Design and Benchmarking of a Multimodality Sensor for Robotic Manipulation With GAN-Based Cross-Modality Interpretation

Abstract

In this article, we present the design and benchmark of an innovative sensor, ViTacTip, which fulfills the demand for advanced multimodal 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 multimodal capabilities of ViTacTip, we developed a multitask 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.