XRoboToolkit: A Cross-Platform Framework for Robot Teleoperation
This addresses the problem of limited scalability and data quality in robot teleoperation for researchers and practitioners in robotics and AI, though it appears incremental as it builds on existing teleoperation methods with cross-platform improvements.
The paper tackles the need for scalable, high-quality robot demonstration datasets by introducing XRoboToolkit, a cross-platform teleoperation framework that enables seamless integration across robotic platforms and simulation environments, resulting in robust autonomous performance for trained Vision-Language-Action models.
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.