Robot control model anticipates motion without generating future video| title | PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations |
|---|---|
| authors | Hanqing Wang, Zhaotian Cai, Ruichuan An, Bohan Zeng, Tao Zhang, Yilin Chen, Xu Jiang, Kaiquan Chen, Chongyang Wu, Haoran Li, Fan Yang, Mingan Lin, Wentao Zhang, Zenan Zhou, Youzheng Wu |
| date | 2026-10-27 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2610.37381 |
| affiliation | Jingdong (China) |
| upvotes | 152 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| dispatch | Robot control model anticipates motion without generating future video |
| primer | Vision-language-action models World models |
| abstract | Vision--Language--Action (VLA) models generate robot actions from visual observations and language instructions, but their understanding of physical dynamics remains limited. Video world models can capture scene dynamics, yet generating future videos introduces high latency into the control loop. We introduce PDRVLA, whose lightweight Physical Dynamics Module extracts physical tokens from vision--language features to condition action generation without online video generation. The module learns through direct prediction of future RGB, depth, and optical-flow features, with auxiliary supervision through a frozen pretrained video world model. Training relies entirely on publicly available data, expanded through human-to-robot synthesis, subtask annotation, and embodied chain-of-thought (ECoT) annotation. Across four simulation benchmarks, PDRVLA achieves success rates of 99.3% on LIBERO, 89.9% on LIBERO-Plus, 93.6% on RoboTwin~2.0, and 81.5% on SimplerEnv. Ablations show improved future prediction with world-model supervision and evaluate how subtask conditioning, future object-pose supervision, and ECoT training affect action success. |
cite
@misc{wang2026pdrvla,
title={PDRVLA: A Vision-Language-Action Model with Physical Dynamics Representations},
author={Hanqing Wang and Zhaotian Cai and Ruichuan An and Bohan Zeng and Tao Zhang and Yilin Chen and Xu Jiang and Kaiquan Chen and Chongyang Wu and Haoran Li and Fan Yang and Mingan Lin and Wentao Zhang and Zenan Zhou and Youzheng Wu},
year={2026},
eprint={2610.37381},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2610.37381}
}