| title | World-to-Wrist: Task-Conditioned Future Wrist Modeling for Fine-Grained Robot Manipulation |
|---|---|
| authors | Yuhao Pan, Haosong Peng, Zhengshen Zhang, Zhengyang Yan, Yalun Dai, Fushuo Huo, Chujie Wang, Tianyu Qi, Xiucheng Wang, Nan Cheng, Wenchao Xu |
| date | 2026-08-05 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2608.05369 |
| upvotes | 18 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| primer | Vision-language-action models |
| abstract | Vision-language-action (VLA) models often treat main-view and wrist-view observations as parallel visual inputs, overlooking their distinct roles in robot manipulation. Fine-grained manipulation, however, benefits from anticipating how wrist-local interactions may evolve under the global task context. To address this limitation, we present World-to-Wrist VLA (W2-VLA), a VLA model for fine-grained robot manipulation with task-conditioned future wrist modeling. Given current multi-view observations and a task instruction, W2-VLA contextualizes a set of latent modeling tokens as a compact interface between the vision-language model and the wrist predictor. Conditioned on this interface and the observed wrist history, the predictor forecasts future wrist latents, which are transformed into future-aware context for action prediction. In addition, we introduce W2-CoT, a synthesis pipeline that produces structured annotations describing manipulation progress, physical transition cues, and wrist-local evidence. These annotations provide auxiliary supervision that shapes the task-conditioned latent interface. Experiments on LIBERO, RoboTwin 2.0, and real-world manipulation tasks demonstrate improved fine-grained and contact-sensitive manipulation across both single-arm and bimanual settings, while maintaining action-generation rates above 80 Hz. |
cite
@misc{pan2026world,
title={World-to-Wrist: Task-Conditioned Future Wrist Modeling for Fine-Grained Robot Manipulation},
author={Yuhao Pan and Haosong Peng and Zhengshen Zhang and Zhengyang Yan and Yalun Dai and Fushuo Huo and Chujie Wang and Tianyu Qi and Xiucheng Wang and Nan Cheng and Wenchao Xu},
year={2026},
eprint={2608.05369},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2608.05369}
}