| Behavioral cloning from observation F Torabi, G Warnell, P Stone International Joint Conference on Artificial Intelligence, 2018 | 840 | 2018 |
| Deep TAMER: Interactive agent shaping in high-dimensional state spaces G Warnell, N Waytowich, V Lawhern, P Stone AAAI Conference on Artificial Intelligence, 2018 | 303 | 2018 |
| Motion planning and control for mobile robot navigation using machine learning: a survey X Xiao, B Liu, G Warnell, P Stone Autonomous Robots, 1-29, 2022 | 254 | 2022 |
| Generative adversarial imitation from observation F Torabi, G Warnell, P Stone arXiv preprint arXiv:1807.06158, 2018 | 249 | 2018 |
| Recent advances in imitation learning from observation F Torabi, G Warnell, P Stone International Joint Conference on Artificial Intelligence, 2019 | 183 | 2019 |
| Socially compliant navigation dataset (SCAND): a large-scale dataset of demonstrations for social navigation H Karnan, A Nair, X Xiao, G Warnell, S Pirk, A Toshev, J Hart, J Biswas, ... IEEE Robotics and Automation Letters, 2022 | 102 | 2022 |
| Agents teaching agents: a survey on inter-agent transfer learning FL Da Silva, G Warnell, AHR Costa, P Stone Autonomous Agents and Multi-Agent Systems 34 (1), 1-17, 2020 | 100 | 2020 |
| APPLD: Adaptive planner parameter learning from demonstration X Xiao, B Liu, G Warnell, J Fink, P Stone IEEE Robotics and Automation Letters 5 (3), 4541-4547, 2020 | 87 | 2020 |
| Deterministic implementations for reproducibility in deep reinforcement learning P Nagarajan, G Warnell, P Stone arXiv preprint arXiv:1809.05676, 2018 | 70 | 2018 |
| VOILA: Visual-observation-only imitation learning for autonomous navigation H Karnan, G Warnell, X Xiao, P Stone International Conference on Robotics and Automation, 2022 | 63 | 2022 |
| APPL: Adaptive planner parameter learning X Xiao, Z Wang, Z Xu, B Liu, G Warnell, G Dhamankar, A Nair, P Stone Robotics and Autonomous Systems 154, 2022 | 62 | 2022 |
| Adaptive rate compressive sensing for background subtraction G Warnell, D Reddy, R Chellappa IEEE International Conference on Acoustics, Speech and Signal Processing …, 2012 | 62 | 2012 |
| Adaptive-rate compressive sensing using side information G Warnell, S Bhattacharya, R Chellappa, T Basar IEEE Transactions on Image Processing 24 (11), 3846-3857, 2015 | 60 | 2015 |
| APPLR: Adaptive planner parameter learning from reinforcement Z Xu, G Dhamankar, A Nair, X Xiao, G Warnell, B Liu, Z Wang, P Stone IEEE International Conference on Robotics and Automation, 2021 | 59 | 2021 |
| An imitation from observation approach to transfer learning with dynamics mismatch S Desai, I Durugkar, H Karnan, G Warnell, J Hanna, P Stone Neural Information Processing Systems, 2020 | 56 | 2020 |
| APPLI: Adaptive planner parameter learning from interventions Z Wang, X Xiao, B Liu, G Warnell, P Stone IEEE International Conference on Robotics and Automation, 2021 | 54 | 2021 |
| Toward agile maneuvers in highly constrained spaces: learning from hallucination X Xiao, B Liu, G Warnell, P Stone IEEE Robotics and Automation Letters 6 (2), 1503-1510, 2021 | 54 | 2021 |
| Parsimonious online learning with kernels via sparse projections in function space A Koppel, G Warnell, E Stump, A Ribeiro Journal of Machine Learning Research 20 (1), 83-126, 2019 | 53 | 2019 |
| Human gaze-driven spatial tasking of an autonomous MAV L Yuan, C Reardon, G Warnell, G Loianno IEEE Robotics and Automation Letters 4 (2), 1343-1350, 2019 | 52 | 2019 |
| Parsimonious online learning with kernels via sparse projections in function space A Koppel, G Warnell, E Stump, A Ribeiro IEEE International Conference on Acoustics, Speech and Signal Processing, 2017 | 51* | 2017 |
Peter StoneProfessor of Computer Science, The University of Texas at Austin在 cs.utexas.edu 的电子邮件经过验证
