Beomjoon Kim
{firstname}.{lastname} at kaist.ac.kr

I am an Associate Professor in Graduate School of AI at KAIST. I direct the Intelligent mobile-manipulation (IM^2) lab, where we have several research internship positions available.

I am interested in creating general-purpose mobile manipulation robots that can efficiently make decisions in complex environments.

Previously, I obtained my Ph.D. in computer science from MIT CSAIL, my MSc in computer science from McGill University, and my BMath in computer science and statistics from University of Waterloo.

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Recent research

Check out my talk at MIT Embodied Intelligence seminar for our recent effort in developing a general-purpose robot.

IM^2 lab

Ph.D students

Jamie (Yoonyoung) Cho
Dongwon Son
Junhyek Han
Dongryung Lee
Haewon Jung
Seunghoon Jeon (co-adivsed by Jemin Hwangbo)

Masters students

Jaehyung Kim
Jisu Han
Sanghyeon Son
Sejune Joo
Hojin Jung
Dongwuk Lee

Interns
Jiho Kim
Haecheol Park

Publications

CORN: Contact-based Object Representation for Nonprehensile Manipulation of General Unseen Objects
Yoonyoung Cho, Junhyek Han, Yoontae Cho, Beomjoon Kim
International Conference on Learning Representations (ICLR), 2024. [pdf] [video] [project page]

An Intuitive Multi-Frequency Feature Representation for SO(3)-Equivariant Networks
Dongwon Son, Jaehyung Kim, Sanghyeon Son, Beomjoon Kim
International Conference on Learning Representations (ICLR), 2024. [pdf]

Open X-Embodiment: robotic learning datasets and RT-X models
Open X-Embodiment Collaboration
International Conference on Robotics and Automation (ICRA), 2023. [pdf]

Learning whole-body manipulation for quadrupedal robot
Seunghun Jeon, Moonkyu Jung, Suyoung Choi, Beomjoon Kim (co-corresponding author), Jemin Hwangbo
Robotics and Automation Letters (RA-L), 2023. [pdf] [video]

Preference learning for guiding the tree search in continuous POMDPs
Jiyong Ahn, Sanghyeon Son, Dongryung Lee, Jisu Han, Dongwon Son, and Beomjoon Kim.
Conference on Robot Learnining (CoRL), 2023. [pdf] [project page]

Pre- and Post-Contact Policy Decomposition for Non-Prehensile Manipulation with Sim-to-Real Transfer.
Minchan Kim, Junhyek Han, Jaehyung Kim, and Beomjoon Kim.
International Conference on Intelligent Robots and Systems (IROS), 2023. [pdf] [project page]

Local object crop collision network for efficient simulation of non-convex objects in GPU-based simulators.
Dongwon Son, Beomjoon Kim.
Robotics: Science and Systems (RSS), 2023. [pdf] [project page]

Ohm^2: Optimal hierarchical planner for object search in large environments via mobile manipulation
Yoonyoung Cho*, Donghoon Shin*, Beomjoon Kim.
International Conference on Intelligent Robots and Systems (IROS), 2022. [pdf]

Representation, learning, and planning algorithms for geometric task and motion planning
Beomjoon Kim, Luke Shimanuki, Leslie Pack Kaelbling, Tomás Lozano-Pérez.
International Journal of Robotics Research, 2021. [doi] [arXiv]

Integrated task and motion planning .
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, Tomás Lozano-Pérez.
Annual Review of Control, Robotics, and Autonomous Systems, 2021. [pdf] [arXiv]

A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects .
Anthony Simeonov, Yilun Du, Beomjoon Kim, Francois Hogan, Joshua Tenenbaum, Pulkit Agrawal, Alberto Rodriguez.
Conference on Robot Learning (CoRL), 2020. [arXiv] [project page] [video]

CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs .
Rohan Chitnis*, Tom Silver*, Beomjoon Kim, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Conference on Robot Learning (CoRL), 2020. Plenary talk (top 12% of accepted papers). [pdf] [video]

Monte Carlo Tree Search in continuous spaces using Voronoi optimistic optimization with regret bounds .
Beomjoon Kim, Kyungjae Lee, Sungbin Lim, Leslie Pack Kaelbling, Tomas Lozano-Perez.
AAAI Conference on Artificial Intelligence (AAAI), 2020. Oral (top 6% of accepted papers). [pdf] [appendix]

Learning to guide task and motion planning using score-space representation.
Beomjoon Kim, Zi Wang, Leslie Pack Kaelbling, Tomás Lozano-Pérez.
International Journal of Robotics Research, 2019. [doi] [arXiv]

Learning value functions with relational state representations for guiding task-and-motion planning.
Beomjoon Kim, Luke Shimanuki
Conference on Robot Learning (CoRL), 2019. [pdf] [appendix]

Adversarial actor-critic method for task and motion planning problems using planning experience.
Beomjoon Kim, Leslie Pack Kaelbling, Tomás Lozano-Pérez
AAAI Conference on Artificial Intelligence (AAAI), 2019. Oral (top 6% of accepted papers). [pdf] [appendix]

Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior.
Zi Wang*, Beomjoon Kim*, Leslie Pack Kaelbling
Neural Information Processing Systems (NeurIPS), 2018. Spotlight (top 3.5% of accepted papers). [pdf]

Guiding search in continuous state-action spaces by learning an action sampler from off-target search experience.
Beomjoon Kim, Leslie Pack Kaelbling, Tomás Lozano-Pérez
AAAI Conference on Artificial Intelligence (AAAI), 2018. Oral (top 6% of accepted papers). [pdf]

Learning to guide task and motion planning using score-space representation.
Beomjoon Kim, Leslie Pack Kaelbling, Tomás Lozano-Pérez
International Conference on Robotics and Automation (ICRA), 2017. Best Cognitive Robotics Paper award. [pdf]

Socially adaptive path planning in human environments using inverse reinforcement learning .
Beomjoon Kim, Joelle Pineau
International Journal of Social Robotics, 2016. [pdf]

Generalizing over uncertain dynamics for on-line trajectory generation.
Beomjoon Kim, Leslie Pack Kaelbling, Tomás Lozano-Pérez
International Symposium of Robotics Research (ISRR), 2015. [pdf]

Learning from limited demonstrations .
Beomjoon Kim, Amir-massoud Farahmand, Doina Precup, Joelle Pineau.
Neural Information Processing Systems (NeurIPS), 2013. Spotlight (top 4% of accepted papers). [pdf]

Maximum mean discrepancy imitation learning.
Beomjoon Kim, Joelle Pineau.
Robotics: Science and Systems (RSS), 2013. [pdf]


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