titleVLA-Δ: Learning Physical State Transition for Vision-Language-Action Models
authorsQi Lu, Jiarun Li, Ce Zhang, Miao Pan, Yi Pan, Xu Wang, Wenqi Zhang, Xuhong Zhang
date2026-10-15
venuearXiv (preprint, not peer reviewed)
idarXiv:2610.21306
affiliationZJU-OmniAI
upvotes20 (Hugging Face Daily Papers, 2026-11-01)
linksabs pdf hf code
primerVision-language-action models
abstractVision-language-action (VLA) models transfer semantic knowledge acquired through vision-language pretraining to robot control, achieving strong performance across various benchmarks. However, most VLAs directly map observations to demonstrated actions without explicit supervision of intermediate physical state changes, making them prone to overfitting observation-action correspondences. Some methods introduce intermediate reasoning or future-state prediction in language, visual, or latent spaces to guide action generation, but do not explicitly supervise the physical state transition itself. We therefore propose VLA-Δ, a general framework that uses physical changes between current and future states as explicit learning targets. To support this objective, we construct a large-scale pretraining dataset and derive physical state-transition annotations covering task-relevant semantics and visual motion changes, providing more direct supervision for continuous action generation. During inference, the model generates actions directly from current observations without additional inference overhead. VLA-Δ achieves performance competitive with the state of the art on LIBERO and RoboTwin, and outperforms π_{0.5} in real-robot experiments. Our code is available at https://github.com/ZJU-OmniAI/VLA-Delta

cite

@misc{lu2026vla,
  title={VLA-Δ: Learning Physical State Transition for Vision-Language-Action Models},
  author={Qi Lu and Jiarun Li and Ce Zhang and Miao Pan and Yi Pan and Xu Wang and Wenqi Zhang and Xuhong Zhang},
  year={2026},
  eprint={2610.21306},
  archivePrefix={arXiv},
  url={https://arxiv.org/abs/2610.21306}
}