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lerobot/examples/hopejr/leader.py

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from lerobot.common.robot_devices.motors.feetech import (
CalibrationMode,
FeetechMotorsBus,
)
import serial
import threading
import time
from typing import Callable
import pickle
import cv2
import numpy as np
from collections import deque
import json
import os
LOWER_BOUND_LINEAR = -100
UPPER_BOUND_LINEAR = 200
class HomonculusArm:
def __init__(self, serial_port: str = "/dev/ttyACM1", baud_rate: int = 115200):
self.serial_port = serial_port
self.baud_rate = 115200
self.serial = serial.Serial(self.serial_port, self.baud_rate, timeout=1)
# Number of past values to keep in memory
self.buffer_size = 10
# Initialize a buffer (deque) for each joint
self.joint_buffer = {
"wrist_roll": deque(maxlen=self.buffer_size),
"wrist_pitch": deque(maxlen=self.buffer_size),
"wrist_yaw": deque(maxlen=self.buffer_size),
"elbow_flex": deque(maxlen=self.buffer_size),
"shoulder_roll": deque(maxlen=self.buffer_size),
"shoulder_yaw": deque(maxlen=self.buffer_size),
"shoulder_pitch": deque(maxlen=self.buffer_size),
}
# Start the reading thread
self.thread = threading.Thread(target=self.async_read, daemon=True)
self.thread.start()
# Last read dictionary
self.last_d = {
"wrist_roll": 100,
"wrist_pitch": 100,
"wrist_yaw": 100,
"elbow_flex": 100,
"shoulder_roll": 100,
"shoulder_yaw": 100,
"shoulder_pitch": 100,
}
self.calibration = None
# For adaptive EMA, we store a "previous smoothed" state per joint
self.adaptive_ema_state = {
"wrist_roll": None,
"wrist_pitch": None,
"wrist_yaw": None,
"elbow_flex": None,
"shoulder_roll": None,
"shoulder_yaw": None,
"shoulder_pitch": None,
}
self.kalman_state = {
joint: {"x": None, "P": None} for joint in self.joint_buffer.keys()
}
@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:
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
class HomonculusGlove:
def __init__(self, serial_port: str = "/dev/ttyACM1", baud_rate: int = 115200):
self.serial_port = serial_port
self.baud_rate = baud_rate
self.serial = serial.Serial(self.serial_port, self.baud_rate, timeout=1)
# Number of past values to keep in memory
self.buffer_size = 10
# Initialize a buffer (deque) for each joint
self.joint_buffer = {
"thumb_0": deque(maxlen=self.buffer_size),
"thumb_1": deque(maxlen=self.buffer_size),
"thumb_2": deque(maxlen=self.buffer_size),
"thumb_3": deque(maxlen=self.buffer_size),
"index_0": deque(maxlen=self.buffer_size),
"index_1": deque(maxlen=self.buffer_size),
"index_2": deque(maxlen=self.buffer_size),
"middle_0": deque(maxlen=self.buffer_size),
"middle_1": deque(maxlen=self.buffer_size),
"middle_2": deque(maxlen=self.buffer_size),
"ring_0": deque(maxlen=self.buffer_size),
"ring_1": deque(maxlen=self.buffer_size),
"ring_2": deque(maxlen=self.buffer_size),
"pinky_0": deque(maxlen=self.buffer_size),
"pinky_1": deque(maxlen=self.buffer_size),
"pinky_2": deque(maxlen=self.buffer_size),
"battery_voltage": deque(maxlen=self.buffer_size),
}
# Start the reading thread
self.thread = threading.Thread(target=self.async_read, daemon=True)
self.thread.start()
# Last read dictionary
self.last_d = {
"thumb_0": 100,
"thumb_1": 100,
"thumb_2": 100,
"thumb_3": 100,
"index_0": 100,
"index_1": 100,
"index_2": 100,
"middle_0": 100,
"middle_1": 100,
"middle_2": 100,
"ring_0": 100,
"ring_1": 100,
"ring_2": 100,
"pinky_0": 100,
"pinky_1": 100,
"pinky_2": 100,
"battery_voltage": 100,
}
self.calibration = None
@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)
# INVERTION
# INVERTION
# INVERTION
# INVERTION
# INVERTION
# INVERTION
# INVERTION
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
# def revert_calibration(self, values: np.ndarray | list, motor_names: list[str] | None):
# """Inverse of `apply_calibration`."""
# if motor_names is None:
# motor_names = self.motor_names
# 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]
# # Convert from nominal lnear range of [0, 100] % to
# # actual motor range of values which can be arbitrary.
# values[i] = values[i] / 100 * (end_pos - start_pos) + start_pos
# values = np.round(values).astype(np.int32)
# return values
class EncoderReader:
def __init__(self, serial_port="/dev/ttyUSB1", baud_rate=115200):
self.serial_port = serial_port
self.baud_rate = baud_rate
self.serial = serial.Serial(self.serial_port, self.baud_rate, timeout=1)
# Start a background thread to continuously read from the serial port
self.thread = threading.Thread(target=self.async_read, daemon=True)
self.thread.start()
# Store the latest encoder reading in this dictionary
self.last_d = {"encoder": 500}
def async_read(self):
while True:
# Read one line from serial
line = self.serial.readline().decode("utf-8").strip()
if line:
try:
val = int(line) # Parse the incoming line as integer
self.last_d["encoder"] = val
except ValueError:
# If we couldn't parse it as an integer, just skip
pass
def read(self):
"""
Returns the last encoder value that was read.
"""
return self.last_d["encoder"]
class Tac_Man:
def __init__(self, serial_port="/dev/ttyUSB1", baud_rate=115200):
self.serial_port = serial_port
self.baud_rate = baud_rate
self.serial = serial.Serial(self.serial_port, self.baud_rate, timeout=1)
# Start a background thread to continuously read from the serial port
self.thread = threading.Thread(target=self.async_read, daemon=True)
self.thread.start()
# Store the latest encoder readings in this list
self.last_d = [0, 0, 0] # Default values for three readings
def async_read(self):
while True:
# Read one line from serial
line = self.serial.readline().decode("utf-8").strip()
if line:
try:
# Parse the incoming line as three comma-separated integers
values = [int(val) for val in line.split(",")]
if len(values) == 3: # Ensure we have exactly three values
self.last_d = values
except ValueError:
# If parsing fails, skip this line
pass
def read(self):
"""
Returns the last encoder values that were read as a list of three integers.
"""
return self.last_d
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