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About the Talk: 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, ACID, and Ditto for learning actionable visual representations from embodied interactions. I will then present BUDS, MAPLE, and VIOLA for scaffolding long-horizon tasks with sensorimotor skills. Finally, I will conclude with discussions on future research directions toward building scalable robot autonomy.
About the Speaker: Yuke Zhu is an Assistant Professor in the Computer Science department of UT-Austin, where he directs the Robot Perception and Learning Lab. His research lies at the intersection of robotics, machine learning, and computer vision. He received his Master’s and Ph.D. degrees from Stanford University. His research works have won several awards and nominations, including the Best Conference Paper Award in ICRA 2019, Outstanding Learning Paper at ICRA 2022, and Best Paper Finalists in IROS 2019, 2021. He is the recipient of the NSF CAREER Award and the Amazon Reward Awards.
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