| Sim-to-real transfer of robotic control with dynamics randomization XB Peng, M Andrychowicz, W Zaremba, P Abbeel 2018 IEEE international conference on robotics and automation (ICRA), 3803-3810, 2018 | 1109 | 2018 |
| Deepmimic: Example-guided deep reinforcement learning of physics-based character skills XB Peng, P Abbeel, S Levine, M Van de Panne ACM Transactions On Graphics (TOG) 37 (4), 1-14, 2018 | 774 | 2018 |
| Deeploco: Dynamic locomotion skills using hierarchical deep reinforcement learning XB Peng, G Berseth, KK Yin, M Van De Panne ACM Transactions on Graphics (TOG) 36 (4), 1-13, 2017 | 553 | 2017 |
| Terrain-adaptive locomotion skills using deep reinforcement learning XB Peng, G Berseth, M Van de Panne ACM Transactions on Graphics (TOG) 35 (4), 1-12, 2016 | 295 | 2016 |
| 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 | 289 | 2020 |
| Advantage-weighted regression: Simple and scalable off-policy reinforcement learning XB Peng, A Kumar, G Zhang, S Levine arXiv preprint arXiv:1910.00177, 2019 | 232 | 2019 |
| Sfv: Reinforcement learning of physical skills from videos XB Peng, A Kanazawa, J Malik, P Abbeel, S Levine ACM Transactions On Graphics (TOG) 37 (6), 1-14, 2018 | 196 | 2018 |
| Variational discriminator bottleneck: Improving imitation learning, inverse rl, and gans by constraining information flow XB Peng, A Kanazawa, S Toyer, P Abbeel, S Levine arXiv preprint arXiv:1810.00821, 2018 | 193 | 2018 |
| Learning locomotion skills using deeprl: Does the choice of action space matter? XB Peng, M Van De Panne Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation …, 2017 | 152 | 2017 |
| Mcp: Learning composable hierarchical control with multiplicative compositional policies XB Peng, M Chang, G Zhang, P Abbeel, S Levine Advances in Neural Information Processing Systems 32, 2019 | 150 | 2019 |
| AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control XB Peng, Z Ma, P Abbeel, S Levine, A Kanazawa ACM Transactions on Graphics (TOG) 40 (4), 1-20, 2021 | 111 | 2021 |
| Reinforcement learning for robust parameterized locomotion control of bipedal robots Z Li, X Cheng, XB Peng, P Abbeel, S Levine, G Berseth, K Sreenath 2021 IEEE International Conference on Robotics and Automation (ICRA), 2811-2817, 2021 | 101 | 2021 |
| Dynamic terrain traversal skills using reinforcement learning XB Peng, G Berseth, M Van de Panne ACM Transactions on Graphics (TOG) 34 (4), 1-11, 2015 | 88 | 2015 |
| Deep reinforcement learning for modeling human locomotion control in neuromechanical simulation S Song, £ Kidziñski, XB Peng, C Ong, J Hicks, S Levine, CG Atkeson, ... Journal of neuroengineering and rehabilitation 18, 1-17, 2021 | 54 | 2021 |
| Offline meta-reinforcement learning with advantage weighting E Mitchell, R Rafailov, XB Peng, S Levine, C Finn International Conference on Machine Learning, 7780-7791, 2021 | 54 | 2021 |
| Reward-conditioned policies A Kumar, XB Peng, S Levine arXiv preprint arXiv:1912.13465, 2019 | 49 | 2019 |
| Ase: Large-scale reusable adversarial skill embeddings for physically simulated characters XB Peng, Y Guo, L Halper, S Levine, S Fidler ACM Transactions On Graphics (TOG) 41 (4), 1-17, 2022 | 39 | 2022 |
| On learning symmetric locomotion F Abdolhosseini, HY Ling, Z Xie, XB Peng, M Van de Panne Proceedings of the 12th ACM SIGGRAPH Conference on Motion, Interaction and …, 2019 | 39 | 2019 |
| Reinforcement learning with competitive ensembles of information-constrained primitives A Goyal, S Sodhani, J Binas, XB Peng, S Levine, Y Bengio arXiv preprint arXiv:1906.10667, 2019 | 37 | 2019 |
| 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 | 32 | 2022 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
