| Visual reinforcement learning with imagined goals AV Nair, V Pong, M Dalal, S Bahl, S Lin, S Levine Advances in neural information processing systems 31, 2018 | 409 | 2018 |
| Residual reinforcement learning for robot control T Johannink, S Bahl, A Nair, J Luo, A Kumar, M Loskyll, JA Ojea, ... 2019 International Conference on Robotics and Automation (ICRA), 6023-6029, 2019 | 252 | 2019 |
| Skew-fit: State-covering self-supervised reinforcement learning VH Pong, M Dalal, S Lin, A Nair, S Bahl, S Levine arXiv preprint arXiv:1903.03698, 2019 | 179 | 2019 |
| Deep reinforcement learning for industrial insertion tasks with visual inputs and natural rewards G Schoettler, A Nair, J Luo, S Bahl, JA Ojea, E Solowjow, S Levine arXiv preprint arXiv:1906.05841, 2019 | 121 | 2019 |
| Contextual imagined goals for self-supervised robotic learning A Nair, S Bahl, A Khazatsky, V Pong, G Berseth, S Levine Conference on Robot Learning, 530-539, 2020 | 59 | 2020 |
| Neural dynamic policies for end-to-end sensorimotor learning S Bahl, M Mukadam, A Gupta, D Pathak Advances in Neural Information Processing Systems 33, 5058-5069, 2020 | 36 | 2020 |
| Impact on inequities in health indicators: Effect of implementing the integrated management of neonatal and childhood illness programme in Haryana, India S Taneja, S Bahl, S Mazumder, J Martines, N Bhandari, MK Bhan Journal of global health 5 (1), 2015 | 11 | 2015 |
| Hierarchical neural dynamic policies S Bahl, A Gupta, D Pathak arXiv preprint arXiv:2107.05627, 2021 | 10 | 2021 |
| Human-to-robot imitation in the wild S Bahl, A Gupta, D Pathak arXiv preprint arXiv:2207.09450, 2022 | 7 | 2022 |
| Rb2: Robotic manipulation benchmarking with a twist S Dasari, J Wang, J Hong, S Bahl, Y Lin, A Wang, A Thankaraj, K Chahal, ... arXiv preprint arXiv:2203.08098, 2022 | 4 | 2022 |
| VideoDex: Learning Dexterity from Internet Videos K Shaw, S Bahl, D Pathak arXiv preprint arXiv:2212.04498, 2022 | | 2022 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
