Yoonchang Sung

I am a postdoctoral fellow in the Learning Agents Research Group within the Computer Science Department at the University of Texas at Austin, hosted by Prof. Peter Stone. Previously, I was a postdoctoral associate at MIT CSAIL, hosted by Prof. Tomás Lozano-Pérez and Prof. Leslie Pack Kaelbling.

I received my Ph.D. degree from Virginia Tech under Prof. Pratap Tokekar in 2019. I received my M.S. and B.S. degrees from Korea University in 2013 and 2011, respectively.

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. Motion planning (in)feasibility detection using a prior roadmap via path and cut search Yoonchang Sung, Peter Stone Robotics: Science and Systems (RSS), 2023 Learning to correct mistakes: backjumping in long-horizon task and motion planning Yoonchang Sung, Zizhao Wang, Peter Stone Conference on Robot Learning (CoRL), 2022 Reactive task and motion planning under temporal logic specifications Shen Li, Daehyung Park, Yoonchang Sung, Julie Shah, Nicholas Roy IEEE International Conference on Robotics and Automation (ICRA)*, 2021
Meta-Reasoning	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. Environmental hotspot identification in limited time with a UAV equipped with a downward-facing camera Yoonchang Sung, Deeksha Dixit, Pratap Tokekar IEEE International Conference on Robotics and Automation (ICRA), 2021 Game tree search for minimizing detectability and maximizing visibility Zhongshun Zhang, Jonathon M. Smereka, Joseph Lee, Lifeng Zhou, Yoonchang Sung, Pratap Tokekar Autonomous Robots (AURO), 2021 IEEE International Conference on Robotics and Automation (ICRA), 2019 Distributed assignment with limited communication for multi-robot multi-target tracking Yoonchang Sung, Ashish Budhiraja, Ryan Williams, Pratap Tokekar Autonomous Robots (AURO), Special Issue on Robot Communication Challenges, 2020 IEEE International Conference on Robotics and Automation (ICRA), 2018 A competitive algorithm for online multi-robot exploration of a translating plume Yoonchang Sung, Pratap Tokekar IEEE International Conference on Robotics and Automation (ICRA), 2019
State Estimation & Tracking	We design a multi-target estimator and tracker that handles an unknown and varying number of targets. GM-PHD filter for searching and tracking an unknown number of targets with a mobile sensor with limited FOV Yoonchang Sung, Pratap Tokekar IEEE Transactions on Automation Science and Engineering (T-ASE), 2021 IEEE International Conference on Robotics and Automation (ICRA), 2017 Hierarchical sample-based joint probabilistic data association filter for following human legs using a mobile robot in a cluttered environment Yoonchang Sung, Woojin Chung IEEE Transactions on Human-Machine Systems (T-HMS), 2016
Planning for Object Search	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 Multi-resolution POMDP planning for multi-object search in 3D Kaiyu Zheng, Yoonchang Sung, George Konidaris, Stefanie Tellex IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021 Winner of Best Robocup Paper Award

Design and source code from Jon Barron's website

Task and Motion Planning

Meta-Reasoning

Multi-Robot Coordination

State Estimation & Tracking

Planning for Object Search