| Parrot: Data-driven behavioral priors for reinforcement learning A Singh, H Liu, G Zhou, A Yu, N Rhinehart, S Levine arXiv preprint arXiv:2011.10024, 2020 | 129 | 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 | 74 | 2023 |
| Real world offline reinforcement learning with realistic data source G Zhou, L Ke, S Srinivasa, A Gupta, A Rajeswaran, V Kumar 2023 IEEE International Conference on Robotics and Automation (ICRA), 7176-7183, 2023 | 16 | 2023 |
| Train offline, test online: A real robot learning benchmark G Zhou, V Dean, MK Srirama, A Rajeswaran, J Pari, K Hatch, A Jain, T Yu, ... 2023 IEEE International Conference on Robotics and Automation (ICRA), 9197-9203, 2023 | 11* | 2023 |
| Open X-Embodiment: Robotic learning datasets and RT-X models OXE Collaboration, A Padalkar, A Pooley, A Jain, A Bewley, A Herzog, ... CoRR, 2023 | 11 | 2023 |
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| Putting the con in context: Identifying deceptive actors in the game of mafia S Ibraheem, G Zhou, J DeNero arXiv preprint arXiv:2207.02253, 2022 | 5 | 2022 |
| RoboHive: A Unified Framework for Robot Learning V Kumar, R Shah, G Zhou, V Moens, V Caggiano, A Gupta, A Rajeswaran Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
