| Categorical reparameterization with gumbel-softmax E Jang, S Gu, B Poole arXiv preprint arXiv:1611.01144, 2016 | 4157 | 2016 |
| Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation D Kalashnikov, A Irpan, P Pastor, J Ibarz, A Herzog, E Jang, D Quillen, ... arXiv preprint arXiv:1806.10293, 2018 | 703 | 2018 |
| Time-contrastive networks: Self-supervised learning from video P Sermanet, C Lynch, Y Chebotar, J Hsu, E Jang, S Schaal, S Levine, ... 2018 IEEE international conference on robotics and automation (ICRA), 1134-1141, 2018 | 547 | 2018 |
| Scalable deep reinforcement learning for vision-based robotic manipulation D Kalashnikov, A Irpan, P Pastor, J Ibarz, A Herzog, E Jang, D Quillen, ... Conference on Robot Learning, 651-673, 2018 | 469 | 2018 |
| Do as i can, not as i say: Grounding language in robotic affordances M Ahn, A Brohan, N Brown, Y Chebotar, O Cortes, B David, C Finn, ... arXiv preprint arXiv:2204.01691, 2022 | 241 | 2022 |
| Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods D Quillen, E Jang, O Nachum, C Finn, J Ibarz, S Levine 2018 IEEE International Conference on Robotics and Automation (ICRA), 6284-6291, 2018 | 208 | 2018 |
| Waic, but why? generative ensembles for robust anomaly detection H Choi, E Jang, AA Alemi arXiv preprint arXiv:1810.01392, 2018 | 185 | 2018 |
| Google Brain. Time-contrastive networks: Self-supervised learning from video P Sermanet, C Lynch, Y Chebotar, J Hsu, E Jang, S Schaal, S Levine 2018 IEEE international conference on robotics and automation (ICRA) 3, 2018 | 156 | 2018 |
| Categorical reparametrization with gumble-softmax E Jang, S Gu, B Poole International Conference on Learning Representations (ICLR 2017), 2017 | 126 | 2017 |
| Bc-z: Zero-shot task generalization with robotic imitation learning E Jang, A Irpan, M Khansari, D Kappler, F Ebert, C Lynch, S Levine, ... Conference on Robot Learning, 991-1002, 2022 | 113 | 2022 |
| Grasp2vec: Learning object representations from self-supervised grasping E Jang, C Devin, V Vanhoucke, S Levine arXiv preprint arXiv:1811.06964, 2018 | 108 | 2018 |
| Sim2real viewpoint invariant visual servoing by recurrent control F Sadeghi, A Toshev, E Jang, S Levine Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2018 | 102 | 2018 |
| End-to-end learning of semantic grasping E Jang, S Vijayanarasimhan, P Pastor, J Ibarz, S Levine arXiv preprint arXiv:1707.01932, 2017 | 91 | 2017 |
| Categorical reparameterization with gumbel-softmax. arXiv 2016 E Jang, S Gu, B Poole arXiv preprint arXiv:1611.01144, 2016 | 78 | 2016 |
| Meta-learning requires meta-augmentation J Rajendran, A Irpan, E Jang Advances in Neural Information Processing Systems 33, 5705-5715, 2020 | 63 | 2020 |
| Generative ensembles for robust anomaly detection H Choi, E Jang | 62 | 2018 |
| Multi-game decision transformers KH Lee, O Nachum, MS Yang, L Lee, D Freeman, S Guadarrama, ... Advances in Neural Information Processing Systems 35, 27921-27936, 2022 | 55 | 2022 |
| Sim2real view invariant visual servoing by recurrent control F Sadeghi, A Toshev, E Jang, S Levine arXiv preprint arXiv:1712.07642, 2017 | 47 | 2017 |
| Retinagan: An object-aware approach to sim-to-real transfer D Ho, K Rao, Z Xu, E Jang, M Khansari, Y Bai 2021 IEEE International Conference on Robotics and Automation (ICRA), 10920 …, 2021 | 44 | 2021 |
| Watch, try, learn: Meta-learning from demonstrations and reward A Zhou, E Jang, D Kappler, A Herzog, M Khansari, P Wohlhart, Y Bai, ... arXiv preprint arXiv:1906.03352, 2019 | 44 | 2019 |
