Structuring Manipulation Tasks for More Efficient Learning

In the future, we want to create robots with the robustness and versatility to operate in unstructured and everyday environments. To achieve this goal, robots will need to learn manipulation skills that can be applied to a wide range of objects and task scenarios. In this talk, I will be presenting recent work from my lab on structuring manipulation tasks for more efficient learning. I will begin by discussing how modularity can be used to break down challenging manipulation tasks to learn general object-centric solutions. I will then focus on the question of: what to learn? I will discuss how robots can use model-based reasoning to identify relevant context parameters for adapting skills as well as determining when to learn a skill. I will conclude by discussing how robots can use interactions and multimodal sensing to learn manipulation-oriented representations of different materials.