# TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous Manipulation

arXiv: https://arxiv.org/abs/2607.07287 · HTML: https://mlsanity.com/paper/2607.07287/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Jul 2026.

## Built on by (most cited first, 9 of 9)

- [ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation](https://mlsanity.com/paper/2607.22530/) (2026)
- [What Can Latent World Models Know? Physical Parameter Identifiability in Multimodal Predictive Representations](https://mlsanity.com/paper/2607.27017/) (2026)
- [TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction](https://mlsanity.com/paper/2607.28391/) (2026)
- [TacForcing: Streaming Action Generation with Execution-Time Tactile Feedback](https://mlsanity.com/paper/2608.25798/) (2026)
- [DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation](https://mlsanity.com/paper/2609.20649/) (2026)
- [ME-Dex 1.0: Bringing Heterogeneous Tactile Sensing into World Action Modeling](https://mlsanity.com/paper/2609.21449/) (2026)
- [Unified Visual-Tactile-Action Modeling from Human Demonstrations for Dexterous Manipulation](https://mlsanity.com/paper/2609.34182/) (2026)
- [Dexterous Tactile World Model](https://mlsanity.com/paper/2609.34286/) (2026)
- [TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model](https://mlsanity.com/paper/2610.00638/) (2026)

## Abstract (arXiv)

Dexterous manipulation in everyday environments requires both anticipation and reaction: a robot must predict how contact should evolve while rapidly correcting local errors caused by slip, misalignment, unstable grasping, or force mismatch. Vision and language provide semantic and geometric guidance, but they cannot reliably reveal hidden contact states such as force, slip, and contact stability. Although tactile sensing exposes these physical cues, most existing policies treat touch as a low-frequency observation stream within a monolithic action model, coupling slow task reasoning, action generation, and fast contact feedback in a single loop. We introduce TouchWorld, a predictive-and-reactive tactile foundation model for dexterous manipulation. TouchWorld uses a hierarchical policy that separates vision-language subtask planning, tactile world-model prediction, visuo-tactile goal-conditioned action generation, and high-frequency tactile residual refinement. A High-Level Planning Layer produces executable subtasks and predicts tactile subgoals; a Visuo-Tactile Goal-Conditioned Policy generates nominal action chunks; and a Tactile-Conditioned Refinement Policy performs online residual correction using recent tactile and proprioceptive feedback. By using touch as both a predictive contact reference and a fast feedback signal, TouchWorld preserves the semantic generalization of vision-language-action policies while improving local contact adaptation. Across six long-horizon and contact-rich dexterous manipulation tasks, TouchWorld achieves 65.0% success in the clean setting and 53.7% success under human perturbations, outperforming the strongest baseline by 15.7 and 18.5 percentage points, respectively.