Personalizing Embodied Multimodal Large Language Model Agents over Long-term User Interactions

This paper proposes POLAR, a multimodal memory-augmented framework for personalized embodied agents over long-term user interactions. POLAR organizes prior interactions into a multimodal knowledge graph, capturing semantic and episodic memory to guide embodied task execution.