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arXiv · 2505.01998

A Synergistic Framework of Nonlinear Acoustic Computing and Reinforcement Learning for Real-World Human-Robot Interaction

Xiaoliang Chen·Xin Yu·Le Chang·Yunhe Huang·Jiashuai He·Shibo Zhang·Jin Li·Likai Lin·Ziyu Zeng·Xianling Tu·Shuyu Zhang
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Abstract

This paper introduces a novel framework integrating nonlinear acoustic computing and reinforcement learning to enhance advanced human-robot interaction under complex noise and reverberation. Leveraging physically informed wave equations (e.g., Westervelt, KZK), the approach captures higher-order phenomena such as harmonic generation and shock formation. By embedding these models in a reinforcement learning-driven control loop, the system adaptively optimizes key parameters (e.g., absorption, beamforming) to mitigate multipath interference and non-stationary noise. Experimental evaluations, covering far-field localization, weak signal detection, and multilingual speech recognition, demonstrate that this hybrid strategy surpasses traditional linear methods and purely data-driven baselines, achieving superior noise suppression, minimal latency, and robust accuracy in demanding real-world scenarios. The proposed system demonstrates broad application prospects in AI hardware, robot, machine audition, artificial audition, and brain-machine interfaces.

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BibTeXRIS

Xiaoliang Chen, Xin Yu, Le Chang, Yunhe Huang, Jiashuai He, Shibo Zhang, Jin Li, Likai Lin, Ziyu Zeng, Xianling Tu, Shuyu Zhang. 2025-05-04. A Synergistic Framework of Nonlinear Acoustic Computing and Reinforcement Learning for Real-World Human-Robot Interaction. https://arxiv.org/abs/2505.01998

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