Seungchan Kim

email: seungch2@andrew.cmu.edu

I am a Ph.D. candidate in the Robotics Institute at Carnegie Mellon University, advised by Sebastian Scherer. My research focuses on spatial reasoning and semantic representations for autonomous mobile robot exploration and navigation. I was also a robotics research intern at Field AI.

Previously, I earned a B.S. in Applied Mathematics & Computer Science and a M.S. in Computer Science from Brown University, where I was advised by George Konidaris and Michael Littman. At Brown, my research centered on deep reinforcement learning.

CV / GitHub / Google Scholar / X (Twitter) / LinkedIn / YouTube

News

04/2026: Visited UC San Diego, UC Berkeley, and Stanford for invited talks
01/2026: A paper on outdoor semantic navigation, RAVEN, was accepted to ICRA 2026. See you at Vienna!
09/2025: I started research internship at Field AI.
06/2025: Two papers, RayFronts and PIPE Planner, were accepted to IROS 2025.

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Invited Talks

Predictive Semantic World Models for Long-Horizon Mobile Robot Autonomy
CogAI Group @ Stanford (hosted by Jiajun Wu, Apr 2026)
Predictive Semantic Foresight for Mobile Robot Autonomy
Kanazawa AI Research Lab @ UC Berkeley (hosted by Angjoo Kanazawa, Apr 2026)
Co-PI Seminar on Optimization, Control, and Learning @ UCSD [Link] (hosted by Nikolay Atanasov, Apr 2026)
Interactive and Trustworthy Robotics Lab @ CMU (hosted by Andrea Bajcsy, Apr 2026)
Predictive Mapping and Semantic Reasoning for Autonomous Mobile Robots
Scalable Spatial Intelligence Lab @ U Michigan (hosted by Yulun Tian, Mar 2026)
Robot Learning Seminar @ U Buffalo / Georgia Tech / Penn State (hosted by Chen Wang, Mar 2026)
Spatial Reasoning and Semantic Representations for Intelligent Multi-Robot Exploration and Navigation
Artificial Intelligence for Robot Coordination at Scale Lab @ CMU (hosted by Jiaoyang Li, Jul 2025)
Resilient Intelligent Systems Lab @ CMU (hosted by Wennie Tabib, Nov 2024)

Publications

	RADSeg: Unleasing Parameter and Compute Efficient Zero-Shot Open-Vocabulary Segmentation Using Agglomerative Models Omar Alama, Darshil Jariwala, Avigyan Bhattacharya, Seungchan Kim, Wenshan Wang, Sebastian Scherer IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026 Findings* paper \| arXiv \| project page \| code
	RAVEN: Resilient Aerial Navigation via Open-Set Semantic Memory and Behavior Adaptation Seungchan Kim, Omar Alama, Dmytro Kurdydyk, John Keller, Nikhil Keetha, Wenshan Wang, Yonatan Bisk, Sebastian Scherer IEEE International Conference on Robotics and Automation (ICRA) 2026 IROS 2025 Active Perception Workshop, Best Paper Finalist (Spotlight Presentation) paper \| arXiv \| project page \| video
	MapExRL: Human-Inspired Indoor Exploration with Predicted Environment Context and Reinforcement Learning Narek Harutyunyan, Brady Moon, Seungchan Kim, Cherie Ho, Adam Hung, Sebastian Scherer International Conference on Advanced Robotics (ICAR) 2025 ICRA 2025 Workshop on Structured Learning for Efficient, Reliable, and Transparent Robots paper \| arXiv \| project page \| DOI \| video
	RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration Omar Alama, Avigyan Bhattacharya, Haoyang He, Seungchan Kim, Yuheng Qiu, Wenshan Wang, Cherie Ho, Nikhil Keetha, Sebastian Scherer IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025 RSS 2025 Workshop on Semantic Reasoning and Goal Understanding in Robotics RSS 2025 Workshop on Learned Robot Representations paper \| arXiv \| project page \| code \| DOI \| video
	PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration Seungjae Baek, Brady Moon, Seungchan Kim, Muqing Cao, Cherie Ho, Sebastian Scherer, Jeong hwan Jeon (: Equal Contributions) IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2025 paper \| arXiv \| project page \| code \| DOI \| video
	MapEx: Indoor Structure Exploration with Probabilistic Information Gain from Global Map Predictions Cherie Ho, Seungchan Kim*, Brady Moon, Aditya Parandekar, Narek Harutyunyan, Chen Wang, Katia Sycara, Graeme Best, Sebastian Scherer (: Equal Contributions) IEEE International Conference on Robotics and Automation (ICRA) 2025 paper \| arXiv \| project page \| code \| DOI \| video \| presentation video
	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, Hao-Shu Fang, Shibo Zhao, Shayegan Omidshafiei, Dong-Ki Kim, Ali-akbar Agha-mohammadi, Katia Sycara, Matthew Johnson-Roberson, Dhruv Batra, Xiaolong Wang, Sebastian Scherer, Chen Wang, Zsolt Kira, Fei Xia, Yonatan Bisk arXiv preprint arXiv:2312.08782 (2023).* paper \| arXiv \| project page
	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 Presentation at IEEE International Conference on Robotics and Automation (ICRA) 2024 paper \| arXiv \| project page \| DOI \| video \| presentation 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 paper \| arXiv \| project page \| code \| DOI
	Robotic Interestingness via Human-Informed Few-Shot Object Detection Seungchan Kim, Chen Wang, Bowen Li, Sebastian Scherer IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) 2022 paper \| arXiv \| DOI \| video
	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 paper \| arXiv \| project page \| code \| DOI
	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 DOI
	Combating the Compounding-Error Problem with a Multi-step Model Kavosh Asadi, Dipendra Misra, Seungchan Kim, Michael Littman arXiv preprint arXiv:1905.13320 (2019). paper \| arXiv
	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 paper \| code \| DOI
	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, Extended Abstract paper

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: IJRR, T-ASE, RA-L, ICRA, IROS, CASE, MRS, ICLR, NeurIPS, AAAI, ICML

Website Template from Jon Barron.