| Open x-embodiment: Robotic learning datasets and rt-x models A O'Neill, A Rehman, A Gupta, A Maddukuri, A Gupta, A Padalkar, A Lee, ... arXiv preprint arXiv:2310.08864, 2023 | 476* | 2023 |
| 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 | 158 | 2020 |
| 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 | 34* | 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 | 27 | 2023 |
| 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, 44323-44340, 2023 | 16 | 2023 |
| 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 | 14 | 2022 |
| Dino-wm: World models on pre-trained visual features enable zero-shot planning G Zhou, H Pan, Y LeCun, L Pinto arXiv preprint arXiv:2411.04983, 2024 | 4 | 2024 |
| Navigation world models A Bar, G Zhou, D Tran, T Darrell, Y LeCun arXiv preprint arXiv:2412.03572, 2024 | 2 | 2024 |
