XRoboToolkit: A Cross-Platform Framework for Robot Teleoperation

2025-07-31

AI Summary

Current robot teleoperation data collection methods suffer from poor scalability, complex deployment, and low data fidelity. To address these limitations, this work introduces the first cross-platform teleoperation framework compliant with the OpenXR standard, enabling low-latency stereoscopic visual feedback and multimodal motion tracking—including head-mounted displays, handheld controllers, hand gestures, and external sensors—within XR environments. Leveraging a modular architecture and an optimized inverse kinematics algorithm, the framework significantly improves operational precision and hardware-software compatibility, seamlessly interfacing with diverse real-world robotic platforms (e.g., Franka Emika, Unitree Go2) and simulation environments (e.g., Isaac Sim, PyBullet). It successfully collects high-fidelity demonstration data for fine-grained manipulation tasks, enabling training of vision-language-action foundation models with strong generalization capability. This work establishes a reusable technical foundation for constructing large-scale, high-fidelity robot skill datasets.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

The rapid advancement of Vision-Language-Action models has created an urgent need for large-scale, high-quality robot demonstration datasets. Although teleoperation is the predominant method for data collection, current approaches suffer from limited scalability, complex setup procedures, and suboptimal data quality. This paper presents XRoboToolkit, a cross-platform framework for extended reality based robot teleoperation built on the OpenXR standard. The system features low-latency stereoscopic visual feedback, optimization-based inverse kinematics, and support for diverse tracking modalities including head, controller, hand, and auxiliary motion trackers. XRoboToolkit's modular architecture enables seamless integration across robotic platforms and simulation environments, spanning precision manipulators, mobile robots, and dexterous hands. We demonstrate the framework's effectiveness through precision manipulation tasks and validate data quality by training VLA models that exhibit robust autonomous performance.

Problem

• Need for scalable high-quality robot demonstration datasets
• Current teleoperation methods lack scalability and data quality
• Framework enables cross-platform robot teleoperation with diverse tracking

Innovation

• Cross-platform OpenXR framework for robot teleoperation
• Low-latency stereoscopic feedback and inverse kinematics
• Modular architecture supporting diverse robots and trackers