触覚センサ間の対照学習による統一表現の獲得
Contrastive Touch-to-Touch Pretraining
異なる触覚センサ（GelSlimとSoft Bubble）で同じ物体を触ったペアデータを使い、対照学習で共通の埋め込み空間を学習。姿勢推定や分類の事前学習に有効で、あるセンサで学習したモデルを別のセンサに追加学習なしで適用できることを示した。
著者: Samanta Rodriguez, Yiming Dou, William van den Bogert, Miquel Oller, Kevin So, Andrew Owens, Nima Fazeli
分類: cs.RO
原文アブストラクト
Today's tactile sensors have a variety of different designs, making it challenging to develop general-purpose methods for processing touch signals. In this paper, we learn a unified representation that captures the shared information between different tactile sensors. Unlike current approaches that focus on reconstruction or task-specific supervision, we leverage contrastive learning to integrate tactile signals from two different sensors into a shared embedding space, using a dataset in which the same objects are probed with multiple sensors. We apply this approach to paired touch signals from GelSlim and Soft Bubble sensors. We show that our learned features provide strong pretraining for downstream pose estimation and classification tasks. We also show that our embedding enables models trained using one touch sensor to be deployed using another without additional training. Project details can be found at https://www.mmintlab.com/research/cttp/.
