視覚型触覚センサのための転移学習
Transfer learning for vision-based tactile sensing
内部カメラ画像からセンサ表面の法線力分布を再構成する視覚型触覚センサにおいて、複数センサ間で学習済み知識を転移するキャリブレーション手法を提案し、学習時間とデータ量を削減した。
著者: Carmelo Sferrazza, Raffaello D'Andrea
分類: cs.RO
原文アブストラクト
Due to the complexity of modeling the elastic properties of materials, the use of machine learning algorithms is continuously increasing for tactile sensing applications. Recent advances in deep neural networks applied to computer vision make vision-based tactile sensors very appealing for their high-resolution and low cost. A soft optical tactile sensor that is scalable to large surfaces with arbitrary shape is discussed in this paper. A supervised learning algorithm trains a model that is able to reconstruct the normal force distribution on the sensor's surface, purely from the images recorded by an internal camera. In order to reduce the training times and the need for large datasets, a calibration procedure is proposed to transfer the acquired knowledge across multiple sensors while maintaining satisfactory performance.
