Past Event: Babuška Forum

Pathway to Generalist Robots

Prof. Yuke Zhu, Assistant Professor, Computer Science, UT Austin

Abstract

We have witnessed remarkable advancements in developing generalist models in AI and Machine Learning. These models, such as OpenAI's ChatGPT, can be applied to various tasks in open domains. The creation of these generalist AI models primarily relies on the trinity of powerful algorithms, big data, and advanced computing hardware. The compelling capabilities of these models have intrigued us robot learning researchers to ask: How close are we to building generalist robots capable of performing everyday tasks? In this talk, I will present our lab's research on building principles and methods toward general-purpose robot autonomy in the wild.

Biography

Yuke Zhu is an Assistant Professor in the Computer Science Department of UT-Austin, where he directs the Robot Perception and Learning (RPL) Lab. He is also a core faculty at Texas Robotics and a senior research scientist at NVIDIA. He focuses on developing intelligent algorithms for generalist robots and embodied agents to reason about and interact with the real world. His research spans robotics, computer vision, and machine learning. He received his Master's and Ph.D. degrees from Stanford University. His work has won various awards and nominations, including the Best Conference Paper Award in ICRA 2019, the Outstanding Learning Paper Award at ICRA 2022, the Outstanding Paper Award at NeurIPS 2022, and Best Paper Award Finalists in IROS 2019, 2021, RSS 2022, 2023, and CoRL 2023. He received the NSF CAREER Award and faculty awards from Amazon and JP Morgan.