Generic Python API Guide

TeleopXR provides a pure Python API for integrating WebXR teleoperation into your own projects. This guide covers how to retrieve raw state data, handle button events, visualize robot models, and utilize the modular IK stack.

1. Raw XRState Value Retrieval

To receive real-time data from the XR headset, use the Teleop class. The state is delivered as a dictionary, which can be validated into a structured XRState object for easy access.

import numpy as np
from teleop_xr import Teleop, TeleopSettings
from teleop_xr.messages import XRState

def on_xr_update(pose: np.ndarray, info: dict):
    """
    Args:
        pose: 4x4 matrix representing the primary end-effector target.
        info: Raw dictionary containing all device poses and inputs.
    """
    try:
        # Convert dictionary to structured Pydantic model
        state = XRState.model_validate(info)

        for device in state.devices:
            print(f"Device: {device.role}")
            print(f"Position: {device.pose.position}")

            if device.gamepad:
                print(f"Buttons: {len(device.gamepad.buttons)}")
                print(f"Axes: {device.gamepad.axes}")
    except Exception as e:
        print(f"Error parsing state: {e}")

# Configure and start
settings = TeleopSettings(port=4443, input_mode="controller")
teleop = Teleop(settings=settings)
teleop.subscribe(on_xr_update)
teleop.run()

2. Button Event System

TeleopXR includes an EventProcessor that detects complex interactions like double-presses and long-presses, abstracting away the raw boolean polling.

from teleop_xr.events import EventProcessor, EventSettings, XRButton, ButtonEventType

# 1. Initialize Processor
event_settings = EventSettings(
    double_press_threshold_ms=300,
    long_press_threshold_ms=500
)
event_processor = EventProcessor(event_settings)

# 2. Register Callbacks
def on_trigger_down(event):
    print(f"Trigger pressed on {event.controller}!")

def on_primary_double_press(event):
    print("A/X button double-pressed!")

event_processor.on_button_down(button=XRButton.TRIGGER, callback=on_trigger_down)
event_processor.on_double_press(button=XRButton.BUTTON_PRIMARY, callback=on_primary_double_press)

# 3. Integrate with Teleop
teleop = Teleop(TeleopSettings())
teleop.subscribe(event_processor.process)
teleop.run()

3. Robot Model Visualization

You can stream a URDF-based robot model to the WebXR frontend for real-time visualization. This allows the operator to see a "ghost" or "digital twin" of the robot in VR.

from teleop_xr import Teleop, TeleopSettings
from teleop_xr.config import RobotVisConfig

# Configure the robot model
vis_config = RobotVisConfig(
    urdf_path="path/to/my_robot.urdf",
    mesh_path="path/to/meshes",  # Optional: folder containing STLs/OBJs
    model_scale=1.0,
    initial_rotation_euler=[0, 0, 0]
)

settings = TeleopSettings(robot_vis=vis_config)
teleop = Teleop(settings=settings)

# To update the visualizer's joint positions:
# Use teleop.publish_joint_state(joint_positions_dict)
teleop.run()

4. Camera & Video Streaming

TeleopXR can transmit live camera streams from your robot or workstation into the WebXR scene. Define VideoStreamConfig objects for each camera and pass them inside TeleopSettings.video_config so the frontend automatically subscribes to the tracks.

from teleop_xr import Teleop, TeleopSettings
from teleop_xr.video_stream import VideoStreamConfig

stream_configs = [
    VideoStreamConfig(id="head", device=0, width=1280, height=720, fps=30),
    VideoStreamConfig(id="hand", device=1, width=1280, height=720, fps=30),
]

settings = TeleopSettings(video_config={"streams": [cfg.model_dump() for cfg in stream_configs]})
teleop = Teleop(settings=settings)
teleop.run()

If you need to feed frames from ROS topics, file playback, or other sources, subclass ExternalVideoSource or call put_frame on it directly to inject numpy arrays into the stream manager.

5. IK / Teleoperation Algorithm

The modular IK stack allows you to map 6DoF XR poses directly to robot joint configurations using a JAX-powered optimizer.

Modular Components

• BaseRobot: Abstract class where you define your robot's kinematics and cost functions.
• PyrokiSolver: High-performance solver that executes the optimization.
• IKController: Manages engagement logic (deadman switch) and relative motion snapshots.

Implementation Example

import numpy as np
from teleop_xr.ik import IKController, PyrokiSolver, BaseRobot

class MyRobotModel(BaseRobot):
    # Implement the abstract interface for your specific URDF
    ...

robot = MyRobotModel()
solver = PyrokiSolver(robot)
controller = IKController(robot, solver)

# Initialize state
q_current = robot.get_default_config()

def teleop_loop(xr_state):
    global q_current

    # IKController handles relative motion and deadman logic
    # Returns optimized joint positions (q)
    q_new = controller.step(xr_state, q_current)

    q_current = q_new
    send_to_hardware(q_current)

For a full reference of classes and methods, please refer to the API Reference.