| Ask me anything: Dynamic memory networks for natural language processing A Kumar, O Irsoy, P Ondruska, M Iyyer, J Bradbury, I Gulrajani, V Zhong, ... International conference on machine learning, 1378-1387, 2016 | 1418 | 2016 |
| Maximum entropy deep inverse reinforcement learning M Wulfmeier, P Ondruska, I Posner arXiv preprint arXiv:1507.04888, 2015 | 360 | 2015 |
| Deep tracking: Seeing beyond seeing using recurrent neural networks P Ondruska, I Posner Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016 | 259 | 2016 |
| One thousand and one hours: Self-driving motion prediction dataset J Houston, G Zuidhof, L Bergamini, Y Ye, L Chen, A Jain, S Omari, ... Conference on Robot Learning, 409-418, 2021 | 221 | 2021 |
| Mobilefusion: Real-time volumetric surface reconstruction and dense tracking on mobile phones P Ondrúška, P Kohli, S Izadi IEEE transactions on visualization and computer graphics 21 (11), 1251-1258, 2015 | 146 | 2015 |
| Large-scale cost function learning for path planning using deep inverse reinforcement learning M Wulfmeier, D Rao, DZ Wang, P Ondruska, I Posner The International Journal of Robotics Research 36 (10), 1073-1087, 2017 | 145 | 2017 |
| Lyft level 5 av dataset 2019 R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ... urlhttps://level5. lyft. com/dataset 1, 3, 2019 | 108 | 2019 |
| Deep tracking in the wild: End-to-end tracking using recurrent neural networks J Dequaire, P Ondrúška, D Rao, D Wang, I Posner The International Journal of Robotics Research 37 (4-5), 492-512, 2018 | 105 | 2018 |
| End-to-end tracking and semantic segmentation using recurrent neural networks P Ondruska, J Dequaire, DZ Wang, I Posner arXiv preprint arXiv:1604.05091, 2016 | 82 | 2016 |
| Deep inverse reinforcement learning M Wulfmeier, P Ondruska, I Posner CoRR, abs/1507.04888, 2015 | 72 | 2015 |
| Lyft level 5 perception dataset 2020 R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ... | 52 | 2019 |
| Level 5 perception dataset 2020 R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ... | 50 | 2019 |
| Probabilistic attainability maps: Efficiently predicting driver-specific electric vehicle range P Ondruska, I Posner 2014 IEEE Intelligent Vehicles Symposium Proceedings, 1169-1174, 2014 | 43 | 2014 |
| Simnet: Learning reactive self-driving simulations from real-world observations L Bergamini, Y Ye, O Scheel, L Chen, C Hu, L Del Pero, B Osiński, ... 2021 IEEE International Conference on Robotics and Automation (ICRA), 5119-5125, 2021 | 41 | 2021 |
| Lyft level 5 av dataset 2019. urlhttps R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ... level5. lyft. com/dataset 2 (3), 6, 2019 | 40 | 2019 |
| Scheduled perception for energy-efficient path following P Ondrúška, C Gurău, L Marchegiani, CH Tong, I Posner 2015 IEEE International Conference on Robotics and Automation (ICRA), 4799-4806, 2015 | 37 | 2015 |
| The route not taken: Driver-centric estimation of electric vehicle range P Ondruska, I Posner Proceedings of the International Conference on Automated Planning and …, 2014 | 33 | 2014 |
| Deep tracking on the move: Learning to track the world from a moving vehicle using recurrent neural networks J Dequaire, D Rao, P Ondruska, D Wang, I Posner arXiv preprint arXiv:1609.09365, 2016 | 28 | 2016 |
| Urban driver: Learning to drive from real-world demonstrations using policy gradients O Scheel, L Bergamini, M Wolczyk, B Osiński, P Ondruska Conference on Robot Learning, 718-728, 2022 | 26 | 2022 |
| Safetynet: Safe planning for real-world self-driving vehicles using machine-learned policies M Vitelli, Y Chang, Y Ye, A Ferreira, M Wołczyk, B Osiński, M Niendorf, ... 2022 International Conference on Robotics and Automation (ICRA), 897-904, 2022 | 21 | 2022 |
Ingmar PosnerOxford UniversityVerified email at robots.ox.ac.uk
