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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:

Physical Human Interactive Guidance: Identifying Grasping Principles From Human-Planned Grasps
Ravi Balasubramanian, Ling Xu, Peter Brook, et al.
IEEE Transactions on Robotics (2012) Vol. 28, Iss. 4, pp. 899-910
Open Access | Times Cited: 116

Showing 1-25 of 116 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: 1056
Data-Driven Grasp Synthesis—A Survey
Jeannette Bohg, Antonio Morales, Tamim Asfour, et al.
IEEE Transactions on Robotics (2014) Vol. 30, Iss. 2, pp. 289-309
Open Access | Times Cited: 590
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
Grasp quality measures: review and performance
Máximo A. Roa, Raúl Suárez
Autonomous Robots (2014) Vol. 38, Iss. 1, pp. 65-88
Open Access | Times Cited: 407
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
Leveraging big data for grasp planning
Daniel Kappler, Jeannette Bohg, Stefan Schaal
(2015), pp. 4304-4311
Closed Access | Times Cited: 274
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.
arXiv (Cornell University) (2017)
Open Access | Times Cited: 264
Exploitation of environmental constraints in human and robotic grasping
Clemens Eppner, Raphael Deimel, José Álvarez-Ruiz, et al.
The International Journal of Robotics Research (2015) Vol. 34, Iss. 7, pp. 1021-1038
Closed Access | Times Cited: 183
ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imaging
Samarth Brahmbhatt, Cusuh Ham, Charles C. Kemp, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), pp. 8701-8711
Open Access | Times Cited: 153
GanHand: Predicting Human Grasp Affordances in Multi-Object Scenes
Enric Corona, Albert Pumarola, Guillem Alenyà, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), pp. 5030-5040
Open Access | Times Cited: 139
Hybrid Robotic Grasping With a Soft Multimodal Gripper and a Deep Multistage Learning Scheme
Fukang Liu, Fuchun Sun, Bin Fang, et al.
IEEE Transactions on Robotics (2023) Vol. 39, Iss. 3, pp. 2379-2399
Open Access | Times Cited: 43
Analysis of Human Grasping Behavior: Object Characteristics and Grasp Type
Thomas Feix, Ian Bullock, Aaron M. Dollar
IEEE Transactions on Haptics (2014) Vol. 7, Iss. 3, pp. 311-323
Closed Access | Times Cited: 171
Robot grasp detection using multimodal deep convolutional neural networks
Zhichao Wang, Zhiqi Li, Bin Wang, et al.
Advances in Mechanical Engineering (2016) Vol. 8, Iss. 9
Open Access | Times Cited: 144
Design and Implementation of an Anthropomorphic Hand for Replicating Human Grasping Functions
Caihua Xiong, Wenrui Chen, Bai-Yang Sun, et al.
IEEE Transactions on Robotics (2016) Vol. 32, Iss. 3, pp. 652-671
Closed Access | Times Cited: 130
Learning of grasp selection based on shape-templates
Alexander Herzog, Peter Pástor, Mrinal Kalakrishnan, et al.
Autonomous Robots (2013) Vol. 36, Iss. 1-2, pp. 51-65
Closed Access | Times Cited: 116
Finger design automation for industrial robot grippers: A review
Mohammadali Honarpardaz, Mehdi Tarkian, Johan Ölvander, et al.
Robotics and Autonomous Systems (2016) Vol. 87, pp. 104-119
Open Access | Times Cited: 87
Review of Deep Reinforcement Learning-Based Object Grasping: Techniques, Open Challenges, and Recommendations
Marwan Qaid Mohammed, Lee Chung Kwek, Shing Chyi Chua
IEEE Access (2020) Vol. 8, pp. 178450-178481
Open Access | Times Cited: 81
Active Reward Learning
Christian Daniel, Malte Viering, Jan Metz, et al.
(2014)
Open Access | Times Cited: 93
An autonomous manipulation system based on force control and optimization
Ludovic Righetti, Mrinal Kalakrishnan, Peter Pástor, et al.
Autonomous Robots (2013) Vol. 36, Iss. 1-2, pp. 11-30
Closed Access | Times Cited: 82
The Next Step in Robot Commissioning: Autonomous Picking and Palletizing
Robert Krug, Todor Stoyanov, Vinicio Tincani, et al.
IEEE Robotics and Automation Letters (2016) Vol. 1, Iss. 1, pp. 546-553
Open Access | Times Cited: 77
Supervision via competition: Robot adversaries for learning tasks
Lerrel Pinto, James Davidson, Abhinav Gupta
(2017), pp. 1601-1608
Open Access | Times Cited: 72
Deep Robotic Grasping Prediction with Hierarchical RGB-D Fusion
Yaoxian Song, Jun Wen, Dongfang Liu, et al.
International Journal of Control Automation and Systems (2022) Vol. 20, Iss. 1, pp. 243-254
Closed Access | Times Cited: 28
Part-based grasp planning for familiar objects
Nikolaus Vahrenkamp, Leonard Westkamp, Natsuki Yamanobe, et al.
(2016), pp. 919-925
Closed Access | Times Cited: 54
A review of robotic grasp detection technology
Minglun Dong, Jian Zhang
Robotica (2023) Vol. 41, Iss. 12, pp. 3846-3885
Closed Access | Times Cited: 15
Three-dimensional object reconstruction of symmetric objects by fusing visual and tactile sensing
Jarmo Ilonen, Jeannette Bohg, Ville Kyrki
The International Journal of Robotics Research (2013) Vol. 33, Iss. 2, pp. 321-341
Closed Access | Times Cited: 51
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