Multimodal Agent AI: A Survey of Recent Advances and Future Directions

In recent years, Multimodal Agent AI (MAA) has emerged as a pivotal area of research, promising to rev olutionize human-machine interactions. Agent AI systems, capable of perceiving and responding to inputs from multiple modalities (e.g., language, vision, audio), have demonstrated remarkable progress in understanding complex environments and executing intricate tasks. This survey comprehensively reviews the state-of-the-art developments in MAA, examining its fundamental concepts, key techniques, and applications across diverse domains. We first introduce the basics of Agent AI and its multimodal interaction capabilities. We then delve into the core technologies that enable Agents to perform task planning, decision-making, and multi-sensory fusion. Furthermore, we focus on exploring the various applications of MAA in robotics, healthcare, gaming, and beyond. Additionally, we mainly devote ourselves to analyzing the challenges and limitations faced by current systems and propose promising research directions for future improvements, including human AI collaboration, online learning method improvement and etc. By synthesizing existing knowledge and highlighting open questions, this survey aims to provide a comprehensive roadmap for researchers and practitioners in the field of MAA.