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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: 1515

Showing 26-50 of 1515 citing articles:

Dex-Net 1.0: A cloud-based network of 3D objects for robust grasp planning using a Multi-Armed Bandit model with correlated rewards
Jeffrey Mahler, Florian T. Pokorny, Brian Hou, et al.
(2016), pp. 1957-1964
Closed Access | Times Cited: 354
Vision-based robotic grasping from object localization, object pose estimation to grasp estimation for parallel grippers: a review
Guoguang Du, Kai Wang, Shiguo Lian, et al.
Artificial Intelligence Review (2020) Vol. 54, Iss. 3, pp. 1677-1734
Open Access | Times Cited: 354
Learning robust, real-time, reactive robotic grasping
Douglas Morrison, Peter Corke, Jürgen Leitner
The International Journal of Robotics Research (2019) Vol. 39, Iss. 2-3, pp. 183-201
Open Access | Times Cited: 335
GraspNet-1Billion: A Large-Scale Benchmark for General Object Grasping
Hao-Shu Fang, Chenxi Wang, Minghao Gou, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), pp. 11441-11450
Closed Access | Times Cited: 332
PointNetGPD: Detecting Grasp Configurations from Point Sets
Hongzhuo Liang, Xiaojian Ma, Shuang Li, et al.
2022 International Conference on Robotics and Automation (ICRA) (2019)
Open Access | Times Cited: 292
High precision grasp pose detection in dense clutter
Marcus Gualtieri, Andreas ten Pas, Kate Saenko, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2016), pp. 598-605
Open Access | Times Cited: 284
Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning
Jeffrey Mahler, Matthew Matl, Xinyu Liu, et al.
(2018), pp. 5620-5627
Closed Access | Times Cited: 284
Learning Hand-Eye Coordination for Robotic Grasping with Large-Scale Data Collection
Sergey Levine, Peter Pástor, Alex Krizhevsky, et al.
Springer proceedings in advanced robotics (2017), pp. 173-184
Closed Access | Times Cited: 278
Leveraging big data for grasp planning
Daniel Kappler, Jeannette Bohg, Stefan Schaal
(2015), pp. 4304-4311
Closed Access | Times Cited: 274
Antipodal Robotic Grasping using Generative Residual Convolutional Neural Network
Sulabh Kumra, Shirin Joshi, Ferat Sahin
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020)
Open Access | Times Cited: 270
Deep learning in robotics: a review of recent research
Harry A. Pierson, Michael S. Gashler
Advanced Robotics (2017) Vol. 31, Iss. 16, pp. 821-835
Open Access | Times Cited: 269
Jacquard: A Large Scale Dataset for Robotic Grasp Detection
Amaury Depierre, Emmanuel Dellandréa, Liming Chen
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2018), pp. 3511-3516
Open Access | Times Cited: 260
AffordanceNet: An End-to-End Deep Learning Approach for Object Affordance Detection
Thanh-Toan Do, Anh‐Tu Nguyen, Ian Reid
(2018), pp. 1-5
Open Access | Times Cited: 253
Affordance detection of tool parts from geometric features
Austin Myers, Ching L. Teo, Cornelia Fermüller, et al.
(2015), pp. 1374-1381
Closed Access | Times Cited: 251
Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning
Tianhe Yu, Deirdre Quillen, Zhanpeng He, et al.
arXiv (Cornell University) (2019)
Open Access | Times Cited: 247
A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues
Shahab S. Band, Mahdis Fathi, Abdollah Dehzangi, et al.
Journal of Biomedical Informatics (2020) Vol. 113, pp. 103627-103627
Closed Access | Times Cited: 244
Deep learning models for traffic flow prediction in autonomous vehicles: A review, solutions, and challenges
Arzoo Miglani, Neeraj Kumar
Vehicular Communications (2019) Vol. 20, pp. 100184-100184
Closed Access | Times Cited: 234
Deep learning a grasp function for grasping under gripper pose uncertainty
Edward Johns, Stefan Leutenegger, Andrew J. Davison
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2016), pp. 4461-4468
Open Access | Times Cited: 231
End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks
Sen Wang, Ronald Clark, Hongkai Wen, et al.
The International Journal of Robotics Research (2017) Vol. 37, Iss. 4-5, pp. 513-542
Closed Access | Times Cited: 223
Repeatable Folding Task by Humanoid Robot Worker Using Deep Learning
Pin-Chu Yang, Kazuma Sasaki, Kanata Suzuki, et al.
IEEE Robotics and Automation Letters (2016) Vol. 2, Iss. 2, pp. 397-403
Closed Access | Times Cited: 219
Improving object detection with deep convolutional networks via Bayesian optimization and structured prediction
Yuting Zhang, Kihyuk Sohn, Ruben Villegas, et al.
(2015), pp. 249-258
Open Access | Times Cited: 217
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation
Dmitry Kalashnikov, Alex Irpan, Peter Pástor, et al.
Conference on Robot Learning (2018), pp. 651-673
Closed Access | Times Cited: 216
3D Convolutional Neural Networks for landing zone detection from LiDAR
Daniel Maturana, Sebastian Scherer
(2015), pp. 3471-3478
Closed Access | Times Cited: 215
A Survey on Learning-Based Robotic Grasping
Kilian Kleeberger, Richard Bormann, Werner Kraus, et al.
Current Robotics Reports (2020) Vol. 1, Iss. 4, pp. 239-249
Open Access | Times Cited: 209
Deep Reinforcement Learning for Vision-Based Robotic Grasping: A Simulated Comparative Evaluation of Off-Policy Methods
Deirdre Quillen, Eric Jang, Ofir Nachum, et al.
(2018)
Open Access | Times Cited: 208

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