| 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 | 3028 | 2018 |
| Large-scale evolution of image classifiers E Real, S Moore, A Selle, S Saxena, YL Suematsu, J Tan, Q Le, A Kurakin International Conference on Machine Learning (ICML), 2017 | 2053 | 2017 |
| Sim-to-real: Learning agile locomotion for quadruped robots J Tan, T Zhang, E Coumans, A Iscen, Y Bai, D Hafner, S Bohez, ... arXiv preprint arXiv:1804.10332, 2018 | 915 | 2018 |
| How to train your robot with deep reinforcement learning: lessons we have learned J Ibarz, J Tan, C Finn, M Kalakrishnan, P Pastor, S Levine The International Journal of Robotics Research 40 (4-5), 698-721, 2021 | 645 | 2021 |
| Learning to walk via deep reinforcement learning T Haarnoja, S Ha, A Zhou, J Tan, G Tucker, S Levine arXiv preprint arXiv:1812.11103, 2018 | 569 | 2018 |
| Learning agile robotic locomotion skills by imitating animals XB Peng, E Coumans, T Zhang, TW Lee, J Tan, S Levine arXiv preprint arXiv:2004.00784, 2020 | 548 | 2020 |
| Adaptive power system emergency control using deep reinforcement learning Q Huang, R Huang, W Hao, J Tan, R Fan, Z Huang IEEE Transactions on Smart Grid 11 (2), 1171-1182, 2019 | 373 | 2019 |
| Open x-embodiment: Robotic learning datasets and rt-x models A O'Neill, A Rehman, A Gupta, A Maddukuri, A Gupta, A Padalkar, A Lee, ... arXiv preprint arXiv:2310.08864, 2023 | 363* | 2023 |
| Preparing for the unknown: Learning a universal policy with online system identification W Yu, J Tan, CK Liu, G Turk Robotics: Science and Systems (RSS), 2017 | 347 | 2017 |
| Language to rewards for robotic skill synthesis W Yu, N Gileadi, C Fu, S Kirmani, KH Lee, MG Arenas, HTL Chiang, ... arXiv preprint arXiv:2306.08647, 2023 | 231 | 2023 |
| Learning to walk in the real world with minimal human effort S Ha, P Xu, Z Tan, S Levine, J Tan arXiv preprint arXiv:2002.08550, 2020 | 191 | 2020 |
| On the use of simulation in robotics: Opportunities, challenges, and suggestions for moving forward HS Choi, C Crump, C Duriez, A Elmquist, G Hager, D Han, F Hearl, ... Proceedings of the National Academy of Sciences 118 (1), e1907856118, 2021 | 175 | 2021 |
| Data efficient reinforcement learning for legged robots Y Yang, K Caluwaerts, A Iscen, T Zhang, J Tan, V Sindhwani Conference on Robot Learning, 1-10, 2020 | 167 | 2020 |
| Learning to be safe: Deep rl with a safety critic K Srinivasan, B Eysenbach, S Ha, J Tan, C Finn arXiv preprint arXiv:2010.14603, 2020 | 162 | 2020 |
| Policies modulating trajectory generators A Iscen, K Caluwaerts, J Tan, T Zhang, E Coumans, V Sindhwani, ... Conference on Robot Learning, 916-926, 2018 | 145 | 2018 |
| Stable proportional-derivative controllers J Tan, K Liu, G Turk IEEE Computer Graphics and Applications 31 (4), 34-44, 2011 | 126 | 2011 |
| Articulated swimming creatures J Tan, Y Gu, G Turk, CK Liu ACM Transactions on Graphics (TOG) 30 (4), 1-12, 2011 | 119 | 2011 |
| Learning to dress: Synthesizing human dressing motion via deep reinforcement learning A Clegg, W Yu, J Tan, CK Liu, G Turk ACM Transactions on Graphics (TOG) 37 (6), 1-10, 2018 | 112 | 2018 |
| Learning bicycle stunts J Tan, Y Gu, CK Liu, G Turk ACM Transactions on Graphics (TOG) 33 (4), 1-12, 2014 | 108 | 2014 |
| Legged robots that keep on learning: Fine-tuning locomotion policies in the real world L Smith, JC Kew, XB Peng, S Ha, J Tan, S Levine 2022 International Conference on Robotics and Automation (ICRA), 1593-1599, 2022 | 107 | 2022 |
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