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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:

Generating multi-fingered robotic grasps via deep learning
Jacob Varley, Jonathan Weisz, J Weiss, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2015), pp. 4415-4420
Closed Access | Times Cited: 115

Showing 1-25 of 115 citing articles:

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
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems
Zubair Md. Fadlullah, Fengxiao Tang, Bomin Mao, et al.
IEEE Communications Surveys & Tutorials (2017) Vol. 19, Iss. 4, pp. 2432-2455
Closed Access | Times Cited: 759
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
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
Shape completion enabled robotic grasping
Jacob Varley, Chad DeChant, Adam Richardson, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017), pp. 2442-2447
Open Access | Times Cited: 283
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
Learning for a Robot: Deep Reinforcement Learning, Imitation Learning, Transfer Learning
Hua Jiang, Liangcai Zeng, Gongfa Li, et al.
Sensors (2021) Vol. 21, Iss. 4, pp. 1278-1278
Open Access | Times Cited: 159
Deep Learning Approaches to Grasp Synthesis: A Review
R. Newbury, Morris Gu, Lachlan Chumbley, et al.
IEEE Transactions on Robotics (2023) Vol. 39, Iss. 5, pp. 3994-4015
Open Access | Times Cited: 92
Learning Object Grasping for Soft Robot Hands
Changhyun Choi, Wilko Schwarting, Joseph DelPreto, et al.
IEEE Robotics and Automation Letters (2018) Vol. 3, Iss. 3, pp. 2370-2377
Open Access | Times Cited: 158
Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach
Douglas Morrison, Peter Corke, Jürgen Leitner
arXiv (Cornell University) (2018)
Closed Access | Times Cited: 100
ContactGrasp: Functional Multi-finger Grasp Synthesis from Contact
Samarth Brahmbhatt, Ankur Handa, James Hays, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019), pp. 2386-2393
Open Access | Times Cited: 96
Grasping of Unknown Objects Using Deep Convolutional Neural Networks Based on Depth Images
Philipp Schmidt, Nikolaus Vahrenkamp, Mirko Wächter, et al.
(2018), pp. 6831-6838
Closed Access | Times Cited: 85
DexGraspNet: A Large-Scale Robotic Dexterous Grasp Dataset for General Objects Based on Simulation
Ruicheng Wang, Jialiang Zhang, Jiayi Chen, et al.
(2023), pp. 11359-11366
Open Access | Times Cited: 31
Learning Continuous Grasping Function With a Dexterous Hand From Human Demonstrations
Jianglong Ye, Jiashun Wang, Binghao Huang, et al.
IEEE Robotics and Automation Letters (2023) Vol. 8, Iss. 5, pp. 2882-2889
Open Access | Times Cited: 22
Planning Multi-fingered Grasps as Probabilistic Inference in a Learned Deep Network
Qingkai Lu, Kautilya Chenna, Balakumar Sundaralingam, et al.
Springer proceedings in advanced robotics (2019), pp. 455-472
Open Access | Times Cited: 65
Learning Continuous 3D Reconstructions for Geometrically Aware Grasping
Mark Van der Merwe, Qingkai Lu, Balakumar Sundaralingam, et al.
(2020), pp. 11516-11522
Open Access | Times Cited: 63
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
Chenxi Wang, Hao-Shu Fang, Minghao Gou, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2021), pp. 15944-15953
Open Access | Times Cited: 54
Multifingered Grasp Planning via Inference in Deep Neural Networks: Outperforming Sampling by Learning Differentiable Models
Qingkai Lu, Mark Van der Merwe, Balakumar Sundaralingam, et al.
IEEE Robotics & Automation Magazine (2020) Vol. 27, Iss. 2, pp. 55-65
Open Access | Times Cited: 51
Robotics Dexterous Grasping: The Methods Based on Point Cloud and Deep Learning
Haonan Duan, Peng Wang, Ya-Yu Huang, et al.
Frontiers in Neurorobotics (2021) Vol. 15
Open Access | Times Cited: 43
Autonomous Robotic Manipulation: Real-Time, Deep-Learning Approach for Grasping of Unknown Objects
Malak Sayour, Sharbel Kozhaya, Samer S. Saab
Journal of Robotics (2022) Vol. 2022, pp. 1-14
Open Access | Times Cited: 32
DVGG: Deep Variational Grasp Generation for Dextrous Manipulation
Wei Wei, Daheng Li, Peng Wang, et al.
IEEE Robotics and Automation Letters (2022) Vol. 7, Iss. 2, pp. 1659-1666
Open Access | Times Cited: 29
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose
Juntao Jian, Xiuping Liu, Manyi Li, et al.
2021 IEEE/CVF International Conference on Computer Vision (ICCV) (2023)
Open Access | Times Cited: 18
Generative Attention Learning: a “GenerAL” framework for high-performance multi-fingered grasping in clutter
Bohan Wu, Iretiayo Akinola, Abhi Gupta, et al.
Autonomous Robots (2020) Vol. 44, Iss. 6, pp. 971-990
Closed Access | Times Cited: 43
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