| GPT-4 technical report OpenAI arXiv, 2023 | 23782* | 2023 |
| Openai o1 system card A Jaech, A Kalai, A Lerer, A Richardson, A El-Kishky, A Low, A Helyar, ... arXiv preprint arXiv:2412.16720, 2024 | 1518 | 2024 |
| Offline reinforcement learning with implicit q-learning I Kostrikov, A Nair, S Levine International Conference on Learning Representations, 2021, 2021 | 1401 | 2021 |
| Overcoming exploration in reinforcement learning with demonstrations A Nair, B McGrew, M Andrychowicz, W Zaremba, P Abbeel IEEE International Conference on Robotics and Automation, 2017 | 1132 | 2017 |
| AWAC: Accelerating Online Reinforcement Learning with Offline Datasets A Nair, A Gupta, M Dalal, S Levine | 863 | 2020 |
| Visual reinforcement learning with imagined goals A Nair, V Pong, M Dalal, S Bahl, S Lin, S Levine Advances in Neural Information Processing Systems, 2018 | 720 | 2018 |
| Learning to poke by poking: Experiential learning of intuitive physics P Agrawal, A Nair, P Abbeel, J Malik, S Levine Advances in Neural Information Processing Systems, 2016 | 677 | 2016 |
| Residual reinforcement learning for robot control T Johannink, S Bahl, A Nair, J Luo, A Kumar, M Loskyll, JA Ojea, ... IEEE Conference on Robotics and Automation, 2018 | 658 | 2018 |
| Combining self-supervised learning and imitation for vision-based rope manipulation A Nair, D Chen, P Agrawal, P Isola, P Abbeel, J Malik, S Levine IEEE International Conference on Robotics and Automation, 2017 | 379 | 2017 |
| Skew-fit: State-covering self-supervised reinforcement learning VH Pong, M Dalal, S Lin, A Nair, S Bahl, S Levine International Conference on Machine Learning, 2019 | 350 | 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 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 | 260 | 2019 |
| Meta-reinforcement learning for robotic industrial insertion tasks G Schoettler, A Nair, JA Ojea, S Levine, E Solowjow 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020 | 119 | 2020 |
| Contextual imagined goals for self-supervised robotic learning A Nair, S Bahl, A Khazatsky, V Pong, G Berseth, S Levine Conference on Robot Learning, 2019 | 104 | 2019 |
| Offline meta-reinforcement learning with online self-supervision VH Pong, AV Nair, LM Smith, C Huang, S Levine International Conference on Machine Learning, 17811-17829, 2022 | 98 | 2022 |
| Bisimulation makes analogies in goal-conditioned reinforcement learning P Hansen-Estruch, A Zhang, A Nair, P Yin, S Levine International Conference on Machine Learning, 8407-8426, 2022 | 59 | 2022 |
| What Can I Do Here? Learning New Skills by Imagining Visual Affordances A Khazatsky, A Nair, D Jing, S Levine IEEE Interational Conference on Robotics and Automation, 2021 | 52 | 2021 |
| Planning to practice: Efficient online fine-tuning by composing goals in latent space K Fang, P Yin, A Nair, S Levine 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2022 | 37 | 2022 |
| Generalization with lossy affordances: Leveraging broad offline data for learning visuomotor tasks K Fang, P Yin, A Nair, HR Walke, G Yan, S Levine Conference on Robot Learning, 106-117, 2023 | 34 | 2023 |
| Learning to reason with LLMs OpenAI | 28 | 2024 |
| Disco rl: Distribution-conditioned reinforcement learning for general-purpose policies S Nasiriany, VH Pong, A Nair, A Khazatsky, G Berseth, S Levine 2021 IEEE International Conference on Robotics and Automation (ICRA), 6635-6641, 2021 | 22 | 2021 |
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
