Overcoming Dynamics-Blindness: Training-Free Pace-and-Path Correction for VLA Models
Vision-Language-Action (VLA) models achieve remarkable flexibility and generalization beyond classical control paradigms. However, most prevailing VLAs are trained under a single-frame observation paradigm, which leaves them structurally blind to temporal dynamics. Consequently, these models degrade severely in non-stationary scenarios, even when trained or finetuned on dynamic datasets. Existing approaches either require expensive retraining or suffer from latency bottlenecks and poor temporal consistency across action chunks. We propose Pace-and-Path Correction, a training-free, closed-form inference-time operator that wraps any chunked-action VLA. From a single quadratic cost, joint mi...
| Authors | Yanyan Zhang, Chaoda Song, Vikash Singh et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 May 2026 |
| Last updated at source | 14 May 2026 |
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