In deployment of the VLA models to real-world robotic tasks, execution speed matters. In previous work arXiv:2510.26742 we analyze how to make neural computation of VLAs on GPU fast. However, we leave the question of how to actually deploy the VLA system on the real robots open. In this report we describe a set of practical techniques to achieve the end-to-end result of running a VLA-driven robot at an impressive speed in real world tasks that require both accuracy and dexterity. The stack of technology ranges across calibration, planning & control, and learning based method to identify optimal execution speed. In the tasks we show, the robot even executes in a speed on par with casual human operation and approaching the hardware limit of our lightweight arm. The unaccelerated videos and inference traces are provided in https://dexmal.github.io/realtime-vla-v2/.

翻译：在将VLA模型部署至真实机器人任务时，执行速度至关重要。在先前工作arXiv:2510.26742中，我们分析了如何使VLA模型在GPU上的神经计算达到快速。然而，关于如何将VLA系统实际部署于真实机器人的问题仍悬而未决。本报告描述了一套实用技术体系，旨在实现端到端结果：在同时要求精度与灵巧性的真实场景中，以惊人速度驱动VLA机器人。该技术栈涵盖标定、规划与控制，以及基于学习方法的最优执行速度识别。在展示的任务中，机器人执行速度甚至达到非刻意快放的人类操作水平，并接近我们轻量级机械臂的硬件极限。未加速视频与推理轨迹数据提供于https://dexmal.github.io/realtime-vla-v2/。