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

I am an Assistant Professor in Graduate School of AI at KAIST. I direct the Intelligent mobile-manipulation (iM^2) lab.

I am interested in creating intelligent mobile-manipulation robots that can efficiently make decisions in complex environments by utilizing prior knowledge and new experience.

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.

CV  /  Google Scholar  /  Github

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iM^2 lab

Ph.D students

Jamie (Yoonyoung) Cho

Masters students

Heesang Cho
Jiyong Ahn
Minchan Kim

Visiting scholars

Donghoon Shin

Publications
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, Tomas Lozano-Perez.
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, Tomas Lozano-Perez
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, Tomas Lozano-Perez
AAAI Conference on Artificial Intelligence (AAAI), 2018. Oral (top 6% of accepted papers). [pdf]

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

Generalizing over uncertain dynamics for on-line trajectory generation.
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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