用稀疏机器人执行器操控声波，实现聚焦或抑制。
Acoustic Wave Manipulation Through Sparse Robotic Actuation
- 基于数据驱动学习，通过稀疏控制信号调节声波传播。
- 在聚焦与抑制声能任务中优于现有深度学习方法，计算更高效。
- 适合对声学操控、智能材料设计感兴趣的科研与工程人员。
近年来，机器人技术、控制理论与机器学习的进步推动了物体操纵领域的突破，包括利用深度神经网络表征部分可观测的系统动态，以及使用稀疏控制信号进行有效控制。本文探索更普遍的问题：利用空间稀疏执行器，通过机器人对部分观测的声波进行操控。该问题在新型人工材料设计、超声切割工具、能量收集等领域具有重要应用前景。我们提出一种高效的基于数据驱动的机器人学习方法，可灵活实现指定区域的声能聚焦或抑制。相比现有基于学习的偏微分方程系统操控方法，本方法在解的质量和计算复杂度上均表现更优；同时在所测试任务上，性能可媲美声学研究中的经典半解析方法。项目代码及视频演示已公开：https://gladisor.github.io/waves/
原文摘要 · Abstract (English)
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/.
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