Robot Learning: Generalization in Planning

This lecture explores the challenging process of generalization across sequences of actions or states in robot learning, focusing on how to learn representations that enable plans to generalize. Discover the connections between system identification, sim-to-real transfer, meta reinforcement learning, and in-context learning while examining the limitations and generality of each approach. Gain insights into data-intensive processes that help robots adapt their planning capabilities across different scenarios.