I am interested in building intelligent robots that can plan efficiently, learn from past experience, and reason about their decisions and other agents. In particular, I design algorithms for task and motion planning and multi-robot systems that hopefully are both theoretically and practically useful.
My favorite quote from David Blackwell:
"Basically, I'm not interested in doing research and I never have been... I'm interested in understanding, which is quite a different thing. And often to understand something you have to work it out yourself because no one else has done it."
Email: yooncs8 [@] cs [DOT] utexas [DOT] edu  / 
CV
Research Highlights
Here I list selected publications only. For a complete list, please refer to my CV above.
Task and Motion Planning
Leveraging machine learning techniques to learn useful heuristics from past experience can improve planning efficiency.
Being able to trade-off between plan quality and computation time lets you build a more efficient planner.
Learning when to quit: meta-reasoning for motion planning Yoonchang Sung,
Leslie Kaelbling,
Tomás Lozano-Pérez
IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021
Finalist for Best Cognitive Robotics Paper Award
Multi-Robot Coordination
We study how to algorithmically distribute tasks and computation among robots in multi-robot planning.
We leverage POMDP to search for objects efficiently under partial and noisy observability.
Towards optimal correlational object search
Kaiyu Zheng,
Rohan Chitnis,
Yoonchang Sung,
George Konidaris,
Stefanie Tellex
IEEE International Conference on Robotics and Automation (ICRA), 2022