Recent Vision-Language-Action (VLA) models equipped with Flow Matching (FM) action heads achieve state-of-the-art performance in complex robot manipulation. However, the multi-step iterative ODE solving required by FM introduces inference latency that precludes responsive physical control. While current acceleration efforts optimize the Vision-Language Model (VLM) backbone, the action head bottleneck remains overlooked. To address this, we propose ProbeFlow, a training-free adaptive inference framework tai- lored for continuous robotic control. By evaluating geometric trajectory complexity via the cosine similarity between initial and lookahead velocity vectors, ProbeFlow dynamically sched- ules integration steps to prune redundant network evaluations. On the MetaWorld benchmark, it accelerates action decoding by 14.8x (reducing average steps from N = 50 to 2.6) and cuts end-to-end system latency by 2.8x without compromising the manipulation success rate. On the long-horizon LIBERO benchmark, the probe automatically allocates a denser schedule to navigate semantic bottlenecks, effectively resolving the flow solver delay. Real-world physical deployments confirm that ProbeFlow successfully mitigates action decoding latency while ensuring execution stability, offering a highly practical solution for low-latency continuous generative policies.

翻译：近期配备流匹配(Flow Matching, FM)动作头的视觉-语言-动作(Vision-Language-Action, VLA)模型在复杂机器人操作任务中达到了最先进性能。然而，FM所需的迭代多步常微分方程求解引入了推理延迟，从而阻碍了响应式物理控制。尽管当前加速工作主要优化视觉-语言模型(Vision-Language Model, VLM)骨干网络，但动作头瓶颈问题仍被忽视。为此，我们提出ProbeFlow——一种专为连续机器人控制设计的无训练自适应推理框架。通过基于初始速度向量与前瞻速度向量之间的余弦相似度评估几何轨迹复杂度，ProbeFlow能够动态调度积分步数以精简冗余网络评估。在MetaWorld基准测试中，该方法将动作解码加速14.8倍(平均步数从N=50降至2.6)，并将端到端系统延迟降低2.8倍，同时不牺牲操作成功率。在长视界LIBERO基准测试中，探测机制自动分配更密集的调度步数以穿越语义瓶颈，有效解决流求解器延迟问题。实际物理部署验证表明，ProbeFlow在保证执行稳定性的同时成功缓解了动作解码延迟，为低延迟连续生成策略提供了高度实用的解决方案。