| 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 | 138 | 2016 |
| Learning from limited demonstrations B Kim, A Farahmand, J Pineau, D Precup Advances in Neural Information Processing Systems, 2859-2867, 2013 | 82 | 2013 |
| Maximum Mean Discrepancy Imitation Learning B Kim, J Pineau Robotics: Science and Systems, 2013 | 35 | 2013 |
| 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 | 34 | 2019 |
| 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 | 34 | 2017 |
| 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 | 16 | 2018 |
| Regret bounds for meta Bayesian optimization with an unknown Gaussian process prior Z Wang, B Kim, LP Kaelbling arXiv preprint arXiv:1811.09558, 2018 | 13 | 2018 |
| 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 | 11 | 2013 |
| 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 | 10 | 2021 |
| 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 | 8 | 2020 |
| 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 | 6 | 2019 |
| Learning value functions with relational state representations for guiding task-and-motion planning B Kim, L Shimanuki Conference on Robot Learning, 955-968, 2020 | 5 | 2020 |
| Generalizing over uncertain dynamics for online trajectory generation B Kim, A Kim, H Dai, L Kaelbling, T Lozano-Perez Robotics Research, 39-55, 2018 | 5 | 2018 |
| 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 | 1 | 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 | 1 | 2017 |
| 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 | 1 | 2009 |
| 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 |
Leslie KaelblingVerified email at csail.mit.edu
