Interpreting dynamic, heterogeneous multimedia commands with real-time responsiveness is critical for Human-Robot Interaction. We present VA-FastNavi-MARL, a framework that aligns asynchronous audio-visual inputs into a unified latent representation. By treating diverse instructions as a distribution of navigable goals via Meta-Reinforcement Learning, our method enables rapid adaptation to unseen directives with negligible inference overhead. Unlike approaches bottlenecked by heavy sensory processing, our modality-agnostic stream ensures seamless, low-latency control. Validation on a multi-arm workspace confirms that VA-FastNavi-MARL significantly outperforms baselines in sample efficiency and maintains robust, real-time execution even under noisy multimedia streams.
@article{arxiv.2604.03998,
title = {VA-FastNavi-MARL: Real-Time Robot Control with Multimedia-Driven Meta-Reinforcement Learning},
author = {Yang Zhang and Shengxi Jing and Fengxiang Wang and Yuan Feng and Hong Wang},
journal= {arXiv preprint arXiv:2604.03998},
year = {2026}
}
Comments
Accepted to the 2026 IEEE International Conference on Multimedia and Expo (ICME 2026)