|
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].
|
|
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
|
|
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
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
|
|