| 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 | 1466 | 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, C Fu, ... arXiv preprint arXiv:2204.01691, 2022 | 899 | 2022 |
| Using simulation and domain adaptation to improve efficiency of deep robotic grasping K Bousmalis, A Irpan, P Wohlhart, Y Bai, M Kelcey, M Kalakrishnan, ... 2018 IEEE international conference on robotics and automation (ICRA), 4243-4250, 2018 | 722 | 2018 |
| Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks S James, P Wohlhart, M Kalakrishnan, D Kalashnikov, A Irpan, J Ibarz, ... Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019 | 493 | 2019 |
| Rt-1: Robotics transformer for real-world control at scale A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ... arXiv preprint arXiv:2212.06817, 2022 | 426 | 2022 |
| 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 | 309 | 2022 |
| 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 | 288 | 2023 |
| Do as i can, not as i say: Grounding language in robotic affordances A Brohan, Y Chebotar, C Finn, K Hausman, A Herzog, D Ho, J Ibarz, ... Conference on robot learning, 287-318, 2023 | 241 | 2023 |
| Deep reinforcement learning doesn’t work yet A Irpan | 183 | 2018 |
| Rl-cyclegan: Reinforcement learning aware simulation-to-real K Rao, C Harris, A Irpan, S Levine, J Ibarz, M Khansari Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 170 | 2020 |
| Actionable models: Unsupervised offline reinforcement learning of robotic skills Y Chebotar, K Hausman, Y Lu, T Xiao, D Kalashnikov, J Varley, A Irpan, ... arXiv preprint arXiv:2104.07749, 2021 | 134 | 2021 |
| Meta-learning requires meta-augmentation J Rajendran, A Irpan, E Jang Advances in Neural Information Processing Systems 33, 5705-5715, 2020 | 85 | 2020 |
| Noise contrastive priors for functional uncertainty D Hafner, D Tran, T Lillicrap, A Irpan, J Davidson Uncertainty in Artificial Intelligence, 905-914, 2020 | 83 | 2020 |
| 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 | 80 | 2023 |
| Reliable uncertainty estimates in deep neural networks using noise contrastive priors D Hafner, D Tran, A Irpan, T Lillicrap, J Davidson stat 1050, 24, 2018 | 68 | 2018 |
| Off-policy evaluation via off-policy classification A Irpan, K Rao, K Bousmalis, C Harris, J Ibarz, S Levine Advances in Neural Information Processing Systems 32, 2019 | 53 | 2019 |
| 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, ... Conference on Robot Learning, 2165-2183, 2023 | 50 | 2023 |
| Can deep reinforcement learning solve Erdos-Selfridge-Spencer games? M Raghu, A Irpan, J Andreas, B Kleinberg, Q Le, J Kleinberg International Conference on Machine Learning, 4238-4246, 2018 | 39 | 2018 |
| Scalable multi-task imitation learning with autonomous improvement A Singh, E Jang, A Irpan, D Kappler, M Dalal, S Levinev, M Khansari, ... 2020 IEEE International Conference on Robotics and Automation (ICRA), 2167-2173, 2020 | 37 | 2020 |
| Aw-opt: Learning robotic skills with imitation andreinforcement at scale Y Lu, K Hausman, Y Chebotar, M Yan, E Jang, A Herzog, T Xiao, A Irpan, ... Conference on Robot Learning, 1078-1088, 2022 | 32 | 2022 |
