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

TL;DR

This paper presents a new framework combining nonlinear acoustic modeling with reinforcement learning to improve human-robot interaction in noisy, reverberant environments, achieving superior noise suppression and robustness.

Contribution

It introduces an innovative integration of physically informed nonlinear acoustic models with reinforcement learning for adaptive control in complex auditory scenarios.

Findings

Outperforms traditional linear and data-driven methods in noise suppression.

Achieves robust localization and speech recognition in real-world conditions.

Demonstrates broad applicability in AI hardware and brain-machine interfaces.

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.