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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Deep learning for detecting robotic grasps
Ian Lenz, Honglak Lee, Ashutosh Saxena
The International Journal of Robotics Research (2015) Vol. 34, Iss. 4-5, pp. 705-724
Open Access | Times Cited: 1513

Showing 1-25 of 1513 citing articles:

VoxNet: A 3D Convolutional Neural Network for real-time object recognition
Daniel Maturana, Sebastian Scherer
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2015), pp. 922-928
Closed Access | Times Cited: 3256
Unsupervised Monocular Depth Estimation with Left-Right Consistency
Clément Godard, Oisin Mac Aodha, Gabriel Brostow
(2017)
Open Access | Times Cited: 3014
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, et al.
Journal of Machine Learning Research (2016) Vol. 17, Iss. 1, pp. 1334-1373
Closed Access | Times Cited: 1888
Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
Sergey Levine, Peter Pástor, Alex Krizhevsky, et al.
The International Journal of Robotics Research (2017) Vol. 37, Iss. 4-5, pp. 421-436
Open Access | Times Cited: 1796
Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification
Srdjan Sladojević, Marko Arsenović, Andraš Anderla, et al.
Computational Intelligence and Neuroscience (2016) Vol. 2016, pp. 1-11
Open Access | Times Cited: 1540
Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
Lerrel Pinto, Abhinav Gupta
(2016), pp. 3406-3413
Open Access | Times Cited: 1098
Dex-Net 2.0: Deep Learning to Plan Robust Grasps with Synthetic Point Clouds and Analytic Grasp Metrics
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, et al.
(2017)
Open Access | Times Cited: 1061
DeepFruits: A Fruit Detection System Using Deep Neural Networks
Inkyu Sa, Zongyuan Ge, Feras Dayoub, et al.
Sensors (2016) Vol. 16, Iss. 8, pp. 1222-1222
Open Access | Times Cited: 945
Real-time grasp detection using convolutional neural networks
Joseph Redmon, Anelia Angelova
(2015)
Open Access | Times Cited: 830
Deep Multimodal Learning: A Survey on Recent Advances and Trends
Dhanesh Ramachandram, Graham W. Taylor
IEEE Signal Processing Magazine (2017) Vol. 34, Iss. 6, pp. 96-108
Closed Access | Times Cited: 788
A Survey of Research on Cloud Robotics and Automation
Ben Kehoe, Sachin Patil, Pieter Abbeel, et al.
IEEE Transactions on Automation Science and Engineering (2015) Vol. 12, Iss. 2, pp. 398-409
Open Access | Times Cited: 784
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, et al.
(2018)
Open Access | Times Cited: 626
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pástor, et al.
arXiv (Cornell University) (2018)
Open Access | Times Cited: 578
Identification of Maize Leaf Diseases Using Improved Deep Convolutional Neural Networks
Xihai Zhang, Yue Qiao, Fanfeng Meng, et al.
IEEE Access (2018) Vol. 6, pp. 30370-30377
Open Access | Times Cited: 559
Multimodal deep learning for robust RGB-D object recognition
Andreas Eitel, Jost Tobias Springenberg, Luciano Spinello, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2015)
Open Access | Times Cited: 520
Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach
Douglas Morrison, Jürgen Leitner, Peter Corke
(2018)
Open Access | Times Cited: 490
Learning ambidextrous robot grasping policies
Jeffrey Mahler, Matthew Matl, Vishal Satish, et al.
Science Robotics (2019) Vol. 4, Iss. 26
Closed Access | Times Cited: 487
Robotic grasp detection using deep convolutional neural networks
Sulabh Kumra, Christopher Kanan
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017), pp. 769-776
Open Access | Times Cited: 483
Grasp Pose Detection in Point Clouds
Andreas ten Pas, Marcus Gualtieri, Kate Saenko, et al.
The International Journal of Robotics Research (2017) Vol. 36, Iss. 13-14, pp. 1455-1473
Open Access | Times Cited: 465
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
Arsalan Mousavian, Clemens Eppner, Dieter Fox
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2019), pp. 2901-2910
Open Access | Times Cited: 443
Learning Joint Reconstruction of Hands and Manipulated Objects
Yana Hasson, Gül Varol, Dimitrios Tzionas, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), pp. 11799-11808
Open Access | Times Cited: 440
Real-World Multiobject, Multigrasp Detection
Fu-Jen Chu, Ruinian Xu, Patricio A. Vela
IEEE Robotics and Automation Letters (2018) Vol. 3, Iss. 4, pp. 3355-3362
Open Access | Times Cited: 372
Faster R-CNN for multi-class fruit detection using a robotic vision system
Shaohua Wan, Sotirios K. Goudos
Computer Networks (2019) Vol. 168, pp. 107036-107036
Closed Access | Times Cited: 370
A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification
Ce Zhang, Xin Pan, Huapeng Li, et al.
ISPRS Journal of Photogrammetry and Remote Sensing (2017) Vol. 140, pp. 133-144
Open Access | Times Cited: 367
Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), pp. 12619-12629
Closed Access | Times Cited: 366
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