触覚等変性を利用した残差回転補正
Residual Rotation Correction using Tactile Equivariance
触覚センサのSO(2)対称性を利用し、少ないデータで物体の回転を補正する等変ネットワークを提案。実機で未学習の把持姿勢にも汎化する。
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著者: Yizhe Zhu, Zhang Ye, Boce Hu, Haibo Zhao, Yu Qi, Dian Wang, Robert Platt
分類: cs.RO
原文アブストラクト
Visuotactile policy learning augments vision-only policies with tactile input, facilitating contact-rich manipulation. However, the high cost of tactile data collection makes sample efficiency the key requirement for developing visuotactile policies. We present EquiTac, a framework that exploits the inherent SO(2) symmetry of in-hand object rotation to improve sample efficiency and generalization for visuotactile policy learning. EquiTac first reconstructs surface normals from raw RGB inputs of vision-based tactile sensors, so rotations of the normal vector field correspond to in-hand object rotations. An SO(2)-equivariant network then predicts a residual rotation action that augments a base visuomotor policy at test time, enabling real-time rotation correction without additional reorientation demonstrations. On a real robot, EquiTac accurately achieves robust zero-shot generalization to unseen in-hand orientations with very few training samples, where baselines fail even with more training data. To our knowledge, this is the first tactile learning method to explicitly encode tactile equivariance for policy learning, yielding a lightweight, symmetry-aware module that improves reliability in contact-rich tasks.
