TBAP: Tapping-Based Auditory Perception for Identifying Container Materials

Abstract

In this study, in order to address the robotic auditory perception problem, we propose a novel framework for object material recognition of common containers, which combines deep learning with active auditory perception to achieve breakthrough results. We developed a modular robotic system for acoustic data acquisition that employs a hybrid mechanism of vertical translation and horizontal rotation that is capable of performing full-scale tapping in three dimensions. The system is capable of creating an acoustic dataset consisting of 50 containers made of five materials, which improves the data acquisition efficiency by 93.9% compared to manual operations. In addition, we propose an end-to-end transfer learning model, TBAP, which is trained on a crawler-generated pre-training dataset and 50 real scene samples, and achieves a recognition accuracy of 91.0% for unseen materials. To improve reliability, we design a dynamic confidence assessment mechanism that generates confidence indices through probability distribution analysis and feature stability assessment to support robust robot decision-making. Experimental results show that the framework greatly improves data acquisition efficiency while maintaining high recognition accuracy, providing a valuable tool for advancing acoustic perception research.