Large language models (LLMs) are beginning to automate reward design for dexterous manipulation. However, no prior work has considered tactile sensing, which is known to be critical for human-like dexterity. We present Text2Touch, bringing LLM-crafted rewards to the challenging task of multi-axis in-hand object rotation with real-world vision based tactile sensing in palm-up and palm-down configurations. Our prompt engineering strategy scales to over 70 environment variables, and sim-to-real distillation enables successful policy transfer to a tactile-enabled fully actuated four-fingered dexterous robot hand. Text2Touch significantly outperforms a carefully tuned human-engineered baseline, demonstrating superior rotation speed and stability while relying on reward functions that are an order of magnitude shorter and simpler. These results illustrate how LLM-designed rewards can significantly reduce the time from concept to deployable dexterous tactile skills, supporting more rapid and scalable multimodal robot learning. Project website: https://hpfield.github.io/text2touch-website
@article{arxiv.2509.07445,
title = {Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions},
author = {Harrison Field and Max Yang and Yijiong Lin and Efi Psomopoulou and David Barton and Nathan F. Lepora},
journal= {arXiv preprint arXiv:2509.07445},
year = {2025}
}