做机器人操作或 VLA 研究的团队终于有了一个能按需调速的方案——TempoVLA 让单一模型同时覆盖快速移动和慢速精确操作，值得关注其动态速度控制的实际效果。
现有视觉-语言-动作模型（VLA）只能以训练数据中的固定速度执行任务，无法在低风险阶段快速移动、高风险阶段慢速精确操作。TempoVLA 提出了一种速度可控的解决方案，通过可变速度轨迹增强（VSTA）和模型侧条件机制，让单一 VLA 模型能够根据显式速度条件调整执行速度。实验表明，TempoVLA 在仿真和真实任务中实现了双向灵活速度控制，且 VSTA 通过更好的数据利用提升了默认速度下的性能。与大型多模态模型结合后，TempoVLA 还能实现动态速度控制，在低风险阶段加速、高风险阶段减速。这项工作解决了机器人操作中速度自适应这一关键痛点。
TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
Robot manipulation alternates between low-risk transit phases that call for fast execution and high-risk contact stages that demand slow, precise motion. Yet existing Vision-Language-Action models (VLAs) only inherit a single fixed speed from training demonstrations. Prior efforts to accelerate VLAs through model compression, KV-cache reuse, or reinforcement learning only shift the policy from one fixed speed to another, and leave deceleration almost unexplored. We observe that the magnitude of each predicted action already governs how fast the robot moves, opening a direct route to controllable execution speed. We turn this observation into TempoVLA, a single VLA whose execution speed is controlled by an explicit condition. TempoVLA combines two coupled components. (1) A data-side Variable-Speed Trajectory Augmentation (VSTA) that re-times demonstration to any target speed by merging or splitting actions while preserving its motion semantics. (2) A model-side conditioning mechanism that feeds the speed to the policy. Statistics show that VSTA reaches the requested speed with negligible motion error. Experiments in simulation and on real-world tasks demonstrate that TempoVLA achieves flexible speed control in both directions, while VSTA additionally boosts the default $1\times$ performance via better data utilization. Furthermore, by cooperating with a large multimodal model, TempoVLA realizes dynamic speed control, accelerating through low-risk phases and decelerating for high-risk ones.