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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

TL;DR

This work addresses robust human-robot interaction in loud and reverberant settings by integrating nonlinear acoustic computing with reinforcement learning. It fuses physics-based wave models, notably the Westervelt equation and the KZK equation, with deep learning for feature estimation and PPO-based RL for end-to-end adaptation, enabling real-time optimization of parameters such as attenuation, beamforming, and boundary reflections. The framework demonstrates superior performance in far-field localization, weak-signal detection, multilingual speech recognition, and low-latency processing, aided by components like AzeroVEP, AzeroTTS, AzeroASR, and AzeroGPT. The approach holds significant practical impact for AI hardware, hearing aids, smart microphones, and BMI-integrated auditory systems, enabling robust, personalized, and context-aware human-robot interaction in real-world environments.

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.

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

TL;DR

This work addresses robust human-robot interaction in loud and reverberant settings by integrating nonlinear acoustic computing with reinforcement learning. It fuses physics-based wave models, notably the Westervelt equation and the KZK equation, with deep learning for feature estimation and PPO-based RL for end-to-end adaptation, enabling real-time optimization of parameters such as attenuation, beamforming, and boundary reflections. The framework demonstrates superior performance in far-field localization, weak-signal detection, multilingual speech recognition, and low-latency processing, aided by components like AzeroVEP, AzeroTTS, AzeroASR, and AzeroGPT. The approach holds significant practical impact for AI hardware, hearing aids, smart microphones, and BMI-integrated auditory systems, enabling robust, personalized, and context-aware human-robot interaction in real-world environments.

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.
Paper Structure (30 sections, 14 equations, 11 figures, 8 tables)

This paper contains 30 sections, 14 equations, 11 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Theoretical Foundations and Innovative Framework
  3. Nonlinear Acoustic Modeling
  4. The Westervelt Equation
  5. Derivation of the KZK Equation
  6. Integration with Machine Learning
  7. Deep Learning Synergy
  8. Reinforcement Learning Fusion
  9. Algorithms, Models, and Technical Indicators
  10. AI Noise Reduction and Echo Cancellation Algorithms
  11. Full-Scenario Sensor Array Sound Source Localization Technology
  12. Multilingual Speech Interaction Full-Chain Technology
  13. Multimodal Large Model Technology System
  14. Experimental Results and Performance Analysis
  15. Noise Suppression Capability
  16. ...and 15 more sections

Figures (11)

  • Figure 1: Nonlinear Acoustic Computational Model and Auditory Enhancement Framework Driven by Brain-Machine Interface
  • Figure 2: Fusion Framework of Nonlinear Acoustic Computation and Machine Learning
  • Figure 3: AI Acoustic Noise Reduction Model (in the Direction of Voice)
  • Figure 4: AI Acoustic Array Sound Source Localization and Soundprint Detection Model
  • Figure 5: AI Short-term Voice Cloning Model
  • ...and 6 more figures