| Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours L Pinto, A Gupta 2016 IEEE international conference on robotics and automation (ICRA), 3406-3413, 2016 | 1090 | 2016 |
| Robust adversarial reinforcement learning L Pinto, J Davidson, R Sukthankar, A Gupta Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017 | 665 | 2017 |
| Reinforcement learning with augmented data M Laskin, K Lee, A Stooke, L Pinto, P Abbeel, A Srinivas Advances in neural information processing systems 33, 19884-19895, 2020 | 366 | 2020 |
| Learning to fly by crashing D Gandhi, L Pinto, A Gupta 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2017 | 320 | 2017 |
| Asymmetric actor critic for image-based robot learning L Pinto, M Andrychowicz, P Welinder, W Zaremba, P Abbeel Robotics: Science and Systems, 2018, 2017 | 252 | 2017 |
| The curious robot: Learning visual representations via physical interactions L Pinto, D Gandhi, Y Han, YL Park, A Gupta Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The …, 2016 | 194 | 2016 |
| Learning to push by grasping: Using multiple tasks for effective learning L Pinto, A Gupta 2017 IEEE international conference on robotics and automation (ICRA), 2161-2168, 2017 | 128 | 2017 |
| Learning to manipulate deformable objects without demonstrations Y Wu, W Yan, T Kurutach, L Pinto, P Abbeel Robotics: Science and Systems, 2020, 2019 | 117 | 2019 |
| Learning predictive representations for deformable objects using contrastive estimation W Yan, A Vangipuram, P Abbeel, L Pinto Conference on Robot Learning, 564-574, 2021 | 116 | 2021 |
| Robot learning in homes: Improving generalization and reducing dataset bias A Gupta, A Murali, DP Gandhi, L Pinto Advances in neural information processing systems 31, 2018 | 110 | 2018 |
| Reinforcement learning with prototypical representations D Yarats, R Fergus, A Lazaric, L Pinto International Conference on Machine Learning, 11920-11931, 2021 | 95 | 2021 |
| Pyrobot: An open-source robotics framework for research and benchmarking A Murali, T Chen, KV Alwala, D Gandhi, L Pinto, S Gupta, A Gupta arXiv preprint arXiv:1906.08236, 2019 | 82 | 2019 |
| Supervision via competition: Robot adversaries for learning tasks L Pinto, J Davidson, A Gupta 2017 IEEE International Conference on Robotics and Automation (ICRA), 1601-1608, 2017 | 82 | 2017 |
| Self-supervised policy adaptation during deployment N Hansen, R Jangir, Y Sun, G Alenyà, P Abbeel, AA Efros, L Pinto, ... arXiv preprint arXiv:2007.04309, 2020 | 80 | 2020 |
| Mastering visual continuous control: Improved data-augmented reinforcement learning D Yarats, R Fergus, A Lazaric, L Pinto arXiv preprint arXiv:2107.09645, 2021 | 79 | 2021 |
| Multiple interactions made easy (mime): Large scale demonstrations data for imitation P Sharma, L Mohan, L Pinto, A Gupta Conference on Robot Learning, 2018, 2018 | 58 | 2018 |
| State-only imitation learning for dexterous manipulation I Radosavovic, X Wang, L Pinto, J Malik 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021 | 56 | 2021 |
| A framework for efficient robotic manipulation A Zhan, R Zhao, L Pinto, P Abbeel, M Laskin Deep RL Workshop NeurIPS 2021, 2020 | 54 | 2020 |
| Automatic curriculum learning through value disagreement Y Zhang, P Abbeel, L Pinto Advances in Neural Information Processing Systems 33, 7648-7659, 2020 | 51 | 2020 |
| URLB: Unsupervised reinforcement learning benchmark M Laskin, D Yarats, H Liu, K Lee, A Zhan, K Lu, C Cang, L Pinto, P Abbeel arXiv preprint arXiv:2110.15191, 2021 | 43 | 2021 |
Abhinav GuptaAssociate Professor, Robotics Institute, Carnegie Mellon University在 cs.cmu.edu 的电子邮件经过验证
