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Yoonchang Sung

yooncs8 [@] cs.utexas.edu
yooncs8 [@] cs.utexas.edu
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I am a researcher passionate about developing intelligent robots with the ability to plan efficiently, learn from past experiences, and reason about their decisions and interactions with other agents. In particular, I design algorithms for task and motion planning, as well as multi-robot systems, aiming for solutions that are both theoretically sound and practically valuable.

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 on the academic job market for 2023-2024!

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."


Research

My current research is primarily motivated by addressing long-horizon robot decision-making problems, such as cooking a meal or cleaning a dirty room. Such long-horizon tasks present challenges in selecting high-level actions, like picking up a cup, and in effectively planning how to achieve these actions while satisfying physical and geometric constraints. Existing algorithms are prohibitively expensive to scale up beyond the smallest of problems. To tackle this challenge, I design general-purpose task and motion planning algorithms with theoretical guarantees and improving their efficiency by (1) leveraging metareasoning methods, (2) investigating long-horizon dependencies with the help of machine learning techniques, and (3) distributing tasks among multiple robots.

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Asynchronous task plan refinement for multi-robot task and motion planning

Yoonchang Sung, Rahul Shome, Peter Stone

Under Review

A new multi-robot task and motion planning formulation that eliminates the need for pre-discretization of the space and synchronous actions among robots.

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Motion planning (in)feasibility detection using a prior roadmap via path and cut search

Yoonchang Sung, Peter Stone

Robotics: Science and Systems (RSS 2023)

Iterative pathfinding and cut finding effectively reduce the search space, making the determination of whether a prior roadmap contains a feasible solution more efficient.

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A survey of decision-theoretic approaches for robotic environmental monitoring

Yoonchang Sung, Zhiang Chen, Jnaneshwar Das, Pratap Tokekar

Foundations and Trends in Robotics 2023

A comprehensive survey on robotic environmental monitoring, covering environmental representations and properties, tasks, and decision-theoretic tools.

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Learning to correct mistakes: backjumping in long-horizon task and motion planning

Yoonchang Sung*, Zizhao Wang*, Peter Stone

Conference on Robot Learning (CoRL 2022)

A heuristic leveraged by supervised learning to guide task-level search, alleviating the need for consistently employing expensive backtracking.

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Towards optimal correlational object search

Kaiyu Zheng, Rohan Chitnis, Yoonchang Sung, George Konidaris, Stefanie Tellex

IEEE International Conference on Robotics and Automation (ICRA 2022)

Leveraging correlational information among objects allows the POMDP formulation to efficiently discover optimal solutions for object search.

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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

Being able to trade-off between plan quality and computation time lets you build a more efficient planner.

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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

The challenge of intractable partially observable 3D object search can be alleviated through a multi-resolution belief space representation.

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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)

When robots work with humans, linear temporal logic allows the modeling of human interventions, while a behavior tree enables the robot to execute reactively.

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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)

Unlike standard multi-armed bandit settings, the arms' rewards are correlated, and the reward amount depends on the altitude of a UAV.

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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)

The explicit boundary of limited sensing range is incorporated into the GM-PHD filter, which estimates both the number and spatial density of targets.

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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)

Minimax search tree and Monte-Carlo search tree methods for discrete, sequential, two-player, zero-sum games that involve trading off conflicting objectives.

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Distributed assignment with limited communication for multi-robot multi-target tracking

Yoonchang Sung, Ashish Budhiraja, Ryan Williams, Pratap Tokekar

Autonomous Robots (AURO 2020)

IEEE International Conference on Robotics and Automation (ICRA 2018)

The reduction of multi-robot multi-target tracking problems to mixed packing and covering problems enables the use of local algorithms for distributed problem-solving.

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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)

A recursive depth-first-search algorithm for exploring the unknown shape of a moving plume, with provable competitive ratio guarantees.

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Team VALOR’s ESCHER: A novel electromechanical biped for the DARPA Robotics Challenge

I am one of 23 co-authors

Journal of Field Robotics (JFR 2017)

Virginia Tech's electric series compliant humanoid participated in the finals of the DARPA Robotics Challenge.

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Hierarchical sample-based joint probabilistic data association filter for following human legs using a mobile robot in a cluttered environment

Yoonchang Sung and Woojin Chung

IEEE Transactions on Human-Machine Systems (T-HMS 2016)

Correlations among targets are explicitly modeled for target tracking, a consideration that is generally treated independently in the tracking literature.