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Peter Ondrúška
Peter Ondrúška
Head of Research, Toyota Woven Planet
Verified email at ondruska.com - Homepage
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Cited by
Year
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
12622016
Maximum entropy deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
arXiv preprint arXiv:1507.04888, 2015
3012015
Deep tracking: Seeing beyond seeing using recurrent neural networks
P Ondruska, I Posner
Thirtieth AAAI conference on artificial intelligence, 2016
1682016
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
1382015
One thousand and one hours: Self-driving motion prediction dataset
J Houston, G Zuidhof, L Bergamini, Y Ye, L Chen, A Jain, S Omari, ...
arXiv preprint arXiv:2006.14480, 2020
1182020
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
1142017
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
932018
Deep tracking: Seeing beyond seeing using recurrent neural networks
P Ondrúška, I Posner
Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence …, 2016
842016
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
832019
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
682016
Deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
CoRR, abs/1507.04888, 2015
632015
Lyft level 5 perception dataset 2020
R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ...
452019
Probabilistic attainability maps: Efficiently predicting driver-specific electric vehicle range
P Ondruska, I Posner
2014 IEEE Intelligent Vehicles Symposium Proceedings, 1169-1174, 2014
402014
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
362019
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
352015
The route not taken: Driver-centric estimation of electric vehicle range
P Ondruska, I Posner
Twenty-Fourth International Conference on Automated Planning and Scheduling, 2014
332014
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
252016
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
152021
Collaborative augmented reality on smartphones via life-long city-scale maps
L Platinsky, M Szabados, F Hlasek, R Hemsley, L Del Pero, A Pancik, ...
2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR …, 2020
82020
Autonomy 2.0: Why is self-driving always 5 years away?
A Jain, L Del Pero, H Grimmett, P Ondruska
arXiv preprint arXiv:2107.08142, 2021
72021
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