VA-FastNavi-MARL: Real-Time Robot Control with Multimedia-Driven Meta-Reinforcement Learning

2026-04-05

AI Summary

This work addresses the challenges of multimodal asynchrony, high latency, and poor generalization in robotic response under dynamic, heterogeneous multimedia commands. To overcome these limitations, the authors propose a modality-agnostic, lightweight streaming architecture that aligns asynchronous audio-visual instructions into a unified latent space and leverages meta-reinforcement learning to model diverse instructions as navigable goal distributions. The approach achieves robust real-time responses to noisy inputs with negligible inference overhead and substantially improves sample efficiency. Experimental results on multi-arm manipulation tasks demonstrate that the method maintains real-time control while significantly outperforming baseline approaches in noise robustness and generalization capability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

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.

Problem

• Human-Robot Interaction
• Multimedia Commands
• Real-Time Control
• Meta-Reinforcement Learning
• Latent Representation

Innovation

• Meta-Reinforcement Learning
• Multimodal Alignment
• Real-Time Robot Control
• Modality-Agnostic Representation
• Sample Efficiency