| Spatially-aware graph neural networks for relational behavior forecasting from sensor data S Casas, C Gulino, R Liao, R Urtasun arXiv preprint arXiv:1910.08233, 2019 | 43* | 2019 |
| Implicit latent variable model for scene-consistent motion forecasting S Casas, C Gulino, S Suo, K Luo, R Liao, R Urtasun Proceedings of the European Conference on Computer Vision (ECCV), 2020 | 17 | 2020 |
| Generalizing informed sampling for asymptotically-optimal sampling-based kinodynamic planning via markov chain monte carlo D Yi, R Thakker, C Gulino, O Salzman, S Srinivasa 2018 IEEE International Conference on Robotics and Automation (ICRA), 7063-7070, 2018 | 11 | 2018 |
| The importance of prior knowledge in precise multimodal prediction S Casas, C Gulino, S Suo, R Urtasun arXiv preprint arXiv:2006.02636, 2020 | 9 | 2020 |
| Imitative planning using conditional normalizing flow S Agarwal, H Sikchi, C Gulino, E Wilkinson arXiv preprint arXiv:2007.16162, 2020 | 3 | 2020 |
| Association and Tracking for Autonomous Devices S Gautam, BC Becker, C Vallespi-Gonzalez, CC Gulino US Patent App. 16/562,938, 2021 | | 2021 |
| Systems and Methods for Generating Motion Forecast Data for Actors with Respect to an Autonomous Vehicle and Training a Machine Learned Model for the Same R Urtasun, R Liao, S Casas, CC Gulino US Patent App. 16/816,671, 2021 | | 2021 |
| Providing Actionable Uncertainties in Autonomous Vehicles CC Gulino, AR Ansari US Patent App. 16/043,759, 2020 | | 2020 |
| SingleQuery Planners for Nonholonomic Systems C Gulino, R Thakker, F Naina, BS Hanzra | | |
| Robot Autonomy N Baheti, J Gandhi, C Gulino, K Ritter | | |
Raquel UrtasunProfessor, University of Toronto. Chief Scientist, Uber ATG在 cs.toronto.edu 的电子邮件经过验证
