跨模态导航与多智能体强化学习
Cross-Modal Navigation with Multi-Agent Reinforcement Learning
AI总结：
本文提出CRONA框架，通过多智能体强化学习实现跨模态导航，利用辅助信念和集中式多模态批评者提升协作效率，实验表明多智能体方法在视觉-听觉导航中优于单智能体基线。
摘要：
鲁棒的具身导航依赖互补的感知线索。然而，高质量且对齐的多模态数据在实践中难以获得。训练单一大模型也具有挑战性，因为丰富的多模态输入导致复杂的表示并显著扩大了策略空间。跨模态协作中的轻量级模态专用智能体提供了一种可扩展的范式。它允许灵活的部署和并行执行，同时保留每个模态的优势。本文提出CRONA，一种用于跨模态导航的多智能体强化学习框架。CRONA通过利用与控制相关的辅助信念和具有全局状态的集中式多模态批评者来改进协作。在视觉-听觉导航任务中的实验表明，多智能体方法在性能和效率上显著优于单智能体基线。我们发现，在显著线索下，有限模态的同质协作足以完成短距离导航；在智能体间具有互补模态的异质协作通常高效且有效；而在大型复杂环境中导航则需要更丰富的多模态感知和增加模型容量。
英文摘要：
Robust embodied navigation relies on complementary sensory cues. However, high-quality and well-aligned multi-modal data is often difficult to obtain in practice. Training a monolithic model is also challenging as rich multi-modal inputs induce complex representations and substantially enlarge the policy space. Cross-modal collaboration among lightweight modality-specialized agents offers a scalable paradigm. It enables flexible deployment and parallel execution, while preserving the strength of each modality. In this paper, we propose \textbf{CRONA}, a Multi-Agent Reinforcement Learning (MARL) framework for \textbf{Cro}ss-Modal \textbf{Na}vigation. CRONA improves collaboration by leveraging control-relevant auxiliary beliefs and a centralized multi-modal critic with global state. Experiments on visual-acoustic navigation tasks show that multi-agent methods significantly improve performance and efficiency over single-agent baselines. We find that homogeneous collaboration with limited modalities is sufficient for short-range navigation under salient cues; heterogeneous collaboration among agents with complementary modalities is generally efficient and effective; and navigation in large, complex environments requires both richer multi-modal perception and increased model capacity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
