Recent progress in reinforcement learning (RL) and tactile sensing has significantly advanced dexterous manipulation. However, these methods often utilize simplified tactile signals due to the gap between tactile simulation and the real world. We introduce a sensor model for tactile skin that enables zero-shot sim-to-real transfer of ternary shear and binary normal forces. Using this model, we develop an RL policy that leverages sliding contact for dexterous in-hand translation. We conduct extensive real-world experiments to assess how tactile sensing facilitates policy adaptation to various unseen object properties and robot hand orientations. We demonstrate that our 3-axis tactile policies consistently outperform baselines that use only shear forces, only normal forces, or only proprioception. Website: https://jessicayin.github.io/tactile-skin-rl/

翻译：强化学习与触觉传感的最新进展显著提升了灵巧操作能力。然而，由于触觉仿真与现实世界之间存在差异，现有方法常采用简化的触觉信号。本文提出一种触觉皮肤传感器模型，能够实现三轴剪切力与二值法向力的零样本仿真到现实迁移。基于该模型，我们开发了一种利用滑动接触进行灵巧手内平移操作的强化学习策略。通过大量真实世界实验，我们评估了触觉感知如何促进策略适应各种未见过的物体属性与机械手朝向。实验表明，我们的三轴触觉策略在性能上持续优于仅使用剪切力、仅使用法向力或仅使用本体感知的基线方法。项目网站：https://jessicayin.github.io/tactile-skin-rl/