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arXiv · 2406.17932

SonicSense: Object Perception from In-Hand Acoustic Vibration

Jiaxun Liu·Boyuan Chen
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Abstract

We introduce SonicSense, a holistic design of hardware and software to enable rich robot object perception through in-hand acoustic vibration sensing. While previous studies have shown promising results with acoustic sensing for object perception, current solutions are constrained to a handful of objects with simple geometries and homogeneous materials, single-finger sensing, and mixing training and testing on the same objects. SonicSense enables container inventory status differentiation, heterogeneous material prediction, 3D shape reconstruction, and object re-identification from a diverse set of 83 real-world objects. Our system employs a simple but effective heuristic exploration policy to interact with the objects as well as end-to-end learning-based algorithms to fuse vibration signals to infer object properties. Our framework underscores the significance of in-hand acoustic vibration sensing in advancing robot tactile perception.

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BibTeXRIS

Jiaxun Liu, Boyuan Chen. 2024-06-25. SonicSense: Object Perception from In-Hand Acoustic Vibration. https://arxiv.org/abs/2406.17932

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