せん断力と垂直力を感知する触覚スキンを用いた手内平行移動の学習
Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing
触覚スキンのセンサモデルを提案し、せん断力と垂直力を用いた強化学習方策で手内での物体操作を実現、実機実験で有効性を示した。
著者: Jessica Yin, Haozhi Qi, Jitendra Malik, James Pikul, Mark Yim, Tess Hellebrekers
分類: cs.RO, cs.LG
原文アブストラクト
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/
