Touch2Touch: 物体操作のための触覚センサ間クロスモーダル生成
Touch2Touch: Cross-Modal Tactile Generation for Object Manipulation
拡散モデルを用いてGelSlimとSoft Bubbleという異なる触覚センサ間で触覚信号を相互変換し、センサ固有のアルゴリズムを別センサの信号に適用可能にする手法を提案。
著者: Samanta Rodriguez, Yiming Dou, Miquel Oller, Andrew Owens, Nima Fazeli
分類: cs.RO
原文アブストラクト
Today's touch sensors come in many shapes and sizes. This has made it challenging to develop general-purpose touch processing methods since models are generally tied to one specific sensor design. We address this problem by performing cross-modal prediction between touch sensors: given the tactile signal from one sensor, we use a generative model to estimate how the same physical contact would be perceived by another sensor. This allows us to apply sensor-specific methods to the generated signal. We implement this idea by training a diffusion model to translate between the popular GelSlim and Soft Bubble sensors. As a downstream task, we perform in-hand object pose estimation using GelSlim sensors while using an algorithm that operates only on Soft Bubble signals. The dataset, the code, and additional details can be found at https://www.mmintlab.com/research/touch2touch/.
