Yewon Lee | University of Washington

About Me

I am a Ph.D. student at the Paul G. Allen School of Computer Science and Engineering, University of Washington. I am advised by Prof. Siddhartha Srinivasa at the Personal Robotics Lab. My research interests center around learning for robot manipulation, particularly in long-horizon settings and around humans.

Prior to my Ph.D., I obtained a Master’s in Computer Science at the University of Toronto, where I was advised by Prof. Florian Shkurti at the Robot Vision and Learning Lab and collaborated with Prof. Fabio Ramos at NVIDIA. My Master’s thesis was on differentiable task and motion planning (TAMP). Prior to that, I completed my bachelor’s in Engineering Science at the University of Toronto. During my undergrad, I interned at Epson’s Robotics R&D team researching computer vision algorithms for robot manipulation.

I have been honored to receive the Google DeepMind Fellowship, NSERC Canada Graduate Scholarship (Master’s), and Ontario Graduate Scholarship for graduate studies.

News

[April 2024] Accepted my Ph.D. offer at the University of Washington.
[October 2023] My workshop paper on Differentiable TAMP was accepted to the LEAP Workshop at CoRL 2023!
[July 2023] Selected to attend Robotics: Science and Systems 2023 as an Inclusion Fellow.
[July 2023] Received the Ontario Graduate Scholarship.

Publications

STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent

Yewon Lee, Andrew Z. Li, Philip Huang, Eric Heiden, Krishna Murthy Jatavallabhula, Fabian Damken, Kevin Smith, Derek Nowrouzezahrai, Fabio Ramos, Florian Shkurti

Under Review at IEEE Robotics and Automation Letters (RA-L).

Conference on Robot Learning (CoRL), Learning Effective Abstractions for Planning Workshop, 2023.

Website arXiv Video Poster