| PRECOG: PREdiction Conditioned On Goals in Visual Multi-Agent Settings N Rhinehart, R McAllister, K Kitani, S Levine Proceedings of the IEEE International Conference on Computer Vision, 2019 | 427 | 2019 |
| R2P2: A Reparameterized Pushforward Policy for Diverse, Precise Generative Path Forecasting N Rhinehart, KM Kitani, P Vernaza Proceedings of the European Conference on Computer Vision (ECCV), 772-788, 2018 | 273 | 2018 |
| N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning A Ashok, N Rhinehart, F Beainy, KM Kitani International Conference on Learning Representations (ICLR), 2018 | 213 | 2018 |
| Can autonomous vehicles identify, recover from, and adapt to distribution shifts? A Filos, P Tigkas, R McAllister, N Rhinehart, S Levine, Y Gal International Conference on Machine Learning, 3145-3153, 2020 | 212 | 2020 |
| Deep Imitative Models for Flexible Inference, Planning, and Control N Rhinehart, R McAllister, S Levine International Conference on Learning Representations (ICLR), 2020 | 162 | 2020 |
| First-Person Activity Forecasting with Online Inverse Reinforcement Learning N Rhinehart, KM Kitani The IEEE International Conference on Computer Vision (ICCV), 3716-3725, 2017 | 162 | 2017 |
| Parrot: Data-driven behavioral priors for reinforcement learning A Singh, H Liu, G Zhou, A Yu, N Rhinehart, S Levine arXiv preprint arXiv:2011.10024, 2020 | 147 | 2020 |
| Conservative safety critics for exploration H Bharadhwaj, A Kumar, N Rhinehart, S Levine, F Shkurti, A Garg arXiv preprint arXiv:2010.14497, 2020 | 143 | 2020 |
| Ving: Learning open-world navigation with visual goals D Shah, B Eysenbach, G Kahn, N Rhinehart, S Levine 2021 IEEE International Conference on Robotics and Automation (ICRA), 13215 …, 2021 | 82 | 2021 |
| Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information A Sharma, M Sharma, N Rhinehart, KM Kitani International Conference on Learning Representations (ICLR), 2019 | 79 | 2019 |
| Inverting the pose forecasting pipeline with SPF2: Sequential pointcloud forecasting for sequential pose forecasting X Weng, J Wang, S Levine, K Kitani, N Rhinehart Conference on robot learning, 11-20, 2021 | 70* | 2021 |
| Rapid exploration for open-world navigation with latent goal models D Shah, B Eysenbach, G Kahn, N Rhinehart, S Levine arXiv preprint arXiv:2104.05859, 2021 | 58 | 2021 |
| Learning Action Maps of Large Environments Via First-Person Vision N Rhinehart, KM Kitani The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016 | 54 | 2016 |
| SMiRL: Surprise Minimizing RL in Dynamic Environments G Berseth, D Geng, C Devin, N Rhinehart, C Finn, D Jayaraman, S Levine arXiv preprint arXiv:1912.05510, 2019 | 51* | 2019 |
| Predictive-state decoders: Encoding the future into recurrent networks A Venkatraman*, N Rhinehart*, W Sun, L Pinto, M Hebert, B Boots, ... Advances in Neural Information Processing Systems, 1172-1183, 2017 | 45 | 2017 |
| First-Person Activity Forecasting from Video with Online Inverse Reinforcement Learning N Rhinehart, K Kitani IEEE Transactions on Pattern Analysis and Machine Intelligence, 2018 | 36 | 2018 |
| Generative Hybrid Representations for Activity Forecasting with No-Regret Learning J Guan, Y Yuan, KM Kitani, N Rhinehart arXiv preprint arXiv:1904.06250, 2019 | 31 | 2019 |
| Contingencies from observations: Tractable contingency planning with learned behavior models N Rhinehart, J He, C Packer, MA Wright, R McAllister, JE Gonzalez, ... 2021 IEEE International Conference on Robotics and Automation (ICRA), 13663 …, 2021 | 30 | 2021 |
| The waymo open sim agents challenge N Montali, J Lambert, P Mougin, A Kuefler, N Rhinehart, M Li, C Gulino, ... Advances in Neural Information Processing Systems 36, 2024 | 29 | 2024 |
| Offline reinforcement learning for visual navigation D Shah, A Bhorkar, H Leen, I Kostrikov, N Rhinehart, S Levine arXiv preprint arXiv:2212.08244, 2022 | 18 | 2022 |
Sergey LevineUC Berkeley, Physical IntelligenceVerified email at eecs.berkeley.edu
