| Qmdp-net: Deep learning for planning under partial observability P Karkus, D Hsu, WS Lee Advances in Neural Information Processing Systems, 4694-4704, 2017 | 202 | 2017 |
| Particle Filter Networks with Application to Visual Localization P Karkus, D Hsu, WS Lee 2nd Conference on Robot Learning, 169-178, 2018 | 137 | 2018 |
| Particle Filter Recurrent Neural Networks X Ma, P Karkus, D Hsu, WS Lee arXiv preprint arXiv:1905.12885, 2019 | 106 | 2019 |
| Differentiable algorithm networks for composable robot learning P Karkus, X Ma, D Hsu, LP Kaelbling, WS Lee, T Lozano-Pérez Robotics: Science and Systems, 2019 | 77 | 2019 |
| Differentiable slam-net: Learning particle slam for visual navigation P Karkus, S Cai, D Hsu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 76 | 2021 |
| On-chip generation and demultiplexing of quantum correlated photons using a silicon-silica monolithic photonic integration platform N Matsuda, P Karkus, H Nishi, T Tsuchizawa, WJ Munro, H Takesue, ... Optics express 22 (19), 22831-22840, 2014 | 45 | 2014 |
| Diffstack: A differentiable and modular control stack for autonomous vehicles P Karkus, B Ivanovic, S Mannor, M Pavone Conference on robot learning, 2170-2180, 2023 | 44 | 2023 |
| Discriminative particle filter reinforcement learning for complex partial observations X Ma, P Karkus, D Hsu, WS Lee, N Ye arXiv preprint arXiv:2002.09884, 2020 | 41 | 2020 |
| Tree-structured policy planning with learned behavior models Y Chen, P Karkus, B Ivanovic, X Weng, M Pavone 2023 IEEE International Conference on Robotics and Automation (ICRA), 7902-7908, 2023 | 28 | 2023 |
| Interactive joint planning for autonomous vehicles Y Chen, S Veer, P Karkus, M Pavone IEEE Robotics and Automation Letters, 2023 | 23 | 2023 |
| Dtpp: Differentiable joint conditional prediction and cost evaluation for tree policy planning in autonomous driving Z Huang, P Karkus, B Ivanovic, Y Chen, M Pavone, C Lv 2024 IEEE International Conference on Robotics and Automation (ICRA), 6806-6812, 2024 | 17 | 2024 |
| Generation of entangled photons using an arrayed waveguide grating N Matsuda, H Nishi, P Karkus, T Tsuchizawa, K Yamada, WJ Munro, ... Journal of Optics 19 (12), 124005, 2017 | 15 | 2017 |
| Physically embedded planning problems: New challenges for reinforcement learning M Mirza, A Jaegle, JJ Hunt, A Guez, S Tunyasuvunakool, A Muldal, ... arXiv preprint arXiv:2009.05524, 2020 | 13 | 2020 |
| Differentiable mapping networks: Learning structured map representations for sparse visual localization P Karkus, A Angelova, V Vanhoucke, R Jonschkowski 2020 IEEE International Conference on Robotics and Automation (ICRA), 4753-4759, 2020 | 13 | 2020 |
| Receding horizon planning with rule hierarchies for autonomous vehicles S Veer, K Leung, RK Cosner, Y Chen, P Karkus, M Pavone 2023 IEEE International Conference on Robotics and Automation (ICRA), 1507-1513, 2023 | 11 | 2023 |
| Planning with occluded traffic agents using bi-level variational occlusion models F Christianos, P Karkus, B Ivanovic, SV Albrecht, M Pavone 2023 IEEE International Conference on Robotics and Automation (ICRA), 5558-5565, 2023 | 11 | 2023 |
| Beyond tabula-rasa: a modular reinforcement learning approach for physically embedded 3d sokoban P Karkus, M Mirza, A Guez, A Jaegle, T Lillicrap, L Buesing, N Heess, ... arXiv preprint arXiv:2010.01298, 2020 | 10 | 2020 |
| Integrating algorithmic planning and deep learning for partially observable navigation P Karkus, D Hsu, WS Lee arXiv preprint arXiv:1807.06696, 2018 | 10 | 2018 |
| Factored contextual policy search with Bayesian optimization P Karkus, A Kupcsik, D Hsu, WS Lee arXiv preprint arXiv:1612.01746, 2016 | 10* | 2016 |
| Foundation models for semantic novelty in reinforcement learning T Gupta, P Karkus, T Che, D Xu, M Pavone arXiv preprint arXiv:2211.04878, 2022 | 7 | 2022 |
David HsuProfessor of Computer Science, National University of Singaporecomp.nus.edu.sg의 이메일 확인됨
