  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.
