Embodied AI Agents: Modeling the World
It addresses the challenge of creating more human-like embodied AI agents for applications in robotics and virtual systems, but it is incremental as it builds on existing concepts of world modeling.
This paper tackles the problem of enabling AI agents to interact with environments and users by developing world models, which integrate multimodal perception, reasoning, and memory to enhance autonomous task performance.
This paper describes our research on AI agents embodied in visual, virtual or physical forms, enabling them to interact with both users and their environments. These agents, which include virtual avatars, wearable devices, and robots, are designed to perceive, learn and act within their surroundings, which makes them more similar to how humans learn and interact with the environments as compared to disembodied agents. We propose that the development of world models is central to reasoning and planning of embodied AI agents, allowing these agents to understand and predict their environment, to understand user intentions and social contexts, thereby enhancing their ability to perform complex tasks autonomously. World modeling encompasses the integration of multimodal perception, planning through reasoning for action and control, and memory to create a comprehensive understanding of the physical world. Beyond the physical world, we also propose to learn the mental world model of users to enable better human-agent collaboration.