Julian Ibarz
Julian Ibarz
Tesla Inc.
Verified email at - Homepage
Cited by
Cited by
In-datacenter performance analysis of a tensor processing unit
NP Jouppi, C Young, N Patil, D Patterson, G Agrawal, R Bajwa, S Bates, ...
Proceedings of the 44th annual international symposium on computer …, 2017
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
S Levine, P Pastor, A Krizhevsky, J Ibarz, D Quillen
The International journal of robotics research 37 (4-5), 421-436, 2018
Scalable deep reinforcement learning for vision-based robotic manipulation
D Kalashnikov, A Irpan, P Pastor, J Ibarz, A Herzog, E Jang, D Quillen, ...
Conference on robot learning, 651-673, 2018
Do as i can, not as i say: Grounding language in robotic affordances
A Brohan, Y Chebotar, C Finn, K Hausman, A Herzog, D Ho, J Ibarz, ...
Conference on robot learning, 287-318, 2023
Diversity is all you need: Learning skills without a reward function
B Eysenbach, A Gupta, J Ibarz, S Levine
arXiv preprint arXiv:1802.06070, 2018
Multi-digit number recognition from street view imagery using deep convolutional neural networks
IJ Goodfellow, Y Bulatov, J Ibarz, S Arnoud, V Shet
arXiv preprint arXiv:1312.6082, 2013
Using simulation and domain adaptation to improve efficiency of deep robotic grasping
K Bousmalis, A Irpan, P Wohlhart, Y Bai, M Kelcey, M Kalakrishnan, ...
2018 IEEE international conference on robotics and automation (ICRA), 4243-4250, 2018
How to train your robot with deep reinforcement learning: lessons we have learned
J Ibarz, J Tan, C Finn, M Kalakrishnan, P Pastor, S Levine
The International Journal of Robotics Research 40 (4-5), 698-721, 2021
Rt-1: Robotics transformer for real-world control at scale
A Brohan, N Brown, J Carbajal, Y Chebotar, J Dabis, C Finn, ...
arXiv preprint arXiv:2212.06817, 2022
Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
S James, P Wohlhart, M Kalakrishnan, D Kalashnikov, A Irpan, J Ibarz, ...
Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2019
Deep reinforcement learning for vision-based robotic grasping: A simulated comparative evaluation of off-policy methods
D Quillen, E Jang, O Nachum, C Finn, J Ibarz, S Levine
2018 IEEE international conference on robotics and automation (ICRA), 6284-6291, 2018
Recovery rl: Safe reinforcement learning with learned recovery zones
B Thananjeyan, A Balakrishna, S Nair, M Luo, K Srinivasan, M Hwang, ...
IEEE Robotics and Automation Letters 6 (3), 4915-4922, 2021
Attention-based extraction of structured information from street view imagery
Z Wojna, AN Gorban, DS Lee, K Murphy, Q Yu, Y Li, J Ibarz
2017 14th IAPR international conference on document analysis and recognition …, 2017
Rl-cyclegan: Reinforcement learning aware simulation-to-real
K Rao, C Harris, A Irpan, S Levine, J Ibarz, M Khansari
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020
Leave no trace: Learning to reset for safe and autonomous reinforcement learning
B Eysenbach, S Gu, J Ibarz, S Levine
arXiv preprint arXiv:1711.06782, 2017
Discrete sequential prediction of continuous actions for deep rl
L Metz, J Ibarz, N Jaitly, J Davidson
arXiv preprint arXiv:1705.05035, 2017
End-to-end learning of semantic grasping
E Jang, S Vijayanarasimhan, P Pastor, J Ibarz, S Levine
arXiv preprint arXiv:1707.01932, 2017
Off-policy evaluation via off-policy classification
A Irpan, K Rao, K Bousmalis, C Harris, J Ibarz, S Levine
Advances in Neural Information Processing Systems 32, 2019
End-to-end interpretation of the french street name signs dataset
R Smith, C Gu, DS Lee, H Hu, R Unnikrishnan, J Ibarz, S Arnoud, S Lin
Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8 …, 2016
Q-transformer: Scalable offline reinforcement learning via autoregressive q-functions
Y Chebotar, Q Vuong, K Hausman, F Xia, Y Lu, A Irpan, A Kumar, T Yu, ...
Conference on Robot Learning, 3909-3928, 2023
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