MemCtrl: Using MLLMs as Active Memory Controllers on Embodied Agents

This paper investigates the use of multimodal large language models (MLLMs) as active memory controllers for embodied agents. This approach could significantly enhance the autonomy and adaptability of robots and other embodied AI systems in dynamic environments.