refactor(robots): lewiki v0.1
This commit is contained in:
Steven Palma
parent
99c0938b42
commit
6a3cdc0489
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@ -0,0 +1,35 @@
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def main():
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teleop_arm_config = SO100TeleopConfig(port="/dev/tty.usbmodem585A0085511")
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teleop_arm = SO100Teleop(teleop_arm_config)
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keyboard_config = KeyboardTeleopConfig()
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keyboard = KeyboardTeleop(keyboard_config)
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robot_config = kiwiconfig(port="/dev/tty.usbmodem575E0032081")
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robot = KiwiRobotDaemon(robot_config)
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teleop_arm.connect()
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keyboard.connect()
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robot.connect() # Establish ZMQ sockets with the mobile robot
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start = time.perf_counter()
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duration = 0
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while duration < 20:
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arm_action = teleop_arm.get_action()
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base_action = keyboard.get_action()
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action = {
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**arm_action,
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# **base_action ??
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}
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robot.send_action(action) # sends over ZMQ
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# robot.get_observation() # receives over ZMQ
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print(action)
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duration = time.perf_counter() - start
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robot.disconnect() # cleans ZMQ comms
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teleop.disconnect()
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if __name__ == "__main__":
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main()
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@ -1,17 +1,3 @@
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# Copyright 2024 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 base64
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import json
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import os
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@ -407,19 +393,21 @@ class MobileManipulator:
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for name in self.leader_arms:
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pos = self.leader_arms[name].read("Present_Position")
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pos_tensor = torch.from_numpy(pos).float()
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# Instead of pos_tensor.item(), use tolist() to convert the entire tensor to a list
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arm_positions.extend(pos_tensor.tolist())
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y_cmd = 0.0 # m/s forward/backward
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x_cmd = 0.0 # m/s lateral
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# (The rest of your code for generating wheel commands remains unchanged)
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x_cmd = 0.0 # m/s forward/backward
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y_cmd = 0.0 # m/s lateral
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theta_cmd = 0.0 # deg/s rotation
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if self.pressed_keys["forward"]:
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y_cmd += xy_speed
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if self.pressed_keys["backward"]:
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y_cmd -= xy_speed
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if self.pressed_keys["left"]:
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x_cmd += xy_speed
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if self.pressed_keys["right"]:
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if self.pressed_keys["backward"]:
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x_cmd -= xy_speed
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if self.pressed_keys["left"]:
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y_cmd += xy_speed
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if self.pressed_keys["right"]:
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y_cmd -= xy_speed
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if self.pressed_keys["rotate_left"]:
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theta_cmd += theta_speed
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if self.pressed_keys["rotate_right"]:
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@ -597,8 +585,8 @@ class MobileManipulator:
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# Create the body velocity vector [x, y, theta_rad].
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velocity_vector = np.array([x_cmd, y_cmd, theta_rad])
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# Define the wheel mounting angles (defined from y axis cw)
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angles = np.radians(np.array([300, 180, 60]))
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# Define the wheel mounting angles with a -90° offset.
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angles = np.radians(np.array([240, 120, 0]) - 90)
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# Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed.
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# The third column (base_radius) accounts for the effect of rotation.
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m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
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@ -654,8 +642,8 @@ class MobileManipulator:
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# Compute each wheel’s linear speed (m/s) from its angular speed.
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wheel_linear_speeds = wheel_radps * wheel_radius
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# Define the wheel mounting angles (defined from y axis cw)
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angles = np.radians(np.array([300, 180, 60]))
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# Define the wheel mounting angles with a -90° offset.
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angles = np.radians(np.array([240, 120, 0]) - 90)
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m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
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# Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds.
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@ -1,692 +1,339 @@
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import base64
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import json
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import os
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import sys
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from pathlib import Path
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#!/usr/bin/env python
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import cv2
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# Copyright 2024 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 json
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import logging
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import time
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import threading
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import numpy as np
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import torch
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import zmq
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import time
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# import torch
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from lerobot.common.cameras.utils import make_cameras_from_configs
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from lerobot.common.errors import DeviceNotConnectedError
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from lerobot.common.motors.feetech.feetech import TorqueMode
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from lerobot.common.motors.feetech.feetech_calibration import run_full_arm_calibration
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from lerobot.common.motors.motors_bus import MotorsBus
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from lerobot.common.motors.utils import make_motors_buses_from_configs
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from lerobot.common.robots.lekiwi.configuration_lekiwi import LeKiwiRobotConfig
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from lerobot.common.robots.utils import get_arm_id
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from lerobot.common.constants import OBS_IMAGES, OBS_STATE
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from lerobot.common.errors import DeviceAlreadyConnectedError, DeviceNotConnectedError
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from ..robot import Robot
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from ..utils import ensure_safe_goal_position
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from .configuration_lekiwi import LeKiwiRobotConfig
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from lerobot.common.motors.feetech import (
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FeetechMotorsBus,
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TorqueMode,
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run_arm_manual_calibration,
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)
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import zmq
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PYNPUT_AVAILABLE = True
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try:
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# Only import if there's a valid X server or if we're not on a Pi
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if ("DISPLAY" not in os.environ) and ("linux" in sys.platform):
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print("No DISPLAY set. Skipping pynput import.")
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raise ImportError("pynput blocked intentionally due to no display.")
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from pynput import keyboard
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except ImportError:
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keyboard = None
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PYNPUT_AVAILABLE = False
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except Exception as e:
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keyboard = None
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PYNPUT_AVAILABLE = False
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print(f"Could not import pynput: {e}")
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class MobileManipulator:
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class LeKiwiRobot(Robot):
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"""
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MobileManipulator is a class for connecting to and controlling a remote mobile manipulator robot.
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The robot includes a three omniwheel mobile base and a remote follower arm.
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The leader arm is connected locally (on the laptop) and its joint positions are recorded and then
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forwarded to the remote follower arm (after applying a safety clamp).
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In parallel, keyboard teleoperation is used to generate raw velocity commands for the wheels.
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"""
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config_class = LeKiwiRobotConfig
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name = "lekiwi"
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def __init__(self, config: LeKiwiRobotConfig):
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"""
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Expected keys in config:
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- ip, port, video_port for the remote connection.
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- calibration_dir, leader_arms, follower_arms, max_relative_target, etc.
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"""
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self.robot_type = config.type
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super().__init__(config)
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self.config = config
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# self.robot_type = config.type
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# self.id = config.id
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self.remote_ip = config.ip
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self.remote_port = config.port
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self.remote_port_video = config.video_port
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self.calibration_dir = Path(self.config.calibration_dir)
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self.logs = {}
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# self.logs = {}
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# TODO(Steven): This should go to teleop
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# self.teleop_keys = self.config.teleop_keys
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self.teleop_keys = self.config.teleop_keys
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# TODO(Steven): Consider in the future using S100 robot class
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# TODO(Steven): Another option is to use the motorbus factory, but in this case we assume that
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# what we consider 'lekiwi robot' always uses the FeetechMotorsBus
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# TODO(Steve): We will need to have a key for arm and base for calibration
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self.actuators = FeetechMotorsBus(
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port=self.config.port,
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motors={
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"shoulder_pan": config.shoulder_pan,
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"shoulder_lift": config.shoulder_lift,
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"elbow_flex": config.elbow_flex,
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"wrist_flex": config.wrist_flex,
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"wrist_roll": config.wrist_roll,
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"gripper": config.gripper,
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"left_wheel": config.left_wheel,
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"right_wheel": config.right_wheel,
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"back_wheel": config.back_wheel,
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},
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)
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# For teleoperation, the leader arm (local) is used to record the desired arm pose.
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self.leader_arms = make_motors_buses_from_configs(self.config.leader_arms)
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self.follower_arms = make_motors_buses_from_configs(self.config.follower_arms)
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self.cameras = make_cameras_from_configs(self.config.cameras)
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#TODO(Steven): Consider removing cameras from configs
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self.cameras = make_cameras_from_configs(config.cameras)
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self.is_connected = False
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self.logs = {}
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self.last_frames = {}
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self.last_present_speed = {}
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self.last_remote_arm_state = torch.zeros(6, dtype=torch.float32)
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# Define three speed levels and a current index
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self.speed_levels = [
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{"xy": 0.1, "theta": 30}, # slow
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{"xy": 0.2, "theta": 60}, # medium
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{"xy": 0.3, "theta": 90}, # fast
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]
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self.speed_index = 0 # Start at slow
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self.observation_lock = threading.Lock()
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self.last_observation = None
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# self.last_frames = {}
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# self.last_present_speed = {}
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# self.last_remote_arm_state = torch.zeros(6, dtype=torch.float32)
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# ZeroMQ context and sockets.
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self.context = None
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self.cmd_socket = None
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self.video_socket = None
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self.observation_socket = None
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# Keyboard state for base teleoperation.
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self.running = True
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self.pressed_keys = {
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"forward": False,
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"backward": False,
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"left": False,
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"right": False,
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"rotate_left": False,
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"rotate_right": False,
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}
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if PYNPUT_AVAILABLE:
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print("pynput is available - enabling local keyboard listener.")
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self.listener = keyboard.Listener(
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on_press=self.on_press,
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on_release=self.on_release,
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)
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self.listener.start()
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else:
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print("pynput not available - skipping local keyboard listener.")
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self.listener = None
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def get_motor_names(self, arms: dict[str, MotorsBus]) -> list:
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return [f"{arm}_{motor}" for arm, bus in arms.items() for motor in bus.motors]
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@property
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def camera_features(self) -> dict:
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def state_feature(self) -> dict:
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return {
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"dtype": "float32",
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"shape": (len(self.actuators),),
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"names": {"motors": list(self.actuators.motors)},
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}
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@property
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def action_feature(self) -> dict:
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return self.state_feature
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@property
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def camera_features(self) -> dict[str, dict]:
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cam_ft = {}
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for cam_key, cam in self.cameras.items():
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key = f"observation.images.{cam_key}"
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cam_ft[key] = {
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cam_ft[cam_key] = {
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"shape": (cam.height, cam.width, cam.channels),
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"names": ["height", "width", "channels"],
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"info": None,
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}
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return cam_ft
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@property
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def motor_features(self) -> dict:
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follower_arm_names = [
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"shoulder_pan",
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"shoulder_lift",
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"elbow_flex",
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"wrist_flex",
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"wrist_roll",
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"gripper",
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]
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observations = ["x_mm", "y_mm", "theta"]
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combined_names = follower_arm_names + observations
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return {
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"action": {
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"dtype": "float32",
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"shape": (len(combined_names),),
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"names": combined_names,
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},
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"observation.state": {
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"dtype": "float32",
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"shape": (len(combined_names),),
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"names": combined_names,
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},
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}
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def setup_zmq_sockets(self, config):
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context = zmq.Context()
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cmd_socket = context.socket(zmq.PULL)
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cmd_socket.setsockopt(zmq.CONFLATE, 1)
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cmd_socket.bind(f"tcp://*:{config.port}")
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@property
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def features(self):
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return {**self.motor_features, **self.camera_features}
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observation_socket = context.socket(zmq.PUSH)
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observation_socket.setsockopt(zmq.CONFLATE, 1)
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observation_socket.bind(f"tcp://*:{config.video_port}")
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@property
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def has_camera(self):
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return len(self.cameras) > 0
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return context, cmd_socket, observation_socket
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def setup_actuators(self):
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# We assume that at connection time, arm is in a rest position,
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# and torque can be safely disabled to run calibration.
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self.actuators.write("Torque_Enable", TorqueMode.DISABLED.value)
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self.calibrate()
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@property
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def num_cameras(self):
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return len(self.cameras)
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# Mode=0 for Position Control
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# TODO(Steven): Base robots should actually be in vel mode
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self.actuators.write("Mode", 0)
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# Set P_Coefficient to lower value to avoid shakiness (Default is 32)
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self.actuators.write("P_Coefficient", 16)
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# Set I_Coefficient and D_Coefficient to default value 0 and 32
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self.actuators.write("I_Coefficient", 0)
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self.actuators.write("D_Coefficient", 32)
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# Close the write lock so that Maximum_Acceleration gets written to EPROM address,
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# which is mandatory for Maximum_Acceleration to take effect after rebooting.
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self.actuators.write("Lock", 0)
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# Set Maximum_Acceleration to 254 to speedup acceleration and deceleration of
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# the motors. Note: this configuration is not in the official STS3215 Memory Table
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self.actuators.write("Maximum_Acceleration", 254)
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self.actuators.write("Acceleration", 254)
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@property
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def available_arms(self):
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available = []
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for name in self.leader_arms:
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available.append(get_arm_id(name, "leader"))
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for name in self.follower_arms:
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available.append(get_arm_id(name, "follower"))
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return available
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logging.info("Activating torque.")
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self.actuators.write("Torque_Enable", TorqueMode.ENABLED.value)
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def on_press(self, key):
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try:
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# Movement
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if key.char == self.teleop_keys["forward"]:
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self.pressed_keys["forward"] = True
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elif key.char == self.teleop_keys["backward"]:
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self.pressed_keys["backward"] = True
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elif key.char == self.teleop_keys["left"]:
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self.pressed_keys["left"] = True
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elif key.char == self.teleop_keys["right"]:
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self.pressed_keys["right"] = True
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elif key.char == self.teleop_keys["rotate_left"]:
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self.pressed_keys["rotate_left"] = True
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elif key.char == self.teleop_keys["rotate_right"]:
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self.pressed_keys["rotate_right"] = True
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# Check arm can be read
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self.actuators.read("Present_Position")
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# Quit teleoperation
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elif key.char == self.teleop_keys["quit"]:
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self.running = False
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return False
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def connect(self) -> None:
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if self.is_connected:
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raise DeviceAlreadyConnectedError(
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"LeKiwi Robot is already connected. Do not run `robot.connect()` twice."
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)
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# Speed control
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elif key.char == self.teleop_keys["speed_up"]:
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self.speed_index = min(self.speed_index + 1, 2)
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print(f"Speed index increased to {self.speed_index}")
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elif key.char == self.teleop_keys["speed_down"]:
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self.speed_index = max(self.speed_index - 1, 0)
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print(f"Speed index decreased to {self.speed_index}")
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logging.info("Connecting actuators.")
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self.actuators.connect()
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self.setup_actuators()
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except AttributeError:
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# e.g., if key is special like Key.esc
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if key == keyboard.Key.esc:
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self.running = False
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return False
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logging.info("Connecting cameras.")
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for cam in self.cameras.values():
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cam.connect()
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def on_release(self, key):
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try:
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if hasattr(key, "char"):
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if key.char == self.teleop_keys["forward"]:
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self.pressed_keys["forward"] = False
|
||||
elif key.char == self.teleop_keys["backward"]:
|
||||
self.pressed_keys["backward"] = False
|
||||
elif key.char == self.teleop_keys["left"]:
|
||||
self.pressed_keys["left"] = False
|
||||
elif key.char == self.teleop_keys["right"]:
|
||||
self.pressed_keys["right"] = False
|
||||
elif key.char == self.teleop_keys["rotate_left"]:
|
||||
self.pressed_keys["rotate_left"] = False
|
||||
elif key.char == self.teleop_keys["rotate_right"]:
|
||||
self.pressed_keys["rotate_right"] = False
|
||||
except AttributeError:
|
||||
pass
|
||||
logging.info("Connecting ZMQ sockets.")
|
||||
self.context, self.cmd_socket, self.observation_socket = self.setup_zmq_sockets(self.config)
|
||||
|
||||
def connect(self):
|
||||
if not self.leader_arms:
|
||||
raise ValueError("MobileManipulator has no leader arm to connect.")
|
||||
for name in self.leader_arms:
|
||||
print(f"Connecting {name} leader arm.")
|
||||
self.calibrate_leader()
|
||||
|
||||
# Set up ZeroMQ sockets to communicate with the remote mobile robot.
|
||||
self.context = zmq.Context()
|
||||
self.cmd_socket = self.context.socket(zmq.PUSH)
|
||||
connection_string = f"tcp://{self.remote_ip}:{self.remote_port}"
|
||||
self.cmd_socket.connect(connection_string)
|
||||
self.cmd_socket.setsockopt(zmq.CONFLATE, 1)
|
||||
self.video_socket = self.context.socket(zmq.PULL)
|
||||
video_connection = f"tcp://{self.remote_ip}:{self.remote_port_video}"
|
||||
self.video_socket.connect(video_connection)
|
||||
self.video_socket.setsockopt(zmq.CONFLATE, 1)
|
||||
print(
|
||||
f"[INFO] Connected to remote robot at {connection_string} and video stream at {video_connection}."
|
||||
)
|
||||
self.is_connected = True
|
||||
|
||||
def load_or_run_calibration_(self, name, arm, arm_type):
|
||||
arm_id = get_arm_id(name, arm_type)
|
||||
arm_calib_path = self.calibration_dir / f"{arm_id}.json"
|
||||
def calibrate(self) -> None:
|
||||
# Copied from S100 robot
|
||||
"""After calibration all motors function in human interpretable ranges.
|
||||
Rotations are expressed in degrees in nominal range of [-180, 180],
|
||||
and linear motions (like gripper of Aloha) in nominal range of [0, 100].
|
||||
"""
|
||||
actuators_calib_path = self.calibration_dir / f"{self.config.id}.json"
|
||||
|
||||
if arm_calib_path.exists():
|
||||
with open(arm_calib_path) as f:
|
||||
if actuators_calib_path.exists():
|
||||
with open(actuators_calib_path) as f:
|
||||
calibration = json.load(f)
|
||||
else:
|
||||
print(f"Missing calibration file '{arm_calib_path}'")
|
||||
calibration = run_full_arm_calibration(arm, self.robot_type, name, arm_type)
|
||||
print(f"Calibration is done! Saving calibration file '{arm_calib_path}'")
|
||||
arm_calib_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(arm_calib_path, "w") as f:
|
||||
# TODO(rcadene): display a warning in __init__ if calibration file not available
|
||||
logging.info(f"Missing calibration file '{actuators_calib_path}'")
|
||||
calibration = run_arm_manual_calibration(self.actuators, self.robot_type, self.name, "follower")
|
||||
|
||||
logging.info(f"Calibration is done! Saving calibration file '{actuators_calib_path}'")
|
||||
actuators_calib_path.parent.mkdir(parents=True, exist_ok=True)
|
||||
with open(actuators_calib_path, "w") as f:
|
||||
json.dump(calibration, f)
|
||||
|
||||
return calibration
|
||||
self.actuators.set_calibration(calibration)
|
||||
|
||||
def calibrate_leader(self):
|
||||
for name, arm in self.leader_arms.items():
|
||||
# Connect the bus
|
||||
arm.connect()
|
||||
|
||||
# Disable torque on all motors
|
||||
for motor_id in arm.motors:
|
||||
arm.write("Torque_Enable", TorqueMode.DISABLED.value, motor_id)
|
||||
|
||||
# Now run calibration
|
||||
calibration = self.load_or_run_calibration_(name, arm, "leader")
|
||||
arm.set_calibration(calibration)
|
||||
|
||||
def calibrate_follower(self):
|
||||
for name, bus in self.follower_arms.items():
|
||||
bus.connect()
|
||||
|
||||
# Disable torque on all motors
|
||||
for motor_id in bus.motors:
|
||||
bus.write("Torque_Enable", 0, motor_id)
|
||||
|
||||
# Then filter out wheels
|
||||
arm_only_dict = {k: v for k, v in bus.motors.items() if not k.startswith("wheel_")}
|
||||
if not arm_only_dict:
|
||||
continue
|
||||
|
||||
original_motors = bus.motors
|
||||
bus.motors = arm_only_dict
|
||||
|
||||
calibration = self.load_or_run_calibration_(name, bus, "follower")
|
||||
bus.set_calibration(calibration)
|
||||
|
||||
bus.motors = original_motors
|
||||
|
||||
def _get_data(self):
|
||||
"""
|
||||
Polls the video socket for up to 15 ms. If data arrives, decode only
|
||||
the *latest* message, returning frames, speed, and arm state. If
|
||||
nothing arrives for any field, use the last known values.
|
||||
"""
|
||||
frames = {}
|
||||
present_speed = {}
|
||||
remote_arm_state_tensor = torch.zeros(6, dtype=torch.float32)
|
||||
|
||||
# Poll up to 15 ms
|
||||
poller = zmq.Poller()
|
||||
poller.register(self.video_socket, zmq.POLLIN)
|
||||
socks = dict(poller.poll(15))
|
||||
if self.video_socket not in socks or socks[self.video_socket] != zmq.POLLIN:
|
||||
# No new data arrived → reuse ALL old data
|
||||
return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
|
||||
|
||||
# Drain all messages, keep only the last
|
||||
last_msg = None
|
||||
while True:
|
||||
try:
|
||||
obs_string = self.video_socket.recv_string(zmq.NOBLOCK)
|
||||
last_msg = obs_string
|
||||
except zmq.Again:
|
||||
break
|
||||
|
||||
if not last_msg:
|
||||
# No new message → also reuse old
|
||||
return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
|
||||
|
||||
# Decode only the final message
|
||||
try:
|
||||
observation = json.loads(last_msg)
|
||||
|
||||
images_dict = observation.get("images", {})
|
||||
new_speed = observation.get("present_speed", {})
|
||||
new_arm_state = observation.get("follower_arm_state", None)
|
||||
|
||||
# Convert images
|
||||
for cam_name, image_b64 in images_dict.items():
|
||||
if image_b64:
|
||||
jpg_data = base64.b64decode(image_b64)
|
||||
np_arr = np.frombuffer(jpg_data, dtype=np.uint8)
|
||||
frame_candidate = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)
|
||||
if frame_candidate is not None:
|
||||
frames[cam_name] = frame_candidate
|
||||
|
||||
# If remote_arm_state is None and frames is None there is no message then use the previous message
|
||||
if new_arm_state is not None and frames is not None:
|
||||
self.last_frames = frames
|
||||
|
||||
remote_arm_state_tensor = torch.tensor(new_arm_state, dtype=torch.float32)
|
||||
self.last_remote_arm_state = remote_arm_state_tensor
|
||||
|
||||
present_speed = new_speed
|
||||
self.last_present_speed = new_speed
|
||||
else:
|
||||
frames = self.last_frames
|
||||
|
||||
remote_arm_state_tensor = self.last_remote_arm_state
|
||||
|
||||
present_speed = self.last_present_speed
|
||||
|
||||
except Exception as e:
|
||||
print(f"[DEBUG] Error decoding video message: {e}")
|
||||
# If decode fails, fall back to old data
|
||||
return (self.last_frames, self.last_present_speed, self.last_remote_arm_state)
|
||||
|
||||
return frames, present_speed, remote_arm_state_tensor
|
||||
|
||||
def _process_present_speed(self, present_speed: dict) -> torch.Tensor:
|
||||
state_tensor = torch.zeros(3, dtype=torch.int32)
|
||||
if present_speed:
|
||||
decoded = {key: MobileManipulator.raw_to_degps(value) for key, value in present_speed.items()}
|
||||
if "1" in decoded:
|
||||
state_tensor[0] = decoded["1"]
|
||||
if "2" in decoded:
|
||||
state_tensor[1] = decoded["2"]
|
||||
if "3" in decoded:
|
||||
state_tensor[2] = decoded["3"]
|
||||
return state_tensor
|
||||
|
||||
def teleop_step(
|
||||
self, record_data: bool = False
|
||||
) -> None | tuple[dict[str, torch.Tensor], dict[str, torch.Tensor]]:
|
||||
def get_observation(self) -> dict[str, np.ndarray]:
|
||||
"""The returned observations do not have a batch dimension."""
|
||||
if not self.is_connected:
|
||||
raise DeviceNotConnectedError("MobileManipulator is not connected. Run `connect()` first.")
|
||||
raise DeviceNotConnectedError(
|
||||
"ManipulatorRobot is not connected. You need to run `robot.connect()`."
|
||||
)
|
||||
|
||||
speed_setting = self.speed_levels[self.speed_index]
|
||||
xy_speed = speed_setting["xy"] # e.g. 0.1, 0.25, or 0.4
|
||||
theta_speed = speed_setting["theta"] # e.g. 30, 60, or 90
|
||||
obs_dict = {}
|
||||
|
||||
# Prepare to assign the position of the leader to the follower
|
||||
arm_positions = []
|
||||
for name in self.leader_arms:
|
||||
pos = self.leader_arms[name].read("Present_Position")
|
||||
pos_tensor = torch.from_numpy(pos).float()
|
||||
# Instead of pos_tensor.item(), use tolist() to convert the entire tensor to a list
|
||||
arm_positions.extend(pos_tensor.tolist())
|
||||
# Read actuators position
|
||||
# TODO(Steven): Base motors should return a vel instead of a pos
|
||||
before_read_t = time.perf_counter()
|
||||
obs_dict[OBS_STATE] = self.actuators.read("Present_Position")
|
||||
self.logs["read_pos_dt_s"] = time.perf_counter() - before_read_t
|
||||
|
||||
# (The rest of your code for generating wheel commands remains unchanged)
|
||||
x_cmd = 0.0 # m/s forward/backward
|
||||
y_cmd = 0.0 # m/s lateral
|
||||
theta_cmd = 0.0 # deg/s rotation
|
||||
if self.pressed_keys["forward"]:
|
||||
x_cmd += xy_speed
|
||||
if self.pressed_keys["backward"]:
|
||||
x_cmd -= xy_speed
|
||||
if self.pressed_keys["left"]:
|
||||
y_cmd += xy_speed
|
||||
if self.pressed_keys["right"]:
|
||||
y_cmd -= xy_speed
|
||||
if self.pressed_keys["rotate_left"]:
|
||||
theta_cmd += theta_speed
|
||||
if self.pressed_keys["rotate_right"]:
|
||||
theta_cmd -= theta_speed
|
||||
|
||||
wheel_commands = self.body_to_wheel_raw(x_cmd, y_cmd, theta_cmd)
|
||||
|
||||
message = {"raw_velocity": wheel_commands, "arm_positions": arm_positions}
|
||||
self.cmd_socket.send_string(json.dumps(message))
|
||||
|
||||
if not record_data:
|
||||
return
|
||||
|
||||
obs_dict = self.capture_observation()
|
||||
|
||||
arm_state_tensor = torch.tensor(arm_positions, dtype=torch.float32)
|
||||
|
||||
wheel_velocity_tuple = self.wheel_raw_to_body(wheel_commands)
|
||||
wheel_velocity_mm = (
|
||||
wheel_velocity_tuple[0] * 1000.0,
|
||||
wheel_velocity_tuple[1] * 1000.0,
|
||||
wheel_velocity_tuple[2],
|
||||
)
|
||||
wheel_tensor = torch.tensor(wheel_velocity_mm, dtype=torch.float32)
|
||||
action_tensor = torch.cat([arm_state_tensor, wheel_tensor])
|
||||
action_dict = {"action": action_tensor}
|
||||
|
||||
return obs_dict, action_dict
|
||||
|
||||
def capture_observation(self) -> dict:
|
||||
"""
|
||||
Capture observations from the remote robot: current follower arm positions,
|
||||
present wheel speeds (converted to body-frame velocities: x, y, theta),
|
||||
and a camera frame.
|
||||
"""
|
||||
if not self.is_connected:
|
||||
raise DeviceNotConnectedError("Not connected. Run `connect()` first.")
|
||||
|
||||
frames, present_speed, remote_arm_state_tensor = self._get_data()
|
||||
|
||||
body_state = self.wheel_raw_to_body(present_speed)
|
||||
|
||||
body_state_mm = (body_state[0] * 1000.0, body_state[1] * 1000.0, body_state[2]) # Convert x,y to mm/s
|
||||
wheel_state_tensor = torch.tensor(body_state_mm, dtype=torch.float32)
|
||||
combined_state_tensor = torch.cat((remote_arm_state_tensor, wheel_state_tensor), dim=0)
|
||||
|
||||
obs_dict = {"observation.state": combined_state_tensor}
|
||||
|
||||
# Loop over each configured camera
|
||||
for cam_name, cam in self.cameras.items():
|
||||
frame = frames.get(cam_name, None)
|
||||
if frame is None:
|
||||
# Create a black image using the camera's configured width, height, and channels
|
||||
frame = np.zeros((cam.height, cam.width, cam.channels), dtype=np.uint8)
|
||||
obs_dict[f"observation.images.{cam_name}"] = torch.from_numpy(frame)
|
||||
# Capture images from cameras
|
||||
for cam_key, cam in self.cameras.items():
|
||||
before_camread_t = time.perf_counter()
|
||||
obs_dict[f"{OBS_IMAGES}.{cam_key}"] = cam.async_read()
|
||||
self.logs[f"read_camera_{cam_key}_dt_s"] = cam.logs["delta_timestamp_s"]
|
||||
self.logs[f"async_read_camera_{cam_key}_dt_s"] = time.perf_counter() - before_camread_t
|
||||
|
||||
return obs_dict
|
||||
|
||||
def send_action(self, action: torch.Tensor) -> torch.Tensor:
|
||||
def send_action(self, action: np.ndarray) -> np.ndarray:
|
||||
# Copied from S100 robot
|
||||
"""Command lekiwi to move to a target joint configuration.
|
||||
|
||||
The relative action magnitude may be clipped depending on the configuration parameter
|
||||
`max_relative_target`. In this case, the action sent differs from original action.
|
||||
Thus, this function always returns the action actually sent.
|
||||
|
||||
Args:
|
||||
action (np.ndarray): array containing the goal positions for the motors.
|
||||
|
||||
Raises:
|
||||
RobotDeviceNotConnectedError: if robot is not connected.
|
||||
|
||||
Returns:
|
||||
np.ndarray: the action sent to the motors, potentially clipped.
|
||||
"""
|
||||
if not self.is_connected:
|
||||
raise DeviceNotConnectedError("Not connected. Run `connect()` first.")
|
||||
raise DeviceNotConnectedError(
|
||||
"ManipulatorRobot is not connected. You need to run `robot.connect()`."
|
||||
)
|
||||
|
||||
# Ensure the action tensor has at least 9 elements:
|
||||
# - First 6: arm positions.
|
||||
# - Last 3: base commands.
|
||||
if action.numel() < 9:
|
||||
# Pad with zeros if there are not enough elements.
|
||||
padded = torch.zeros(9, dtype=action.dtype)
|
||||
padded[: action.numel()] = action
|
||||
action = padded
|
||||
goal_pos = action
|
||||
|
||||
# Extract arm and base actions.
|
||||
arm_actions = action[:6].flatten()
|
||||
base_actions = action[6:].flatten()
|
||||
# Cap goal position when too far away from present position.
|
||||
# /!\ Slower fps expected due to reading from the follower.
|
||||
if self.config.max_relative_target is not None:
|
||||
present_pos = self.actuators.read("Present_Position")
|
||||
goal_pos = ensure_safe_goal_position(goal_pos, present_pos, self.config.max_relative_target)
|
||||
|
||||
x_cmd_mm = base_actions[0].item() # mm/s
|
||||
y_cmd_mm = base_actions[1].item() # mm/s
|
||||
theta_cmd = base_actions[2].item() # deg/s
|
||||
# Send goal position to the actuators
|
||||
# TODO(Steven): Base motors should set a vel instead
|
||||
self.actuators.write("Goal_Position", goal_pos.astype(np.int32))
|
||||
|
||||
# Convert mm/s to m/s for the kinematics calculations.
|
||||
x_cmd = x_cmd_mm / 1000.0 # m/s
|
||||
y_cmd = y_cmd_mm / 1000.0 # m/s
|
||||
return goal_pos
|
||||
|
||||
# Compute wheel commands from body commands.
|
||||
wheel_commands = self.body_to_wheel_raw(x_cmd, y_cmd, theta_cmd)
|
||||
def update_last_observation(self, stop_event):
|
||||
while not stop_event.is_set():
|
||||
with self.observation_lock:
|
||||
self.last_observation = self.get_observation()
|
||||
# TODO(Steven): Consider adding a delay to not starve the CPU
|
||||
|
||||
arm_positions_list = arm_actions.tolist()
|
||||
def run(self):
|
||||
# Copied from run_lekiwi in lekiwi_remote.py
|
||||
# TODO(Steven): Csnsider with, finally
|
||||
if not self.is_connected:
|
||||
self.connect()
|
||||
|
||||
stop_event = threading.Event()
|
||||
observation_thread = threading.Thread(
|
||||
target=self.update_last_observation, args=(stop_event), daemon=True
|
||||
)
|
||||
observation_thread.start()
|
||||
|
||||
message = {"raw_velocity": wheel_commands, "arm_positions": arm_positions_list}
|
||||
self.cmd_socket.send_string(json.dumps(message))
|
||||
last_cmd_time = time.time()
|
||||
logging.info("LeKiwi robot server started. Waiting for commands...")
|
||||
|
||||
return action
|
||||
try:
|
||||
while True:
|
||||
loop_start_time = time.time()
|
||||
|
||||
try:
|
||||
msg = self.cmd_socket.recv_string(zmq.NOBLOCK)
|
||||
data = json.loads(msg)
|
||||
# TODO(Steven): Process data correctly
|
||||
self.send_action(data)
|
||||
last_cmd_time = time.time()
|
||||
# except zmq.Again:
|
||||
# logging.warning("ZMQ again")
|
||||
except Exception as e:
|
||||
logging.warning(f"[ERROR] Message fetching failed: {e}")
|
||||
|
||||
# Watchdog: stop the robot if no command is received for over 0.5 seconds.
|
||||
now = time.time()
|
||||
if now - last_cmd_time > 0.5:
|
||||
# TODO(Steven): Implement stop()
|
||||
#self.stop()
|
||||
pass
|
||||
|
||||
with self.observation_lock:
|
||||
self.observation_socket.send_string(json.dumps(self.last_observation))
|
||||
|
||||
# Ensure a short sleep to avoid overloading the CPU.
|
||||
elapsed = time.time() - loop_start_time
|
||||
time.sleep(
|
||||
max(0.033 - elapsed, 0)
|
||||
) # If robot jitters increase the sleep and monitor cpu load with `top` in cmd
|
||||
except KeyboardInterrupt:
|
||||
print("Shutting down LeKiwi server.")
|
||||
finally:
|
||||
#TODO(Steven): Implement finally
|
||||
pass
|
||||
|
||||
def print_logs(self):
|
||||
# TODO(aliberts): move robot-specific logs logic here
|
||||
pass
|
||||
|
||||
def disconnect(self):
|
||||
if not self.is_connected:
|
||||
raise DeviceNotConnectedError("Not connected.")
|
||||
if self.cmd_socket:
|
||||
stop_cmd = {
|
||||
"raw_velocity": {"left_wheel": 0, "back_wheel": 0, "right_wheel": 0},
|
||||
"arm_positions": {},
|
||||
}
|
||||
self.cmd_socket.send_string(json.dumps(stop_cmd))
|
||||
self.cmd_socket.close()
|
||||
if self.video_socket:
|
||||
self.video_socket.close()
|
||||
if self.context:
|
||||
self.context.term()
|
||||
if PYNPUT_AVAILABLE:
|
||||
self.listener.stop()
|
||||
raise DeviceNotConnectedError(
|
||||
"LeKiwi is not connected. You need to run `robot.connect()` before disconnecting."
|
||||
)
|
||||
# TODO(Steven): Base motors speed should be set to 0
|
||||
# TODO(Steven): Close ZMQ sockets
|
||||
# TODO(Steven): Stop main loop threads
|
||||
self.actuators.disconnect()
|
||||
for cam in self.cameras.values():
|
||||
cam.disconnect()
|
||||
|
||||
self.is_connected = False
|
||||
print("[INFO] Disconnected from remote robot.")
|
||||
|
||||
def __del__(self):
|
||||
if getattr(self, "is_connected", False):
|
||||
self.disconnect()
|
||||
if PYNPUT_AVAILABLE:
|
||||
self.listener.stop()
|
||||
|
||||
@staticmethod
|
||||
def degps_to_raw(degps: float) -> int:
|
||||
steps_per_deg = 4096.0 / 360.0
|
||||
speed_in_steps = abs(degps) * steps_per_deg
|
||||
speed_int = int(round(speed_in_steps))
|
||||
if speed_int > 0x7FFF:
|
||||
speed_int = 0x7FFF
|
||||
if degps < 0:
|
||||
return speed_int | 0x8000
|
||||
else:
|
||||
return speed_int & 0x7FFF
|
||||
|
||||
@staticmethod
|
||||
def raw_to_degps(raw_speed: int) -> float:
|
||||
steps_per_deg = 4096.0 / 360.0
|
||||
magnitude = raw_speed & 0x7FFF
|
||||
degps = magnitude / steps_per_deg
|
||||
if raw_speed & 0x8000:
|
||||
degps = -degps
|
||||
return degps
|
||||
|
||||
def body_to_wheel_raw(
|
||||
self,
|
||||
x_cmd: float,
|
||||
y_cmd: float,
|
||||
theta_cmd: float,
|
||||
wheel_radius: float = 0.05,
|
||||
base_radius: float = 0.125,
|
||||
max_raw: int = 3000,
|
||||
) -> dict:
|
||||
"""
|
||||
Convert desired body-frame velocities into wheel raw commands.
|
||||
|
||||
Parameters:
|
||||
x_cmd : Linear velocity in x (m/s).
|
||||
y_cmd : Linear velocity in y (m/s).
|
||||
theta_cmd : Rotational velocity (deg/s).
|
||||
wheel_radius: Radius of each wheel (meters).
|
||||
base_radius : Distance from the center of rotation to each wheel (meters).
|
||||
max_raw : Maximum allowed raw command (ticks) per wheel.
|
||||
|
||||
Returns:
|
||||
A dictionary with wheel raw commands:
|
||||
{"left_wheel": value, "back_wheel": value, "right_wheel": value}.
|
||||
|
||||
Notes:
|
||||
- Internally, the method converts theta_cmd to rad/s for the kinematics.
|
||||
- The raw command is computed from the wheels angular speed in deg/s
|
||||
using degps_to_raw(). If any command exceeds max_raw, all commands
|
||||
are scaled down proportionally.
|
||||
"""
|
||||
# Convert rotational velocity from deg/s to rad/s.
|
||||
theta_rad = theta_cmd * (np.pi / 180.0)
|
||||
# Create the body velocity vector [x, y, theta_rad].
|
||||
velocity_vector = np.array([x_cmd, y_cmd, theta_rad])
|
||||
|
||||
# Define the wheel mounting angles with a -90° offset.
|
||||
angles = np.radians(np.array([240, 120, 0]) - 90)
|
||||
# Build the kinematic matrix: each row maps body velocities to a wheel’s linear speed.
|
||||
# The third column (base_radius) accounts for the effect of rotation.
|
||||
m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
|
||||
|
||||
# Compute each wheel’s linear speed (m/s) and then its angular speed (rad/s).
|
||||
wheel_linear_speeds = m.dot(velocity_vector)
|
||||
wheel_angular_speeds = wheel_linear_speeds / wheel_radius
|
||||
|
||||
# Convert wheel angular speeds from rad/s to deg/s.
|
||||
wheel_degps = wheel_angular_speeds * (180.0 / np.pi)
|
||||
|
||||
# Scaling
|
||||
steps_per_deg = 4096.0 / 360.0
|
||||
raw_floats = [abs(degps) * steps_per_deg for degps in wheel_degps]
|
||||
max_raw_computed = max(raw_floats)
|
||||
if max_raw_computed > max_raw:
|
||||
scale = max_raw / max_raw_computed
|
||||
wheel_degps = wheel_degps * scale
|
||||
|
||||
# Convert each wheel’s angular speed (deg/s) to a raw integer.
|
||||
wheel_raw = [MobileManipulator.degps_to_raw(deg) for deg in wheel_degps]
|
||||
|
||||
return {"left_wheel": wheel_raw[0], "back_wheel": wheel_raw[1], "right_wheel": wheel_raw[2]}
|
||||
|
||||
def wheel_raw_to_body(
|
||||
self, wheel_raw: dict, wheel_radius: float = 0.05, base_radius: float = 0.125
|
||||
) -> tuple:
|
||||
"""
|
||||
Convert wheel raw command feedback back into body-frame velocities.
|
||||
|
||||
Parameters:
|
||||
wheel_raw : Dictionary with raw wheel commands (keys: "left_wheel", "back_wheel", "right_wheel").
|
||||
wheel_radius: Radius of each wheel (meters).
|
||||
base_radius : Distance from the robot center to each wheel (meters).
|
||||
|
||||
Returns:
|
||||
A tuple (x_cmd, y_cmd, theta_cmd) where:
|
||||
x_cmd : Linear velocity in x (m/s).
|
||||
y_cmd : Linear velocity in y (m/s).
|
||||
theta_cmd : Rotational velocity in deg/s.
|
||||
"""
|
||||
# Extract the raw values in order.
|
||||
raw_list = [
|
||||
int(wheel_raw.get("left_wheel", 0)),
|
||||
int(wheel_raw.get("back_wheel", 0)),
|
||||
int(wheel_raw.get("right_wheel", 0)),
|
||||
]
|
||||
|
||||
# Convert each raw command back to an angular speed in deg/s.
|
||||
wheel_degps = np.array([MobileManipulator.raw_to_degps(r) for r in raw_list])
|
||||
# Convert from deg/s to rad/s.
|
||||
wheel_radps = wheel_degps * (np.pi / 180.0)
|
||||
# Compute each wheel’s linear speed (m/s) from its angular speed.
|
||||
wheel_linear_speeds = wheel_radps * wheel_radius
|
||||
|
||||
# Define the wheel mounting angles with a -90° offset.
|
||||
angles = np.radians(np.array([240, 120, 0]) - 90)
|
||||
m = np.array([[np.cos(a), np.sin(a), base_radius] for a in angles])
|
||||
|
||||
# Solve the inverse kinematics: body_velocity = M⁻¹ · wheel_linear_speeds.
|
||||
m_inv = np.linalg.inv(m)
|
||||
velocity_vector = m_inv.dot(wheel_linear_speeds)
|
||||
x_cmd, y_cmd, theta_rad = velocity_vector
|
||||
theta_cmd = theta_rad * (180.0 / np.pi)
|
||||
return (x_cmd, y_cmd, theta_cmd)
|
||||
|
||||
|
||||
class LeKiwi:
|
||||
def __init__(self, motor_bus):
|
||||
"""
|
||||
Initializes the LeKiwi with Feetech motors bus.
|
||||
"""
|
||||
self.motor_bus = motor_bus
|
||||
self.motor_ids = ["left_wheel", "back_wheel", "right_wheel"]
|
||||
|
||||
# Initialize motors in velocity mode.
|
||||
self.motor_bus.write("Lock", 0)
|
||||
self.motor_bus.write("Mode", [1, 1, 1], self.motor_ids)
|
||||
self.motor_bus.write("Lock", 1)
|
||||
print("Motors set to velocity mode.")
|
||||
|
||||
def read_velocity(self):
|
||||
"""
|
||||
Reads the raw speeds for all wheels. Returns a dictionary with motor names:
|
||||
"""
|
||||
raw_speeds = self.motor_bus.read("Present_Speed", self.motor_ids)
|
||||
return {
|
||||
"left_wheel": int(raw_speeds[0]),
|
||||
"back_wheel": int(raw_speeds[1]),
|
||||
"right_wheel": int(raw_speeds[2]),
|
||||
}
|
||||
|
||||
def set_velocity(self, command_speeds):
|
||||
"""
|
||||
Sends raw velocity commands (16-bit encoded values) directly to the motor bus.
|
||||
The order of speeds must correspond to self.motor_ids.
|
||||
"""
|
||||
self.motor_bus.write("Goal_Speed", command_speeds, self.motor_ids)
|
||||
|
||||
def stop(self):
|
||||
"""Stops the robot by setting all motor speeds to zero."""
|
||||
self.motor_bus.write("Goal_Speed", [0, 0, 0], self.motor_ids)
|
||||
print("Motors stopped.")
|
||||
|
|
|
|||
|
|
@ -64,6 +64,7 @@ class KeyboardTeleop(Teleoperator):
|
|||
|
||||
@property
|
||||
def action_feature(self) -> dict:
|
||||
#TODO(Steven): Verify this is correct
|
||||
return {
|
||||
"dtype": "float32",
|
||||
"shape": (len(self.arm),),
|
||||
|
|
@ -75,6 +76,12 @@ class KeyboardTeleop(Teleoperator):
|
|||
return {}
|
||||
|
||||
def connect(self) -> None:
|
||||
#TODO(Steven): Consider instead of raising a warning and then returning the status
|
||||
# if self.is_connected:
|
||||
# logging.warning(
|
||||
# "ManipulatorRobot is already connected. Do not run `robot.connect()` twice."
|
||||
# )
|
||||
# return self.is_connected
|
||||
if self.is_connected:
|
||||
raise DeviceAlreadyConnectedError(
|
||||
"ManipulatorRobot is already connected. Do not run `robot.connect()` twice."
|
||||
|
|
|
|||
|
|
@ -379,7 +379,7 @@ def control_robot(cfg: ControlPipelineConfig):
|
|||
elif isinstance(cfg.control, ReplayControlConfig):
|
||||
replay(robot, cfg.control)
|
||||
elif isinstance(cfg.control, RemoteRobotConfig):
|
||||
from lerobot.common.robots.lekiwi.lekiwi_remote import run_lekiwi
|
||||
from lerobot.common.robots.lekiwi.old_lekiwi_remote import run_lekiwi
|
||||
|
||||
run_lekiwi(cfg.robot)
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue