ViTacTip：透明スキンと生体模倣チップを備えたロボットマニピュレーション用マルチモーダルセンサの設計とベンチマーク
Design and Benchmarking of A Multi-Modality Sensor for Robotic Manipulation with GAN-Based Cross-Modality Interpretation
透明スキンと生体模倣チップで視覚・近接・触覚・力覚を同時に取得する小型センサViTacTipを設計し、GANによるモダリティ間変換と多タスク学習でその性能を実証した論文。
著者: Dandan Zhang, Wen Fan, Jialin Lin, Haoran Li, Qingzheng Cong, Weiru Liu, Nathan F. Lepora, Shan Luo
分類: cs.RO, eess.SP
原文アブストラクト
In this paper, we present the design and benchmark of an innovative sensor, ViTacTip, which fulfills the demand for advanced multi-modal sensing in a compact design. A notable feature of ViTacTip is its transparent skin, which incorporates a `see-through-skin' mechanism. This mechanism aims at capturing detailed object features upon contact, significantly improving both vision-based and proximity perception capabilities. In parallel, the biomimetic tips embedded in the sensor's skin are designed to amplify contact details, thus substantially augmenting tactile and derived force perception abilities. To demonstrate the multi-modal capabilities of ViTacTip, we developed a multi-task learning model that enables simultaneous recognition of hardness, material, and textures. To assess the functionality and validate the versatility of ViTacTip, we conducted extensive benchmarking experiments, including object recognition, contact point detection, pose regression, and grating identification. To facilitate seamless switching between various sensing modalities, we employed a Generative Adversarial Network (GAN)-based approach. This method enhances the applicability of the ViTacTip sensor across diverse environments by enabling cross-modality interpretation.
