len(joints) + 3)
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same_min_max = [joint for joint in joints if mins[joint] == maxes[joint]]
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if same_min_max:
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raise ValueError(f"Some joints have the same min and max values:\n{pformat(same_min_max)}")
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return mins, maxes
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def configure(self) -> None:
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raise NotImplementedError # TODO
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pass
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def _normalize(self, values: dict[str, int], joints: list[str] | None = None):
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if not self.calibration:
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raise RuntimeError(f"{self} has no calibration registered.")
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normalized_values = {}
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for joint, val in values.items():
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min_ = self.calibration[joint].range_min
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max_ = self.calibration[joint].range_max
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drive_mode = self.calibration[joint].drive_mode
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bounded_val = min(max_, max(min_, val))
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if self.joints[joint] is MotorNormMode.RANGE_M100_100:
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norm = (((bounded_val - min_) / (max_ - min_)) * 200) - 100
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normalized_values[joint] = -norm if drive_mode else norm
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elif self.joints[joint] is MotorNormMode.RANGE_0_100:
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norm = ((bounded_val - min_) / (max_ - min_)) * 100
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normalized_values[joint] = 100 - norm if drive_mode else norm
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return normalized_values
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def _read(self, joints: list[str] | None = None, normalize: bool = True) -> dict[str, int | float]:
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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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with self._lock:
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state = self._state
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if joints is not None:
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state = {k: v for k, v in state.items() if k in joints}
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if normalize:
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state = self._normalize(state, joints)
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return state
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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 and sends values to the main thread through
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a queue.
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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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raw_values = self.serial.readline().decode("utf-8").strip().split(" ")
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if len(raw_values) != 21: # 16 raw + 5 angle values
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continue
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try:
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joint_angles = {
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joint: int(pos)
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for joint, pos in zip(self.joints, raw_values[:2] + raw_values[16:], strict=True)
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}
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except ValueError:
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continue
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with self._lock:
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self._state = joint_angles
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def get_action(self) -> dict[str, float]:
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raise NotImplementedError # TODO
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joint_positions = self._read()
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return {f"{joint}.pos": pos for joint, pos in joint_positions.items()}
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def send_feedback(self, feedback: dict[str, float]) -> None:
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raise NotImplementedError
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@@ -115,307 +226,6 @@ class HomonculusArm(Teleoperator):
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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.thread.join(timeout=0.5)
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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:
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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, process_noise: float = 1.0, measurement_noise: float = 100.0, motors: 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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Args:
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process_noise (float, optional): Process noise (Q). Larger Q means the estimate can change more
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freely. Defaults to 1.0.
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measurement_noise (float, optional): Measurement noise (R). Larger R means we trust our sensor
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less. Defaults to 100.0.
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motors (list[str] | None, optional): If None, all joints are filtered. Defaults to None.
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Returns:
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np.ndarray: Kalman-filtered positions.
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"""
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if motors is None:
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motors = self.joint_names
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current_vals = np.array([self.last_d[name] for name in motors], dtype=np.float32)
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filtered_vals = np.zeros_like(current_vals)
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for i, name in enumerate(motors):
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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 + process_noise
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# 2) Update step
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kalman_gain = p_pred / (p_pred + measurement_noise) # Kalman gain
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x_new = x_pred + kalman_gain * (z - x_pred) # new state estimate
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p_new = (1 - kalman_gain) * 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, motors)
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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()
|
||||
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) # TODO(aliberts): use json
|
||||
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
|
||||
|
||||
Reference in New Issue
Block a user