[Paper] TempoVLA: Learning Speed-Controllable Vision-Language-Action Policies
Source: arXiv - 2606.06491v1
Overview
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:
- Variable‑Speed Trajectory Augmentation (VSTA) – a data‑side technique that re‑times demonstrations to any target speed by merging or splitting actions while preserving motion semantics.
- Speed‑conditioning mechanism – a model‑side approach that feeds the desired 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.
Key Contributions
- cs.RO
- cs.AI
Methodology
Please refer to the full paper for detailed methodology.
Practical Implications
This research contributes to the advancement of cs.RO.
Authors
- Dong Jing
- Jingchen Nie
- Tianqi Zhang
- Jiaqi Liu
- Huaxiu Yao
- Zhiwu Lu
- Mingyu Ding
Paper Information
- arXiv ID: 2606.06491v1
- Categories: cs.RO, cs.AI
- Published: June 4, 2026
- PDF: Download PDF