| End-to-end training of deep visuomotor policies S Levine, C Finn, T Darrell, P Abbeel The Journal of Machine Learning Research 17 (1), 1334-1373, 2016 | 1552 | 2016 |
| Model-agnostic meta-learning for fast adaptation of deep networks C Finn, P Abbeel, S Levine International Conference on Machine Learning (ICML), 1126-1135, 2017 | 1225 | 2017 |
| Unsupervised learning for physical interaction through video prediction C Finn, I Goodfellow, S Levine Advances in neural information processing systems, 64-72, 2016 | 457 | 2016 |
| Guided cost learning: Deep inverse optimal control via policy optimization C Finn, S Levine, P Abbeel International Conference on Machine Learning (ICML), 49-58, 2016 | 305 | 2016 |
| Deep spatial autoencoders for visuomotor learning C Finn, XY Tan, Y Duan, T Darrell, S Levine, P Abbeel 2016 IEEE International Conference on Robotics and Automation (ICRA), 512-519, 2016 | 268* | 2016 |
| Deep visual foresight for planning robot motion C Finn, S Levine 2017 IEEE International Conference on Robotics and Automation (ICRA), 2786-2793, 2017 | 251 | 2017 |
| One-shot visual imitation learning via meta-learning C Finn, T Yu, T Zhang, P Abbeel, S Levine Conference on Robot Learning (CoRL), 2017 | 152 | 2017 |
| Stochastic variational video prediction M Babaeizadeh, C Finn, D Erhan, RH Campbell, S Levine International Conference on Learning Representations (ICLR), 2017 | 130 | 2017 |
| A connection between generative adversarial networks, inverse reinforcement learning, and energy-based models C Finn, P Christiano, P Abbeel, S Levine NeurIPS Workshop on Adversarial Training, 2016 | 114 | 2016 |
| Recasting gradient-based meta-learning as hierarchical bayes E Grant, C Finn, S Levine, T Darrell, T Griffiths International Conference on Learning Representations (ICLR), 2018 | 113 | 2018 |
| Stochastic adversarial video prediction AX Lee, R Zhang, F Ebert, P Abbeel, C Finn, S Levine arXiv preprint arXiv:1804.01523, 2018 | 96 | 2018 |
| Probabilistic model-agnostic meta-learning C Finn, K Xu, S Levine Advances in Neural Information Processing Systems, 9516-9527, 2018 | 96 | 2018 |
| 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 | 92 | 2018 |
| Self-supervised visual planning with temporal skip connections F Ebert, C Finn, AX Lee, S Levine Conference on Robot Learning (CoRL), 2017 | 83 | 2017 |
| Learning to adapt in dynamic, real-world environments through meta-reinforcement learning A Nagabandi, I Clavera, S Liu, RS Fearing, P Abbeel, S Levine, C Finn International Conference on Learning Representations (ICLR), 2019 | 75* | 2019 |
| Universal planning networks A Srinivas, A Jabri, P Abbeel, S Levine, C Finn International Conference on Machine Learning (ICML), 2018 | 72 | 2018 |
| Towards adapting deep visuomotor representations from simulated to real environments E Tzeng, C Devin, J Hoffman, C Finn, X Peng, S Levine, K Saenko, ... arXiv preprint arXiv:1511.07111 2 (3), 2015 | 72 | 2015 |
| Meta-learning and universality: Deep representations and gradient descent can approximate any learning algorithm C Finn, S Levine International Conference on Learning Representations (ICLR), 2018 | 61 | 2018 |
| Model-based reinforcement learning for atari L Kaiser, M Babaeizadeh, P Milos, B Osinski, RH Campbell, ... International Conference on Learning Representations (ICLR), 2020 | 57 | 2020 |
| Adapting deep visuomotor representations with weak pairwise constraints E Tzeng, C Devin, J Hoffman, C Finn, P Abbeel, S Levine, K Saenko, ... arXiv preprint arXiv:1511.07111, 2015 | 53 | 2015 |
Sergey LevineUC Berkeley, GoogleVerified email at eecs.berkeley.edu
