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

• URL: http://arxiv.org/abs/2505.01998v2
• Date: Tue, 06 May 2025 16:09:59 GMT
• Title: A Synergistic Framework of Nonlinear Acoustic Computing and Reinforcement Learning for Real-World Human-Robot Interaction
• Authors: Xiaoliang Chen, Xin Yu, Le Chang, Yunhe Huang, Jiashuai He, Shibo Zhang, Jin Li, Likai Lin, Ziyu Zeng, Xianling Tu, Shuyu Zhang,
• Abstract summary: This paper introduces a novel framework integrating nonlinear acoustic computing and reinforcement learning to enhance human-robot interaction under complex noise and reverberation.<n>The proposed system demonstrates broad application prospects in AI hardware, robot, machine audition, artificial audition, and brain-machine interfaces.
• Score: 15.759904937490832
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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