Acoustic Wave Manipulation Through Sparse Robotic Actuation

TL;DR

This paper presents a data-driven robotic method for manipulating acoustic waves using sparse actuators, enabling focused energy control with improved efficiency and competitiveness against classical methods.

Contribution

It introduces a novel, efficient learning-based approach for acoustic wave manipulation with sparse control, outperforming existing methods in quality and computational complexity.

Findings

Better solution quality than state-of-the-art methods

Lower computational complexity in wave manipulation tasks

Competitive with classical semi-analytical acoustics methods

Abstract

Recent advancements in robotics, control, and machine learning have facilitated progress in the challenging area of object manipulation. These advancements include, among others, the use of deep neural networks to represent dynamics that are partially observed by robot sensors, as well as effective control using sparse control signals. In this work, we explore a more general problem: the manipulation of acoustic waves, which are partially observed by a robot capable of influencing the waves through spatially sparse actuators. This problem holds great potential for the design of new artificial materials, ultrasonic cutting tools, energy harvesting, and other applications. We develop an efficient data-driven method for robot learning that is applicable to either focusing scattered acoustic energy in a designated region or suppressing it, depending on the desired task. The proposed method is better in terms of a solution quality and computational complexity as compared to a state-of-the-art learning based method for manipulation of dynamical systems governed by partial differential equations. Furthermore our proposed method is competitive with a classical semi-analytical method in acoustics research on the demonstrated tasks. We have made the project code publicly available, along with a web page featuring video demonstrations: https://gladisor.github.io/waves/.