| Value function approximation in reinforcement learning using the Fourier basis G Konidaris, S Osentoski, P Thomas Proceedings of the Twenty-Fifth Conference on Artificial Intelligence, 380-385, 2011 | 551 | 2011 |
| A review of robot learning for manipulation: Challenges, representations, and algorithms O Kroemer, S Niekum, G Konidaris Journal of Machine Learning Research 22 (30), 1-82, 2021 | 387 | 2021 |
| Robot learning from demonstration by constructing skill trees G Konidaris, S Kuindersma, R Grupen, A Barto The International Journal of Robotics Research 31 (3), 360-375, 2012 | 381 | 2012 |
| Skill discovery in continuous reinforcement learning domains using skill chaining G Konidaris, A Barto Advances in Neural Information Processing Systems, 1015-1023, 2009 | 380 | 2009 |
| From skills to symbols: Learning symbolic representations for abstract high-level planning G Konidaris, LP Kaelbling, T Lozano-Perez Journal of Artificial Intelligence Research 61, 215-289, 2018 | 352 | 2018 |
| Building Portable Options: Skill Transfer in Reinforcement Learning. GD Konidaris, AG Barto Ijcai 7, 895-900, 2007 | 345 | 2007 |
| LQR-RRT*: Optimal sampling-based motion planning with automatically derived extension heuristics A Perez, R Platt, G Konidaris, L Kaelbling, T Lozano-Perez Robotics and Automation (ICRA), 2012 IEEE International Conference on, 2537-2542, 2012 | 318 | 2012 |
| Learning multi-level hierarchies with hindsight A Levy, G Konidaris, R Platt, K Saenko The Seventh International Conference on Learning Representations, 2019 | 316 | 2019 |
| Autonomous shaping: Knowledge transfer in reinforcement learning G Konidaris, A Barto Proceedings of the 23rd international conference on Machine learning, 489-496, 2006 | 298 | 2006 |
| Learning grounded finite-state representations from unstructured demonstrations S Niekum, S Osentoski, G Konidaris, S Chitta, B Marthi, AG Barto The International Journal of Robotics Research 34 (2), 131-157, 2015 | 255 | 2015 |
| Learning and generalization of complex tasks from unstructured demonstrations S Niekum, S Osentoski, G Konidaris, AG Barto 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2012 | 242 | 2012 |
| Reinforcement learning with parameterized actions W Masson, P Ranchod, G Konidaris Proceedings of the AAAI conference on artificial intelligence 30 (1), 2016 | 239 | 2016 |
| Learning parameterized skills B Da Silva, G Konidaris, A Barto Proceedings of the Twenty Ninth International Conference on Machine Learning …, 2012 | 227 | 2012 |
| Comparing robot grasping teleoperation across desktop and virtual reality with ROS reality D Whitney, E Rosen, E Phillips, G Konidaris, S Tellex Robotics Research: The 18th International Symposium ISRR, 335-350, 2019 | 183 | 2019 |
| Modeling and planning with macro-actions in decentralized POMDPs C Amato, G Konidaris, LP Kaelbling, JP How Journal of Artificial Intelligence Research 64, 817-859, 2019 | 155 | 2019 |
| Robot motion planning on a chip. S Murray, W Floyd-Jones, Y Qi, DJ Sorin, GD Konidaris Robotics: Science and Systems 6, 2016 | 153 | 2016 |
| Communicating robot arm motion intent through mixed reality head-mounted displays E Rosen, D Whitney, E Phillips, G Chien, J Tompkin, G Konidaris, S Tellex Robotics research: The 18th international symposium ISRR, 301-316, 2020 | 139 | 2020 |
| Hidden parameter markov decision processes: A semiparametric regression approach for discovering latent task parametrizations F Doshi-Velez, G Konidaris IJCAI: proceedings of the conference 2016, 1432, 2016 | 134 | 2016 |
| Planning for decentralized control of multiple robots under uncertainty C Amato, G Konidaris, G Cruz, CA Maynor, JP How, LP Kaelbling 2015 IEEE international conference on robotics and automation (ICRA), 1241-1248, 2015 | 131 | 2015 |
| Constructing symbolic representations for high-level planning G Konidaris, L Kaelbling, T Lozano-Perez Proceedings of the AAAI Conference on Artificial Intelligence 28 (1), 2014 | 130 | 2014 |
Andrew BartoUniversity of Massachusetts AmherstVerified email at cs.umass.edu
