Seungyeon Kim

Hello! I am postdoctoral researcher at Robotics Laboratory in Seoul National University, advised by Frank C. Park.

Prior to joining postdoctoral program, I completed my Ph. D., M.S., and B.S. with Mechanical Engineering (minor: Economics) from Seoul National University.

Email: ksy at robotics.snu.ac.kr

CV / Google Scholar / Github / Youtube

Research (Research Statement)

My research focuses on developing practical solutions for intelligent robots that are adaptive and generalizable to unknown arbitrary environments. I am particularly interested in leveraging inductive biases to enable robots to perform effectively in real-world scenarios with limited data while also adapting to various downstream manipulation tasks. My research aims to address the challenges of designing effective inductive biases for intelligent robots and contribute to the development of more efficient and capable robotic systems.

Introducing inductive bias into the recognition system enables efficient 3D reconstruction of objects from partial and incomplete visual observations, allowing robots to manipulate them effectively [DSQNet, T2SQNet, Search-for-Grasp].
Adopting equivariant models reduces data requirements while enhancing generalizability across diverse robot manipulation tasks, ranging from pushing dynamics learning to skill learning [SQPDNet, EMMP].
Identifying low-dimensional representations of robot trajectories reduces the complexity of high-dimensional data, enabling rapid adaptation to environmental changes [EMMP, DIVO].

Publications ( * denotes equal contribution.)

	Diverse Policy Learning via Random Obstacle Deployment for Zero-Shot Adaptation Seokjin Choi, Yonghyeon Lee, Seungyeon Kim, Che-Sang Park, Himchan Hwang, Frank C. Park IEEE Robotics and Automation Letters (RA-L) 2025 Project Page • Paper • Bibtex
	T²SQNet: A Recognition Model for Manipulating Partially Observed Transparent Tableware Objects Young Hun Kim, Seungyeon Kim, Yonghyeon Lee, Frank C. Park Conference on Robot Learning (CoRL) 2024 Project Page • Paper • Code • Bibtex
	Leveraging 3D Reconstruction for Mechanical Search on Cluttered Shelves Seungyeon Kim, Young Hun Kim, Yonghyeon Lee, Frank C. Park Conference on Robot Learning (CoRL) 2023 Project Page • Paper • Code • Bibtex
	Equivariant Motion Manifold Primitives Byeongho Lee, Yonghyeon Lee, Seungyeon Kim, MinJun Son, Frank C. Park Conference on Robot Learning (CoRL) 2023 Project Page • Paper • Code • Bibtex
	SE(2)-Equivariant Pushing Dynamics Models for Tabletop Object Manipulations Seungyeon Kim, Byeongdo Lim, Yonghyeon Lee, Frank C. Park Conference on Robot Learning (CoRL) 2022 Oral Presentation • Project Page • Paper • Code • Supplementary • Bibtex
	DSQNet: A Deformable Model-Based Supervised Learning Algorithm for Grasping Unknown Occluded Objects Seungyeon Kim, Taegyun Ahn, Yonghyeon Lee, Jihwan Kim, Michael Y. Wang, Frank C. Park IEEE Transactions on Automation Science and Engineering (T-ASE) 2022 Project Page • Paper • Code • Bibtex
	A Statistical Manifold Framework for Point Cloud Data Yonghyeon Lee, Seungyeon Kim, Jinwon Choi, Frank C. Park International Conference on Machine Learning (ICML) 2022 Paper • Code • Bibtex
	On the Encoding Capacity of Human Motor Adaptation Seungyeon Kim, Jaewoon Kwon, Jin-Min Kim, Frank C. Park, Sang-Hoon Yeo Journal of Neurophysiology (JNP) 2021 Paper • Bibtex

Projects

	Object Grasping and Manipulation Skills for Stable Housekeeping Service Project Leader The goal of the project is to develop skills to enable various household tasks, specifically, to develop a skill that can grasp various tableware and objects on the table.
	Deep Learning-based Lane Detection Algorithm from LiDAR data Project Leader The goal of the proejct to develop a neural network architecture that recognizes 3D lane information from LiDAR data (3D point cloud + point-wise intensity).
	Deep Reinforcement Learning Algorithm for Industrial Robot Project Leader The goal of the proejct is to develop safe and efficient reinforcement learning algorithm for high-gain position controller-based industrial robots.
	Artificial Intelligence-based Automated Painting Robot System Project Member (Role: Visualization) The goal of the project is to develop an artificial intelligence-based smart painting robot automation system for automobile factories.

Education

Feb 2024. Ph.D. degree in Mechanical Engineering, Seoul National University

Advisor: Prof. Frank Chongwoo Park
Thesis: Learning for Vision-Based Object Manipulation: A Shape Recognition-Based Approach
PDF • Slides
Outstanding Doctoral Dissertation Award

Feb 2019. M.S. degree in Mechanical Engineering, Seoul National University

Advisor: Prof. Frank Chongwoo Park / work closely with Prof. Sang-Hoon Yeo
Thesis: On the Encoding Capacity of Human Motor Adaptation

Feb 2017. B.S. degree in Mechanical Engineering and Minor in Economics, Seoul National University

Summa Cum Laude

Feb 2013. Gyeonggibuk Science High School

One-year early graduation

Teaching Experiences

Teaching Assistant, Geometric Methods for High-Dimensional Data Analysis (M3239.006800), SNU, 2022F

Teaching Assistant, Dynamics (446.204A), SNU, 2018F

Teaching Assistant, Introduction to Robotics (M2794.0027), SNU, 2017S

Undergraduate Student Instructor, Basic Calculus 1 (033.016), SNU, 2015S

Undergraduate Student Instructor, Basic Calculus 2 (033.017), SNU, 2014F

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