Acoustic Wave Manipulation Through Sparse Robotic Actuation

2025-02-12

AI Summary

This work addresses the problem of spatially sparse control of acoustic wavefields, aiming to focus or suppress energy in partially observable regions using a minimal number of physical actuators—enabling applications in programmable acoustic metasurfaces, precision ultrasonic cutting, and directional energy harvesting. We propose the first integrated framework combining sparse robotic actuation with data-driven learning: acoustic propagation is modeled via partial differential equations (PDEs), while sparse optimal control is synergistically coupled with deep neural networks to capture nonlinear system responses. The method achieves high accuracy—comparable to semi-analytical acoustic solvers—while significantly improving computational efficiency over state-of-the-art PDE-learning approaches. To foster reproducibility and adoption, we publicly release open-source code and interactive visualizations. This work establishes a new paradigm for programmable acoustic interfaces grounded in physics-informed, sparse, and learning-augmented control.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Learning to Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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/.

Problem

• Manipulating acoustic waves with sparse robotic actuation
• Focusing or suppressing scattered acoustic energy
• Developing efficient data-driven methods for robot learning

Innovation

• Deep neural networks represent dynamics
• Sparse control signals effective manipulation
• Data-driven method for acoustic control