linfengzhao.txt

Enabling decision-making agents, such as robots and AI systems, to operate effectively in complex open-world environments poses significant challenges. This talk presents an approach that integrates structured learning into planning and world modeling to enhance data efficiency and generalization. By incorporating structure into end-to-end learning, agents can jointly learn representations and plan actions, allowing them to build world models on-the-fly and adapt to new situations. I explore two primary paradigms of world representation: lossless abstractions, which retain full environmental complexity through methods like symmetric and compositional representations; and lossy abstractions, which simplify planning for computational efficiency but require grounding abstract plans to real-world execution, such as symbolic-based abstraction. By combining these structured learning approaches, I aim to overcome the limitations of traditional planning methods and end-to-end learning, leading to more scalable and adaptable decision-making agents in complex environments.