| 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 | 758 | 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 | 656 | 2020 |
| 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 | 654 | 2017 |
| 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 | 567 | 2020 |
| One-shot visual imitation learning via meta-learning C Finn*, T Yu*, T Zhang, P Abbeel, S Levine Conference on Robot Learning (CoRL), 2017 | 562 | 2017 |
| 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 | 388 | 2023 |
| 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 | 370 | 2018 |
| 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 | 251 | 2021 |
| 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 | 219 | 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 | 144 | 2021 |
| 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 | 94 | 2021 |
| 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 | 90 | 2023 |
| Generalizing Skills with Semi-Supervised Reinforcement Learning C Finn, T Yu, J Fu, P Abbeel, S Levine International Conference on Learning Representations (ICLR), 2016 | 80 | 2016 |
| One-Shot Hierarchical Imitation Learning of Compound Visuomotor Tasks T Yu, P Abbeel, S Levine, C Finn International Conference on Intelligent Robots and Systems (IROS), 2018 | 64 | 2018 |
| Meta-Inverse Reinforcement Learning with Probabilistic Context Variables L Yu*, T Yu*, C Finn, S Ermon Neural Information Processing Systems (NeurIPS), 2019 | 62 | 2019 |
| Conservative Data Sharing for Multi-Task Offline Reinforcement Learning T Yu, A Kumar, Y Chebotar, K Hausman, S Levine, C Finn Neural Information Processing Systems (NeurIPS), 2021 | 58 | 2021 |
| Unsupervised Visuomotor Control through Distributional Planning Networks T Yu, G Shevchuk, D Sadigh, C Finn Robotics: Science and Systems (RSS), 2019 | 46 | 2019 |
| How to Leverage Unlabeled Data in Offline Reinforcement Learning T Yu, A Kumar, Y Chebotar, K Hausman, C Finn, S Levine International Conference on Machine Learning (ICML), 2022 | 37 | 2022 |
| Visual Adversarial Imitation Learning using Variational Models R Rafailov, T Yu, A Rajeswaran, C Finn Neural Information Processing Systems (NeurIPS), 2021 | 31 | 2021 |
| On the Expressivity of Neural Networks for Deep Reinforcement Learning K Dong, Y Luo, T Yu, C Finn, T Ma International Conference on Machine Learning (ICML), 2020 | 28 | 2020 |
Chelsea FinnStanford University, GoogleVerified email at cs.stanford.edu
