Beomjoon Kim
Title
Cited by
Cited by
Year
Socially adaptive path planning in human environments using inverse reinforcement learning
B Kim, J Pineau
International Journal of Social Robotics 8 (1), 51-66, 2016
1382016
Learning from limited demonstrations
B Kim, A Farahmand, J Pineau, D Precup
Advances in Neural Information Processing Systems, 2859-2867, 2013
822013
Maximum Mean Discrepancy Imitation Learning
B Kim, J Pineau
Robotics: Science and Systems, 2013
352013
Learning to guide task and motion planning using score-space representation
B Kim, Z Wang, LP Kaebling, T Lozano-Perez
The International Journal of Robotics Research 28 (7), 2019
342019
Learning to guide task and motion planning using score-space representation
B Kim, LP Kaelbling, T Lozano-Pérez
International Conference on Robotics and Automation, 2017
342017
Guiding Search in Continuous State-action Spaces by Learning an Action Sampler from Off-target Search Experience
B Kim, LP Kaelbling, T Lozano-Pérez
AAAI Conference on Artificial Intelligence, 2018
162018
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Z Wang, B Kim, LP Kaelbling
arXiv preprint arXiv:1811.09558, 2018
132018
Human-like navigation: Socially adaptive path planning in dynamic environments
B Kim, J Pineau
RSS 2013 workshop on inverse optimal control and robotic learning from …, 2013
112013
Integrated task and motion planning
CR Garrett, R Chitnis, R Holladay, B Kim, T Silver, LP Kaelbling, ...
Annual review of control, robotics, and autonomous systems 4, 265-293, 2021
102021
Monte Carlo tree search in continuous spaces using Voronoi optimistic optimization with regret bounds
B Kim, K Lee, S Lim, L Kaelbling, T Lozano-Pérez
Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 9916-9924, 2020
82020
Adversarial actor-critic method for task and motion planning problems using planning experience
B Kim, LP Kaelbling, T Lozano-Pérez
Proceedings of the AAAI Conference on Artificial Intelligence 33 (01), 8017-8024, 2019
62019
Learning value functions with relational state representations for guiding task-and-motion planning
B Kim, L Shimanuki
Conference on Robot Learning, 955-968, 2020
52020
Generalizing over uncertain dynamics for online trajectory generation
B Kim, A Kim, H Dai, L Kaelbling, T Lozano-Perez
Robotics Research, 39-55, 2018
52018
A Long Horizon Planning Framework for Manipulating Rigid Pointcloud Objects
A Simeonov, Y Du, B Kim, FR Hogan, J Tenenbaum, P Agrawal, ...
arXiv preprint arXiv:2011.08177, 2020
12020
Guiding the search in continuous state-action spaces by learning an action sampling distribution from off-target samples
B Kim, LP Kaelbling, T Lozano-Perez
arXiv preprint arXiv:1711.01391, 2017
12017
An optimisation model for airlift load planning: GALAHAD and the quest for the ‘holy grail’
BL Kaluzny, RHAD Shaw, A Ghanmi, B Kim
2009 IEEE Symposium on Computational Intelligence for Security and Defense …, 2009
12009
Integrated Task and Motion Planning
C Reed Garrett, R Chitnis, R Holladay, B Kim, T Silver, L Pack Kaelbling, ...
arXiv e-prints, arXiv: 2010.01083, 2020
2020
CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs
R Chitnis, T Silver, B Kim, LP Kaelbling, T Lozano-Perez
arXiv preprint arXiv:2007.13202, 2020
2020
Learning to guide task and motion planning
B Kim
Massachusetts Institute of Technology, 2020
2020
Appendix for Monte Carlo Tree Search in high-dimensional continuous spaces using Voronoi optimistic optimization with regret bounds
B Kim, K Lee, S Lim, LP Kaelbling, T Lozano-Pérez
2020
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