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Peter Ondr˙╣ka
Peter Ondr˙╣ka
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
15222016
Maximum entropy deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
arXiv preprint arXiv:1507.04888, 2015
4322015
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
3532021
Deep tracking: Seeing beyond seeing using recurrent neural networks
P Ondruska, I Posner
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
2782016
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
1812017
Mobilefusion: Real-time volumetric surface reconstruction and dense tracking on mobile phones
P Ondr˙╣ka, P Kohli, S Izadi
IEEE transactions on visualization and computer graphics 21 (11), 1251-1258, 2015
1642015
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
1292019
Deep tracking in the wild: End-to-end tracking using recurrent neural networks
J Dequaire, P Ondr˙╣ka, D Rao, D Wang, I Posner
The International Journal of Robotics Research 37 (4-5), 492-512, 2018
1192018
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
852016
Deep inverse reinforcement learning
M Wulfmeier, P Ondruska, I Posner
CoRR, abs/1507.04888, 2015
782015
Simnet: Learning reactive self-driving simulations from real-world observations
L Bergamini, Y Ye, O Scheel, L Chen, C Hu, L Del Pero, B Osi˝ski, ...
2021 IEEE International Conference on Robotics and Automation (ICRA), 5119-5125, 2021
702021
Level 5 perception dataset 2020
R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ...
Woven Planet Holdings, Tokyo, Japan, 2019
692019
Urban driver: Learning to drive from real-world demonstrations using policy gradients
O Scheel, L Bergamini, M Wolczyk, B Osi˝ski, P Ondruska
Conference on Robot Learning, 718-728, 2022
632022
Lyft level 5 perception dataset 2020
R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ...
542019
Probabilistic attainability maps: Efficiently predicting driver-specific electric vehicle range
P Ondruska, I Posner
2014 IEEE Intelligent Vehicles Symposium Proceedings, 1169-1174, 2014
482014
Safetynet: Safe planning for real-world self-driving vehicles using machine-learned policies
M Vitelli, Y Chang, Y Ye, A Ferreira, M Wo│czyk, B Osi˝ski, M Niendorf, ...
2022 International Conference on Robotics and Automation (ICRA), 897-904, 2022
462022
Lyft level 5 av dataset 2019. urlhttps
R Kesten, M Usman, J Houston, T Pandya, K Nadhamuni, A Ferreira, ...
level5. lyft. com/dataset 1 (2), 3, 2019
462019
Scheduled perception for energy-efficient path following
P Ondr˙╣ka, C GurŃu, L Marchegiani, CH Tong, I Posner
2015 IEEE International Conference on Robotics and Automation (ICRA), 4799-4806, 2015
402015
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
382021
The route not taken: Driver-centric estimation of electric vehicle range
P Ondruska, I Posner
Proceedings of the International Conference on Automated Planning andá…, 2014
352014
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