Seungchan Kim


I am a fourth-year PhD student in the Robotics Institute at Carnegie Mellon University, advised by Sebastian Scherer. My research interests are robot exploration & navigation, multi-robot systems, and embodied AI.

Previously, I received B.S. in Applied Mathematics & Computer Science and M.S. in Computer Science from Brown University. During my time at Brown, I was advised by George Konidaris and Michael Littman, working on research in deep reinforcement learning.

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  • 12/2023: Survey on foundation models and robotics was released! Check out here!
  • 11/2023: Paper on multi-robot exploration was accepted at IEEE Robotics and Automation Letters!
  • 11/2023: Attended CoRL 2023. Here are the notes on the sessions that I joined

Toward General-Purpose Robots via Foundation Models: A Survey and Meta-Analysis
Yafei Hu*, Quanting Xie*, Vidhi Jain*, Jonathan Francis, Jay Patrikar, Nikhil Keetha, Seungchan Kim, Yaqi Xie, Tianyi Zhang, Shibo Zhao, Yu-Quan Chong, Chen Wang, Katia Sycara, Matthew Johnson-Roberson, Dhruv Batra, Xiaolong Wang, Sebastian Scherer, Zsolt Kira, Fei Xia, Yonatan Bisk
arXiv preprint arXiv:2312.08782 (2023).
project page | arXiv
Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Circular Decomposition
Seungchan Kim, Micah Corah, John Keller, Graeme Best, Sebastian Scherer
IEEE Robotics and Automation Letters (RA-L) 2023
Selected for Presentation at International Conference on Robotics and Automation (ICRA) 2024
project page | arXiv | DOI | video
AirDet: Few-Shot Detection without Fine-tuning for Autonomous Exploration
Bowen Li, Chen Wang, Pranay Reddy, Seungchan Kim, Sebastian Scherer
European Conference on Computer Vision (ECCV) 2022
project page | code | arXiv
Robotic Interestingness via Human-Informed Few-Shot Object Detection
Seungchan Kim, Chen Wang, Bowen Li, Sebastian Scherer
IEEE/RSJ International Conference on Robotics and Systems (IROS) 2022
Unsupervised Online Learning for Robotic Interestingness with Visual Memory
Chen Wang, Yuheng Qiu, Wenshan Wang, Yafei Hu, Seungchan Kim, Sebastian Scherer
IEEE Transactions on Robotics (T-RO) 2021
project page | code | arXiv
Using Computational Analysis of Behavior to Discover Developmental Change in Memory-Guided Attention Mechanisms in Childhood
Dima Amso, Lakshmi Govindarajan, Pankaj Gupta, Diego Placido, Heidi Baumgartner, Andrew Lynn, Kelley Gunther, Tarun Sharma, Vijay Veerabadran, Kalpit Thakkar, Seungchan Kim, Thomas Serre
PsyArXiv. doi:10.31234/
Combating the Compounding-Error Problem with a Multi-step Model
Kavosh Asadi, Dipendra Misra, Seungchan Kim, Michael Littman
arXiv preprint arXiv:1905.13320 (2019).
DeepMellow: Removing the Need for a Target Network in Deep Q-Learning
Seungchan Kim, Kavosh Asadi, Michael Littman, George Konidaris
International Joint Conference on Artificial Intelligence (IJCAI) 2019
Multidisciplinary Conference on Reinforcement Learning and Decision Making (RLDM) 2019
project page | code
Removing the Target Network from Deep Q-Networks with the Mellowmax Operator
Seungchan Kim, Kavosh Asadi, Michael Littman, George Konidaris
International Conference on Autonomous Agents and Multiagent Systems (AAMAS) 2019
16-711 Kinematics, Dynamics, Control , Carnegie Mellon University Robotics Institute
Teaching Assistant, Spring 2023

16-833 Robot Localization and Mapping, Carnegie Mellon University Robotics Institute
Teaching Assistant, Spring 2022
CSCI1430 Computer Vision, Brown University
Teaching Assistant, Spring 2019

CSCI0040 Intro to Scientific Computing and Problem Solving, Brown University
Teaching Assistant, Spring 2015
Organizer: Tartan Planning Series Spring 2023


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