Andy Zeng
Hi there! I'm a research scientist at Google DeepMind, where I enjoy tinkering with algorithms to make robots smarter. My research focuses on robot learning – to enable machines to intelligently interact with the world and improve themselves over time. These days I'm interested in training large deep neural nets on Internet-scale data.
Andy Zeng is a Staff Research Scientist at Google DeepMind , where he leads a small team working on self-improving Foundation models in robotics. He received his Bachelors in Computer Science and Mathematics at UC Berkeley, and his PhD in Computer Science at Princeton. He is interested in building algorithms that enable machines to intelligently interact with the world and improve themselves over time. Andy received Best Paper Awards from HRI '24, CoRL '23, ICRA '23, T-RO '20, RSS'19, and has been finalist for paper awards at RSS '23, CoRL '20 - '22, ICRA '20, RSS '19, IROS '18. He led machine learning as part of Team MIT-Princeton, winning 1st place (stow task) at the worldwide Amazon Picking Challenge '17. Andy is a recipient of the Princeton SEAS Award for Excellence, Japan Foundation Paper Award, NVIDIA Fellowship, and Gordon Y.S. Wu Fellowship in Engineering and Wu Prize. His work has been featured in the press, including the New York Times, BBC, and Wired.
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2023
Conference on Robot Learning (CoRL) Best Student Paper Award2022
Conference on Robot Learning (CoRL) Special Innovation Award2021
Google AI blog post "Decisiveness in Imitation Learning for Robots"2020
IEEE Transactions on Robotics (T-RO) Best Paper Award2019
New York Times article "A New Lab Full of Fast Learners" (100+ articles)2018
Honored to be a recipient of the Princeton SEAS Award for Excellence2015
Honored to be a recipient of the Gordon Y.S. Wu Fellowship in Engineering and Wu Prize
