巧みな視触覚ポリシー学習のための3D触覚の正準表現と力に基づく事前学習
Canonical Representation and Force-Based Pretraining of 3D Tactile for Dexterous Visuo-Tactile Policy Learning
3D触覚データを正準表現に変換し、力に基づく自己教師あり事前学習を行うことで、巧みな手による接触の多い操作タスクの成功率を向上させた研究。
著者: Tianhao Wu, Jinzhou Li, Jiyao Zhang, Mingdong Wu, Hao Dong
分類: cs.RO
原文アブストラクト
Tactile sensing plays a vital role in enabling robots to perform fine-grained, contact-rich tasks. However, the high dimensionality of tactile data, due to the large coverage on dexterous hands, poses significant challenges for effective tactile feature learning, especially for 3D tactile data, as there are no large standardized datasets and no strong pretrained backbones. To address these challenges, we propose a novel canonical representation that reduces the difficulty of 3D tactile feature learning and further introduces a force-based self-supervised pretraining task to capture both local and net force features, which are crucial for dexterous manipulation. Our method achieves an average success rate of 78% across four fine-grained, contact-rich dexterous manipulation tasks in real-world experiments, demonstrating effectiveness and robustness compared to other methods. Further analysis shows that our method fully utilizes both spatial and force information from 3D tactile data to accomplish the tasks. The codes and videos can be viewed at https://3dtacdex.github.io.
