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

Here I list selected publications only. For a complete list, please refer to my CV above.

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
International Conference on Intelligent Robots and Systems (IROS), 2021
Finalist for Best Cognitive Robotics Paper Award

Task and Motion Planning

We develop a task and motion planner under temporal constraints.

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

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
arXiv, 2021

Multi-resolution POMDP planning for multi-object search in 3D
Kaiyu Zheng, Yoonchang Sung, George Konidaris, Stefanie Tellex
International Conference on Intelligent Robots and Systems (IROS), 2021
Winner of Best Robocup Paper Award

Design and source code from Jon Barron's website