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
1042016
Learning from limited demonstrations
B Kim, A Farahmand, J Pineau, D Precup
Advances in Neural Information Processing Systems, 2859-2867, 2013
692013
Maximum Mean Discrepancy Imitation Learning
B Kim, J Pineau
Robotics: Science and Systems, 2013
342013
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
242019
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
242017
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
142018
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
Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior
Z Wang, B Kim, LP Kaelbling
Advances in Neural Information Processing Systems, 10477-10488, 2018
102018
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
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, 8017-8024, 2019
32019
Monte Carlo Tree Search in Continuous Spaces Using Voronoi Optimistic Optimization with Regret Bounds.
B Kim, K Lee, S Lim, LP Kaelbling, T Lozano-Pérez
AAAI, 9916-9924, 2020
22020
Learning value functions with relational state representations for guiding task-and-motion planning
B Kim, L Shimanuki
Conference on Robot Learning, 955-968, 2020
12020
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
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
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
2017
Efficient Imitation Learning and Inverse Reinforcement Learning with Application to Navigation in Human Environments
B Kim
McGill University Libraries, 2014
2014
Approximate Policy Iteration with Demonstration Data
B Kim, A Farahmand, J Pineau, D Precup
RLDM 2013, 168, 2013
2013
Learning to guide task and motion planning using score-space representation Download PDF
B Kim, Z Wang, LP Kaelbling, T Lozano-Perez
Learning to Plan with Pointcloud Affordances for General-Purpose Dexterous Manipulation
A Simeonov, Y Du, B Kim, FR Hogan, P Agrawal, A Rodriguez
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
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Articles 1–20