Peter Karkus
Peter Karkus
Research Scientist, NVIDIA Research
Verified email at - Homepage
Cited by
Cited by
Qmdp-net: Deep learning for planning under partial observability
P Karkus, D Hsu, WS Lee
Advances in Neural Information Processing Systems, 4694-4704, 2017
Particle Filter Recurrent Neural Networks
X Ma, P Karkus, D Hsu, WS Lee
arXiv preprint arXiv:1905.12885, 2019
Particle Filter Networks with Application to Visual Localization
P Karkus, D Hsu, WS Lee
2nd Conference on Robot Learning, 169-178, 2018
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
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
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
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
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
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
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
Integrating algorithmic planning and deep learning for partially observable navigation
P Karkus, D Hsu, WS Lee
arXiv preprint arXiv:1807.06696, 2018
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
Factored contextual policy search with Bayesian optimization
P Karkus, A Kupcsik, D Hsu, WS Lee
arXiv preprint arXiv:1612.01746, 2016
Tree-structured Policy Planning with Learned Behavior Models
Y Chen, P Karkus, B Ivanovic, X Weng, M Pavone
arXiv preprint arXiv:2301.11902, 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
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
Particle filter networks: End-toend probabilistic localization from visual observations
P Karkus, D Hsu, WS Lee
arXiv preprint arXiv:1805.08975, 2018
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
PF-LSTM: Belief state particle filter for LSTM
X Ma, P Karkus, D Hsu, WS Lee
Proc. NeurIPS RLPO Workshop, 2018
Foundation Models for Semantic Novelty in Reinforcement Learning
T Gupta, P Karkus, T Che, D Xu, M Pavone
arXiv preprint arXiv:2211.04878, 2022
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