触覚表現学習への視覚基盤モデルの適用検討
Investigating Vision Foundational Models for Tactile Representation Learning
異なる触覚センサの読み取りを画像に変換し、視覚基盤モデルで扱うことで、センサに依存しない触覚表現学習を実現する手法を提案した。
著者: Ben Zandonati, Ruohan Wang, Ruihan Gao, Yan Wu
分類: cs.RO
原文アブストラクト
Tactile representation learning (TRL) equips robots with the ability to leverage touch information, boosting performance in tasks such as environment perception and object manipulation. However, the heterogeneity of tactile sensors results in many sensor- and task-specific learning approaches. This limits the efficacy of existing tactile datasets, and the subsequent generalisability of any learning outcome. In this work, we investigate the applicability of vision foundational models to sensor-agnostic TRL, via a simple yet effective transformation technique to feed the heterogeneous sensor readouts into the model. Our approach recasts TRL as a computer vision (CV) problem, which permits the application of various CV techniques for tackling TRL-specific challenges. We evaluate our approach on multiple benchmark tasks, using datasets collected from four different tactile sensors. Empirically, we demonstrate significant improvements in task performance, model robustness, as well as cross-sensor and cross-task knowledge transferability with limited data requirements.
