Active Acoustic Sensing for Robot Manipulation

TL;DR

This paper introduces an active acoustic sensing method for robot manipulation that uses resonant properties of objects to improve perception of internal states and contact interactions, enhancing manipulation capabilities.

Contribution

It develops a novel active acoustic sensing approach combining hardware design, simulation, and evaluation to aid robot manipulation tasks.

Findings

Effective sensing of object internal states demonstrated

Hardware and simulation models validated the approach

Potential for improved object recognition and pose estimation

Abstract

Perception in robot manipulation has been actively explored with the goal of advancing and integrating vision and touch for global and local feature extraction. However, it is difficult to perceive certain object internal states, and the integration of visual and haptic perception is not compact and is easily biased. We propose to address these limitations by developing an active acoustic sensing method for robot manipulation. Active acoustic sensing relies on the resonant properties of the object, which are related to its material, shape, internal structure, and contact interactions with the gripper and environment. The sensor consists of a vibration actuator paired with a piezo-electric microphone. The actuator generates a waveform, and the microphone tracks the waveform's propagation and distortion as it travels through the object. This paper presents the sensing principles, hardware design, simulation development, and evaluation of physical and simulated sensory data under different conditions as a proof-of-concept. This work aims to provide fundamentals on a useful tool for downstream robot manipulation tasks using active acoustic sensing, such as object recognition, grasping point estimation, object pose estimation, and external contact formation detection.