| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 2372 | 2023 |
| PaLM-E: An Embodied Multimodal Language Model D Driess, F Xia, MSM Sajjadi, C Lynch, A Chowdhery, B Ichter, A Wahid, ... International Conference on Machine Learning (ICML), 2023 | 1567 | 2023 |
| Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning T Yu, D Quillen, Z He, R Julian, K Hausman, C Finn, S Levine Conference on Robot Learning (CoRL), 2019 | 1186 | 2019 |
| Gradient Surgery for Multi-Task Learning T Yu, S Kumar, A Gupta, S Levine, K Hausman, C Finn Neural Information Processing Systems (NeurIPS), 2020 | 1105 | 2020 |
| MOPO: Model-based Offline Policy Optimization T Yu, G Thomas, L Yu, S Ermon, J Zou, S Levine, C Finn, T Ma Neural Information Processing Systems (NeurIPS), 2020 | 891 | 2020 |
| RT-1: Robotics Transformer for Real-World Control at Scale A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ... Robotics: Science and Systems (RSS), 2023 | 889 | 2023 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 862 | 2024 |
| Real-time user-guided image colorization with learned deep priors R Zhang, JY Zhu, P Isola, X Geng, AS Lin, T Yu, AA Efros ACM Transactions on Graphics (TOG) 36 (4), 119:1--119:11, 2017 | 820 | 2017 |
| RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control A Brohan, N Brown, J Carbajal, Y Chebotar, X Chen, K Choromanski, ... arXiv preprint arXiv:2307.15818, 2023 | 753 | 2023 |
| One-shot visual imitation learning via meta-learning C Finn*, T Yu*, T Zhang, P Abbeel, S Levine Conference on Robot Learning (CoRL), 2017 | 664 | 2017 |
| COMBO: Conservative Offline Model-Based Policy Optimization T Yu, A Kumar, R Rafailov, A Rajeswaran, S Levine, C Finn Neural Information Processing Systems (NeurIPS), 2021 | 450 | 2021 |
| One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning T Yu, C Finn, A Xie, S Dasari, T Zhang, P Abbeel, S Levine Robotics: Science and Systems (RSS), 2018 | 418 | 2018 |
| 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 | 288* | 2023 |
| Efficiently Identifying Task Groupings for Multi-Task Learning C Fifty, E Amid, Z Zhao, T Yu, R Anil, C Finn Neural Information Processing Systems (NeurIPS), 2021 | 279 | 2021 |
| RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control B Zitkovich, T Yu, S Xu, P Xu, T Xiao, F Xia, J Wu, P Wohlhart, S Welker, ... 7th Annual Conference on Robot Learning, 2023 | 189 | 2023 |
| Offline Reinforcement Learning from Images with Latent Space Models R Rafailov*, T Yu*, A Rajeswaran, C Finn Learning for Dynamics and Control (L4DC), 1154-1168, 2021 | 132 | 2021 |
| Open X-Embodiment: Robotic Learning Datasets and RT-X Models : Open X-Embodiment Collaboration0 A O’Neill, A Rehman, A Maddukuri, A Gupta, A Padalkar, A Lee, A Pooley, ... 2024 IEEE International Conference on Robotics and Automation (ICRA), 6892-6903, 2024 | 131 | 2024 |
| Scaling Robot Learning with Semantically Imagined Experience T Yu, T Xiao, A Stone, J Tompson, A Brohan, S Wang, J Singh, C Tan, ... arXiv preprint arXiv:2302.11550, 2023 | 118 | 2023 |
| Meta-Inverse Reinforcement Learning with Probabilistic Context Variables L Yu*, T Yu*, C Finn, S Ermon Neural Information Processing Systems (NeurIPS), 2019 | 89 | 2019 |
| Generalizing Skills with Semi-Supervised Reinforcement Learning C Finn, T Yu, J Fu, P Abbeel, S Levine International Conference on Learning Representations (ICLR), 2016 | 88 | 2016 |
Chelsea FinnStanford University, Physical IntelligenceAdresse e-mail validée de cs.stanford.edu
