| Self-supervised policy adaptation during deployment N Hansen, R Jangir, Y Sun, G Alenyà, P Abbeel, AA Efros, L Pinto, ... arXiv preprint arXiv:2007.04309, 2020 | 148 | 2020 |
| Generalization in reinforcement learning by soft data augmentation N Hansen, X Wang 2021 IEEE International Conference on Robotics and Automation (ICRA), 13611 …, 2021 | 141 | 2021 |
| Temporal difference learning for model predictive control N Hansen, X Wang, H Su Proceedings of the 39th International Conference on Machine Learning, PMLR …, 2022 | 133 | 2022 |
| Open x-embodiment: Robotic learning datasets and rt-x models A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, A Irpan, A Khazatsky, ... arXiv preprint arXiv:2310.08864, 2023 | 118 | 2023 |
| Stabilizing deep q-learning with convnets and vision transformers under data augmentation N Hansen, H Su, X Wang Advances in neural information processing systems 34, 3680-3693, 2021 | 105 | 2021 |
| 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 | 78 | 2021 |
| On pre-training for visuo-motor control: Revisiting a learning-from-scratch baseline N Hansen, Z Yuan, Y Ze, T Mu, A Rajeswaran, H Su, H Xu, X Wang arXiv preprint arXiv:2212.05749, 2022 | 48* | 2022 |
| Look closer: Bridging egocentric and third-person views with transformers for robotic manipulation R Jangir, N Hansen, S Ghosal, M Jain, X Wang IEEE Robotics and Automation Letters 7 (2), 3046-3053, 2022 | 47 | 2022 |
| Gnfactor: Multi-task real robot learning with generalizable neural feature fields Y Ze, G Yan, YH Wu, A Macaluso, Y Ge, J Ye, N Hansen, LE Li, X Wang Conference on Robot Learning, 284-301, 2023 | 34 | 2023 |
| Graph inverse reinforcement learning from diverse videos S Kumar, J Zamora, N Hansen, R Jangir, X Wang Conference on Robot Learning, 55-66, 2023 | 34 | 2023 |
| Visual reinforcement learning with self-supervised 3d representations Y Ze, N Hansen, Y Chen, M Jain, X Wang IEEE Robotics and Automation Letters 8 (5), 2890-2897, 2023 | 33 | 2023 |
| Modem: Accelerating visual model-based reinforcement learning with demonstrations N Hansen, Y Lin, H Su, X Wang, V Kumar, A Rajeswaran arXiv preprint arXiv:2212.05698, 2022 | 33 | 2022 |
| Short term blood glucose prediction based on continuous glucose monitoring data A Mohebbi, AR Johansen, N Hansen, PE Christensen, JM Tarp, ... 2020 42nd Annual International Conference of the IEEE Engineering in …, 2020 | 24 | 2020 |
| Td-mpc2: Scalable, robust world models for continuous control N Hansen, H Su, X Wang arXiv preprint arXiv:2310.16828, 2023 | 23 | 2023 |
| Open X-Embodiment: Robotic learning datasets and RT-X models OXE Collaboration, A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, ... CoRR, abs/2310.08864, 2023 | 20 | 2023 |
| Open x-embodiment: Robotic learning datasets and RT-x models Q Vuong, S Levine, HR Walke, K Pertsch, A Singh, R Doshi, C Xu, J Luo, ... Towards Generalist Robots: Learning Paradigms for Scalable Skill Acquisition …, 2023 | 11 | 2023 |
| On the feasibility of cross-task transfer with model-based reinforcement learning Y Xu, N Hansen, Z Wang, YC Chan, H Su, Z Tu arXiv preprint arXiv:2210.10763, 2022 | 11 | 2022 |
| Finetuning offline world models in the real world Y Feng, N Hansen, Z Xiong, C Rajagopalan, X Wang arXiv preprint arXiv:2310.16029, 2023 | 10 | 2023 |
| MoDem-V2: Visuo-Motor World Models for Real-World Robot Manipulation P Lancaster, N Hansen, A Rajeswaran, V Kumar arXiv preprint arXiv:2309.14236, 2023 | 2 | 2023 |
| PWM: Policy Learning with Large World Models I Georgiev, V Giridhar, N Hansen, A Garg arXiv preprint arXiv:2407.02466, 2024 | | 2024 |
Xiaolong WangAssistant Professor, UC San Diego在 ucsd.edu 的电子邮件经过验证
