Design and Benchmarking of A Multi-Modality Sensor for Robotic Manipulation with GAN-Based Cross-Modality Interpretation

2025-01-04

AI Summary

To address the challenges of multimodal sensor fusion and weak cross-modal semantic understanding in robotic perception, this paper proposes ViTacTip—a compact tactile-vision fusion sensor. Our method integrates optical imaging and tactile force sensing via a novel transparent “see-through skin” architecture enabling physically co-located sensing. A bioinspired microstructured tactile tip simultaneously captures high-resolution visual, proximity, tactile, and 3D force signals. Furthermore, we introduce a conditional generative adversarial network (cGAN)-driven cross-modal interpretation framework that enables seamless feature mapping and joint semantic understanding across vision, touch, and force modalities. Evaluated on object recognition, contact point localization, pose regression, and grating discrimination, ViTacTip consistently outperforms unimodal baselines. Notably, it achieves a 92.7% accuracy in the joint classification of object hardness, material, and surface texture—demonstrating significant advances in integrated multimodal perception for robotics.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

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.

Problem

• Multi-sensor Fusion
• Robot Perception
• Adaptability Enhancement

Innovation

• ViTacTip Sensor
• GAN Model Integration
• Biomimetic Design