視覚ベース触覚センサによる能動的テクスチャ認識で重要な要素
What Matters for Active Texture Recognition With Vision-Based Tactile Sensors
視覚ベース触覚センサを用いた布地テクスチャ認識のための能動的サンプリング戦略を検討し、データ拡張とドロップアウト率が精度に大きく影響することを示した。
著者: Alina Böhm, Tim Schneider, Boris Belousov, Alap Kshirsagar, Lisa Lin, Katja Doerschner, Knut Drewing, Constantin A. Rothkopf, Jan Peters
分類: cs.RO, cs.LG
原文アブストラクト
This paper explores active sensing strategies that employ vision-based tactile sensors for robotic perception and classification of fabric textures. We formalize the active sampling problem in the context of tactile fabric recognition and provide an implementation of information-theoretic exploration strategies based on minimizing predictive entropy and variance of probabilistic models. Through ablation studies and human experiments, we investigate which components are crucial for quick and reliable texture recognition. Along with the active sampling strategies, we evaluate neural network architectures, representations of uncertainty, influence of data augmentation, and dataset variability. By evaluating our method on a previously published Active Clothing Perception Dataset and on a real robotic system, we establish that the choice of the active exploration strategy has only a minor influence on the recognition accuracy, whereas data augmentation and dropout rate play a significantly larger role. In a comparison study, while humans achieve 66.9% recognition accuracy, our best approach reaches 90.0% in under 5 touches, highlighting that vision-based tactile sensors are highly effective for fabric texture recognition.
