Seungchan Kim

email: seungch2@andrew.cmu.edu

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, active robotic perception, and spatial 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.

CV / GitHub / Google Scholar / Twitter

News

03/2023: Organizing Tartan Planning Series Spring 2023!
07/2022: 2 papers on few-shot object detection were accepted at ECCV and IROS
11/2021: 1 paper on unsupervised online learning for robotics accepted at T-RO
09/2020: Started PhD in Robotics at Carnegie Mellon University!

Publications

	Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Spherical Decomposition Seungchan Kim, Micah Corah, John Keller, Graeme Best, Sebastian Scherer arXiv preprint arXiv:2307.15202. Under Review
	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
	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) 2022
	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/osf.io/gq4rt
	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

Teaching

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

Service

Organizer: Tartan Planning Series Spring 2023

Reviewer: ICML, AAAI, ICLR, NeurIPS, ICRA, IEEE RA-L, IJRR

Website Template from Jon Barron.