| End-to-end learning of driving models from large-scale video datasets H Xu, Y Gao, F Yu, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 1047 | 2017 |
| Natural language object retrieval R Hu, H Xu, M Rohrbach, J Feng, K Saenko, T Darrell Proceedings of the IEEE conference on computer vision and pattern …, 2016 | 637 | 2016 |
| Algorithmic Framework for Model-based Deep Reinforcement Learning with Theoretical Guarantees Y Luo, H Xu, Y Li, Y Tian, T Darrell, T Ma International Conference of Learning Representation (ICLR), https://arxiv …, 2019 | 254 | 2019 |
| Reinforcement learning from imperfect demonstrations Y Gao, H Xu, J Lin, F Yu, S Levine, T Darrell arXiv preprint arXiv:1802.05313, 2018 | 248 | 2018 |
| Multi-task reinforcement learning with soft modularization R Yang, H Xu, Y Wu, X Wang Advances in Neural Information Processing Systems 33, 4767-4777, 2020 | 189 | 2020 |
| Synthesizing long-term 3d human motion and interaction in 3d scenes J Wang, H Xu, J Xu, S Liu, X Wang Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 115 | 2021 |
| Disentangling propagation and generation for video prediction H Gao, H Xu, QZ Cai, R Wang, F Yu, T Darrell Proceedings of the IEEE/CVF international conference on computer vision …, 2019 | 106 | 2019 |
| Bebold: Exploration beyond the boundary of explored regions T Zhang, H Xu, X Wang, Y Wu, K Keutzer, JE Gonzalez, Y Tian arXiv preprint arXiv:2012.08621, 2020 | 105* | 2020 |
| Learning vision-guided quadrupedal locomotion end-to-end with cross-modal transformers R Yang, M Zhang, N Hansen, H Xu, X Wang arXiv preprint arXiv:2107.03996, 2021 | 89 | 2021 |
| Multi-person 3d motion prediction with multi-range transformers J Wang, H Xu, M Narasimhan, X Wang Advances in Neural Information Processing Systems 34, 6036-6049, 2021 | 58 | 2021 |
| Modular architecture for starcraft ii with deep reinforcement learning D Lee, H Tang, J Zhang, H Xu, T Darrell, P Abbeel Proceedings of the AAAI Conference on Artificial Intelligence and …, 2018 | 58 | 2018 |
| RoboCraft: Learning to see, simulate, and shape elasto-plastic objects in 3D with graph networks H Shi, H Xu, Z Huang, Y Li, J Wu The International Journal of Robotics Research 43 (4), 533-549, 2024 | 57 | 2024 |
| Pre-trained image encoder for generalizable visual reinforcement learning Z Yuan, Z Xue, B Yuan, X Wang, Y Wu, Y Gao, H Xu Advances in Neural Information Processing Systems 35, 13022-13037, 2022 | 57 | 2022 |
| Discovering diverse multi-agent strategic behavior via reward randomization Z Tang, C Yu, B Chen, H Xu, X Wang, F Fang, S Du, Y Wang, Y Wu arXiv preprint arXiv:2103.04564, 2021 | 55 | 2021 |
| Plan better amid conservatism: Offline multi-agent reinforcement learning with actor rectification L Pan, L Huang, T Ma, H Xu International conference on machine learning, 17221-17237, 2022 | 45 | 2022 |
| Multi-agent collaboration via reward attribution decomposition T Zhang, H Xu, X Wang, Y Wu, K Keutzer, JE Gonzalez, Y Tian arXiv preprint arXiv:2010.08531, 2020 | 40 | 2020 |
| See, hear, and feel: Smart sensory fusion for robotic manipulation H Li, Y Zhang, J Zhu, S Wang, MA Lee, H Xu, E Adelson, L Fei-Fei, R Gao, ... arXiv preprint arXiv:2212.03858, 2022 | 39 | 2022 |
| Robocook: Long-horizon elasto-plastic object manipulation with diverse tools H Shi, H Xu, S Clarke, Y Li, J Wu arXiv preprint arXiv:2306.14447, 2023 | 35 | 2023 |
| A Low-Complexity Linear Precoding Scheme Based on SOR Method for Massive MIMO Systems T Xie, Q Han, H Xu, Z Qi, W Shen IEEE VTC’15 Spring, 2015 | 35* | 2015 |
| Gensim: Generating robotic simulation tasks via large language models L Wang, Y Ling, Z Yuan, M Shridhar, C Bao, Y Qin, B Wang, H Xu, ... arXiv preprint arXiv:2310.01361, 2023 | 34 | 2023 |
Trevor DarrellProfessor of Computer Science, U.C. Berkeley在 eecs.berkeley.edu 的电子邮件经过验证
