| title | N_0-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation |
|---|---|
| authors | NeoteAI Team, Fudan TEAI Team |
| date | 2026-07-26 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2607.23783 |
| affiliation | NeoteAI |
| upvotes | 48 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | We present N_0-TWAM, a tactile-native world-action model for contact-rich manipulation that predicts both future vision and future contact. To our knowledge, it is the first tactile world-action model trained at large scale, and it shows strong capability on contact-rich tasks. We pre-train N_0-TWAM at large scale with visuo-tactile joint training over tactile-rich demonstrations spanning six embodiments and 450 tasks. We use NeoForce, a unified force-based tactile representation, to form a physically grounded contact signal that conditions action generation. To improve long-horizon and multi-stage manipulation, we introduce tactile contact events for task staging and advance through them during execution. For real-time efficiency, we adopt an asymmetric Mixture-of-Transformers architecture that pairs a full-width expert for video prediction with slim experts for downstream action and tactile prediction. Evaluations on both real and simulated benchmarks justify the capabilities of N_0-TWAM across a range of contact-rich tasks, and demonstrate the benefit of data scaling for precise tactile and action prediction. In summary, N_0-TWAM endows a world-action model with predictive capabilities to foresee vision, touch and action, building a solid foundation for fine-grained manipulation on open contact-rich tasks. The codebase and model checkpoints will be made publicly available to foster further research and development in tactile-enabled robotic manipulation. |
cite
@misc{team2026n,
title={N_0-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation},
author={NeoteAI Team and Fudan TEAI Team},
year={2026},
eprint={2607.23783},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2607.23783}
}