触覚ファンクタセット：触覚データセットのニューラル陰関数表現
Tactile Functasets: Neural Implicit Representations of Tactile Datasets
触覚センサの生画像をニューラル陰関数で再構成し、コンパクトでセンサ間の汎化が可能な表現を提案。手先物体姿勢推定で画像ベース手法を上回る性能を示した。
著者: Sikai Li, Samanta Rodriguez, Yiming Dou, Andrew Owens, Nima Fazeli
分類: cs.RO
原文アブストラクト
Modern incarnations of tactile sensors produce high-dimensional raw sensory feedback such as images, making it challenging to efficiently store, process, and generalize across sensors. To address these concerns, we introduce a novel implicit function representation for tactile sensor feedback. Rather than directly using raw tactile images, we propose neural implicit functions trained to reconstruct the tactile dataset, producing compact representations that capture the underlying structure of the sensory inputs. These representations offer several advantages over their raw counterparts: they are compact, enable probabilistically interpretable inference, and facilitate generalization across different sensors. We demonstrate the efficacy of this representation on the downstream task of in-hand object pose estimation, achieving improved performance over image-based methods while simplifying downstream models. We release code, demos and datasets at https://www.mmintlab.com/tactile-functasets.
