VA-FastNavi-MARL: Real-Time Robot Control with Multimedia-Driven Meta-Reinforcement Learning
This addresses the challenge of real-time responsiveness in human-robot interaction, though it appears incremental as it builds on existing Meta-Reinforcement Learning methods for a specific domain.
The paper tackled the problem of interpreting dynamic, heterogeneous multimedia commands for real-time human-robot interaction by aligning audio-visual inputs into a unified latent representation using Meta-Reinforcement Learning, resulting in significant outperformance over baselines in sample efficiency and robust, real-time execution under noisy streams.
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.