Zobrazeno 1 - 10
of 11
pro vyhledávání: '"Julian Ibarz"'
Autor:
Yao Lu, Dmitry Kalashnikov, Anelia Angelova, Julian Ibarz, Jacob Varley, Yevgen Chebotar, Michael S. Ryoo, Iretiayo Akinola
Publikováno v:
ICRA
We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action inputs and high dimensional visual inputs. Our approach automatically designs architectures while train
Deep reinforcement learning (RL) has emerged as a promising approach for autonomously acquiring complex behaviors from low-level sensor observations. Although a large portion of deep RL research has focused on applications in video games and simulate
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e47299376b1f1149d79eb0b685dc453d
Autor:
Michael Luo, Krishnan Srinivasan, Julian Ibarz, Chelsea Finn, Minho Hwang, Ashwin Balakrishna, Brijen Thananjeyan, Joseph E. Gonzalez, Ken Goldberg, Suraj Nair
Safety remains a central obstacle preventing widespread use of RL in the real world: learning new tasks in uncertain environments requires extensive exploration, but safety requires limiting exploration. We propose Recovery RL, an algorithm which nav
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::41b49e06b5e38d9264280ba70b6a7665
http://arxiv.org/abs/2010.15920
http://arxiv.org/abs/2010.15920
Publikováno v:
CVPR
Deep neural network based reinforcement learning (RL) can learn appropriate visual representations for complex tasks like vision-based robotic grasping without the need for manually engineering or prior learning a perception system. However, data for
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::855bf7a3858b320a1dba3f13afe83513
http://arxiv.org/abs/2006.09001
http://arxiv.org/abs/2006.09001
Autor:
Alek Jaworski, Suresh Bhatia, Kieran Miller, Rahul Nagarajan, Amir Salek, Gordon MacKean, Jeffrey Dean, Dan Steinberg, Sarah Bates, Matt Ross, Rick Boyle, Walter Wang, Mark Omernick, Albert T. Borchers, Narayana Penukonda, Ray Ni, Bo Tian, Diemthu Le, David A. Patterson, Aaron Jaffey, Ben Gelb, Andy Swing, Khaitan Harshit, Andrew Everett Phelps, Christopher Aaron Clark, Robert Hundt, Gregory Michael Thorson, Gregory Sizikov, Zhuyuan Liu, Michael J. Daley, Kathy Nix, Andy Koch, Horia Toma, Alexander Kaplan, C. Richard Ho, Steve Lacy, Maire Mahony, Nan Boden, Chris Severn, Rajendra Gottipati, Emad Samadiani, Adriana Maggiore, Norman P. Jouppi, Richard Walter, Mercedes Tan, Doe Hyun Yoon, Vijay K. Vasudevan, Jonathan Ross, Erick Tuttle, Doug Hogberg, Raminder Bajwa, Jed Souter, James Law, Robert Hagmann, William John Gulland, Ravi Narayanaswami, Jeremy Coriell, Naveen Kumar, Chris Leary, Tara Vazir Ghaemmaghami, Pierre-luc Cantin, Matt Dau, D. Hurt, Matthew Snelham, Julian Ibarz, Daniel Killebrew, John Hu, James Laudon, Cliff Young, Thomas Norrie, Kyle Lucke, Gaurav Agrawal, Clifford Chao, Nishant Patil, Alan Lundin, Eric Wilcox
Publikováno v:
ISCA
Many architects believe that major improvements in cost-energy-performance must now come from domain-specific hardware. This paper evaluates a custom ASIC---called a Tensor Processing Unit (TPU) --- deployed in datacenters since 2015 that accelerates
Publikováno v:
The International Journal of Robotics Research. 37:421-436
We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural network to predict the probability that task-space motion
Autor:
Derrall Heath, Daniel Kifer, Julian Ibarz, Alexander Gorban, Dafang He, Yeqing Li, C. Lee Giles, Qian Yu
Publikováno v:
Computer Vision – ACCV 2018 ISBN: 9783030208721
ACCV (5)
ACCV (5)
Many tasks are related to determining if a particular text string exists in an image. In this work, we propose a new framework that learns this task in an end-to-end way. The framework takes an image and a text string as input and then outputs the pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d063d514439ac8e2e7dd1a8d70198fa1
https://doi.org/10.1007/978-3-030-20873-8_17
https://doi.org/10.1007/978-3-030-20873-8_17
Publikováno v:
ICRA
In this paper, we explore deep reinforcement learning algorithms for vision-based robotic grasping. Model-free deep reinforcement learning (RL) has been successfully applied to a range of challenging environments, but the proliferation of algorithms
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e8aea14c3831f9bd8c4c5bfccce5662c
Autor:
Dar-Shyang Lee, Julian Ibarz, Qian Yu, Alexander Gorban, Yeqing Li, Kevin Murphy, Zbigniew Wojna
Publikováno v:
ICDAR
We present a neural network model - based on CNNs, RNNs and a novel attention mechanism - which achieves 84.2% accuracy on the challenging French Street Name Signs (FSNS) dataset, significantly outperforming the previous state of the art (Smith'16),
Autor:
Alex Irpan, Sergey Levine, Vincent Vanhoucke, Konstantinos Bousmalis, Paul Wohlhart, Peter Pastor, Laura Downs, Julian Ibarz, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Kurt Konolige
Publikováno v:
ICRA
Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive. An appealing alternative is to use off-the-shelf simulators to render synthetic data for which
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6c7f9eb38a322db476b954fd724bbbc1