Guest
Yuke Zhu UT Austin February 18, 2022 Recent years have witnessed great strides in deep learning for robotics. Yet, state-of-the-art robot learning algorithms still fall short of generalization and robustness for widespread deployment. In this talk, I argue that the key to building the next generation of deployable autonomous robots is integrating scientific advances in AI with engineering disciplines of building scalable systems. Specifically, I will discuss the role of abstraction and composition in building robot autonomy and introduce our recent work on developing a compositional autonomy stack through state-action abstractions. I will talk about GIGA and Ditto for learning actionable object representations from embodied interactions. I will then present BUDS and MAPLE for scaffolding long-horizon tasks with sensorimotor skills. Finally, I will conclude with discussions on future research directions towards building scalable robot autonomy. View the full playlist:
