触覚ニューラル逆レンダリング
Tactile Neural De-rendering
触覚信号のみから生成モデルを用いて物体の局所3D表現を再構成し、姿勢推定と不確かさの定量化を可能にする手法を提案。
著者: Jose A. Eyzaguirre, Miquel Oller, Nima Fazeli
分類: cs.RO
原文アブストラクト
Tactile sensing has proven to be an invaluable tool for enhancing robotic perception, particularly in scenarios where visual data is limited or unavailable. However, traditional methods for pose estimation using tactile data often rely on intricate modeling of sensor mechanics or estimation of contact patches, which can be cumbersome and inherently deterministic. In this work, we introduce Tactile Neural De-rendering, a novel approach that leverages a generative model to reconstruct a local 3D representation of an object based solely on its tactile signature. By rendering the object as though perceived by a virtual camera embedded at the fingertip, our method provides a more intuitive and flexible representation of the tactile data. This 3D reconstruction not only facilitates precise pose estimation but also allows for the quantification of uncertainty, providing a robust framework for tactile-based perception in robotics.
