| Soft actor-critic algorithms and applications T Haarnoja, A Zhou, K Hartikainen, G Tucker, S Ha, J Tan, V Kumar, ... arXiv preprint arXiv:1812.05905, 2018 | 2382 | 2018 |
| Learning complex dexterous manipulation with deep reinforcement learning and demonstrations A Rajeswaran*, V Kumar*, A Gupta, G Vezzani, J Schulman, E Todorov, ... arXiv preprint arXiv:1709.10087, 2017 | 992 | 2017 |
| Multi-goal reinforcement learning: Challenging robotics environments and request for research M Plappert, M Andrychowicz, A Ray, B McGrew, B Baker, G Powell, ... arXiv preprint arXiv:1802.09464, 2018 | 514 | 2018 |
| Dynamics-aware unsupervised discovery of skills A Sharma, S Gu, S Levine, V Kumar, K Hausman arXiv preprint arXiv:1907.01657, 2019 | 404 | 2019 |
| Deep dynamics models for learning dexterous manipulation A Nagabandi, K Konolige, S Levine, V Kumar Conference on Robot Learning, 1101-1112, 2020 | 387 | 2020 |
| Learning latent plans from play C Lynch, M Khansari, T Xiao, V Kumar, J Tompson, S Levine, P Sermanet Conference on robot learning, 1113-1132, 2020 | 350 | 2020 |
| Relay policy learning: Solving long-horizon tasks via imitation and reinforcement learning A Gupta, V Kumar, C Lynch, S Levine, K Hausman arXiv preprint arXiv:1910.11956, 2019 | 324 | 2019 |
| R3M: A universal visual representation for robot manipulation S Nair, A Rajeswaran, V Kumar, C Finn, A Gupta arXiv preprint arXiv:2203.12601, 2022 | 301 | 2022 |
| Optimal control with learned local models: Application to dexterous manipulation V Kumar, E Todorov, S Levine 2016 IEEE International Conference on Robotics and Automation (ICRA), 378-383, 2016 | 258 | 2016 |
| AspWood: High-Spatial-Resolution Transcriptome Profiles Reveal Uncharacterized Modularity of Wood Formation in Populus tremula D Sundell, NR Street, M Kumar, EJ Mellerowicz, M Kucukoglu, ... The Plant Cell 29 (7), 1585-1604, 2017 | 207 | 2017 |
| Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost H Zhu, A Gupta, A Rajeswaran, S Levine, V Kumar 2019 International Conference on Robotics and Automation (ICRA), 3651-3657, 2019 | 206 | 2019 |
| Domain randomization and generative models for robotic grasping J Tobin, L Biewald, R Duan, M Andrychowicz, A Handa, V Kumar, ... 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2018 | 181 | 2018 |
| An integrated system for real-time model predictive control of humanoid robots T Erez, K Lowrey, Y Tassa, V Kumar, S Kolev, E Todorov 2013 13th IEEE-RAS International conference on humanoid robots (Humanoids …, 2013 | 174 | 2013 |
| Variance reduction for policy gradient with action-dependent factorized baselines C Wu, A Rajeswaran, Y Duan, V Kumar, AM Bayen, S Kakade, I Mordatch, ... arXiv preprint arXiv:1803.07246, 2018 | 169 | 2018 |
| The ingredients of real-world robotic reinforcement learning H Zhu, J Yu, A Gupta, D Shah, K Hartikainen, A Singh, V Kumar, S Levine arXiv preprint arXiv:2004.12570, 2020 | 164 | 2020 |
| A game theoretic framework for model based reinforcement learning A Rajeswaran, I Mordatch, V Kumar International conference on machine learning, 7953-7963, 2020 | 130 | 2020 |
| Vip: Towards universal visual reward and representation via value-implicit pre-training YJ Ma, S Sodhani, D Jayaraman, O Bastani, V Kumar, A Zhang arXiv preprint arXiv:2210.00030, 2022 | 127 | 2022 |
| Soft actor-critic algorithms and applications. arXiv 2018 T Haarnoja, A Zhou, K Hartikainen, G Tucker, S Ha, J Tan, V Kumar, ... arXiv preprint arXiv:1812.05905, 1812 | 127 | 1812 |
| Divide-and-conquer reinforcement learning D Ghosh, A Singh, A Rajeswaran, V Kumar, S Levine arXiv preprint arXiv:1711.09874, 2017 | 121 | 2017 |
| Robel: Robotics benchmarks for learning with low-cost robots M Ahn, H Zhu, K Hartikainen, H Ponte, A Gupta, S Levine, V Kumar Conference on robot learning, 1300-1313, 2020 | 115 | 2020 |
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
