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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 tactile understanding from visual and haptic data
Yang Gao, Lisa Anne Hendricks, Katherine J. Kuchenbecker, et al.
(2016)
Open Access | Times Cited: 249

Showing 1-25 of 249 citing articles:

A deep learning framework for neuroscience
Blake A. Richards, Timothy Lillicrap, Philippe Beaudoin, et al.
Nature Neuroscience (2019) Vol. 22, Iss. 11, pp. 1761-1770
Open Access | Times Cited: 898
Learning the signatures of the human grasp using a scalable tactile glove
Subramanian Sundaram, Petr Kellnhofer, Yunzhu Li, et al.
Nature (2019) Vol. 569, Iss. 7758, pp. 698-702
Closed Access | Times Cited: 891
Robotic tactile perception of object properties: A review
Shan Luo, João Bimbo, Ravinder Dahiya, et al.
Mechatronics (2017) Vol. 48, pp. 54-67
Open Access | Times Cited: 348
Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks
Michelle A. Lee, Yuke Zhu, Krishnan Srinivasan, et al.
2022 International Conference on Robotics and Automation (ICRA) (2019), pp. 8943-8950
Open Access | Times Cited: 308
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: 268
A Review of Tactile Information: Perception and Action Through Touch
Qiang Li, Oliver Kroemer, Zhe Su, et al.
IEEE Transactions on Robotics (2020) Vol. 36, Iss. 6, pp. 1619-1634
Open Access | Times Cited: 204
Shape-independent hardness estimation using deep learning and a GelSight tactile sensor
Wenzhen Yuan, Chenzhuo Zhu, Andrew Owens, et al.
(2017), pp. 951-958
Open Access | Times Cited: 171
Making Sense of Vision and Touch: Learning Multimodal Representations for Contact-Rich Tasks
Michelle A. Lee, Yuke Zhu, Peter Zachares, et al.
IEEE Transactions on Robotics (2020) Vol. 36, Iss. 3, pp. 582-596
Open Access | Times Cited: 161
Toward Tactile Internet in Beyond 5G Era: Recent Advances, Current Issues, and Future Directions
Shree Krishna Sharma, Isaac Woungang, Alagan Anpalagan, et al.
IEEE Access (2020) Vol. 8, pp. 56948-56991
Open Access | Times Cited: 154
Multimodal Feature-Based Surface Material Classification
Matti Strese, Clemens Schuwerk, Albert Iepure, et al.
IEEE Transactions on Haptics (2016) Vol. 10, Iss. 2, pp. 226-239
Closed Access | Times Cited: 123
Haptic Codecs for the Tactile Internet
Eckehard Steinbach, Matti Strese, Mohamad Eid, et al.
Proceedings of the IEEE (2018) Vol. 107, Iss. 2, pp. 447-470
Open Access | Times Cited: 122
ViTac: Feature Sharing Between Vision and Tactile Sensing for Cloth Texture Recognition
Shan Luo, Wenzhen Yuan, Edward H. Adelson, et al.
(2018), pp. 2722-2727
Open Access | Times Cited: 118
A deep learning approach for hydrological time-series prediction: A case study of Gilgit river basin
Dostdar Hussain, Tahir Hussain, Aftab Ahmed Khan, et al.
Earth Science Informatics (2020) Vol. 13, Iss. 3, pp. 915-927
Closed Access | Times Cited: 116
Deep Learning for Surface Material Classification Using Haptic and Visual Information
Haitian Zheng, Lu Fang, Mengqi Ji, et al.
IEEE Transactions on Multimedia (2016) Vol. 18, Iss. 12, pp. 2407-2416
Open Access | Times Cited: 109
See, feel, act: Hierarchical learning for complex manipulation skills with multisensory fusion
Nima Fazeli, M. Oller, Jiajun Wu, et al.
Science Robotics (2019) Vol. 4, Iss. 26
Open Access | Times Cited: 107
Deep Learning in Robotics: Survey on Model Structures and Training Strategies
Artúr I. Károly, Péter Galambos, József Kuti, et al.
IEEE Transactions on Systems Man and Cybernetics Systems (2020) Vol. 51, Iss. 1, pp. 266-279
Open Access | Times Cited: 106
CNN-Based Methods for Object Recognition With High-Resolution Tactile Sensors
Juan M. Gandarías, Alfonso García-Cerezo, Jesús M. Gómez-de-Gabriel
IEEE Sensors Journal (2019) Vol. 19, Iss. 16, pp. 6872-6882
Open Access | Times Cited: 103
Recent Advances of Artificial Intelligence in Manufacturing Industrial Sectors: A Review
Sung Wook Kim, Jun Ho Kong, Sang Won Lee, et al.
International Journal of Precision Engineering and Manufacturing (2021) Vol. 23, Iss. 1, pp. 111-129
Open Access | Times Cited: 96
Extreme Kernel Sparse Learning for Tactile Object Recognition
Huaping Liu, Jie Qin, Fuchun Sun, et al.
IEEE Transactions on Cybernetics (2016) Vol. 47, Iss. 12, pp. 4509-4520
Closed Access | Times Cited: 88
Event-Driven Visual-Tactile Sensing and Learning for Robots
Tasbolat Taunyazov, Weicong Sng, Brian Y. Lim, et al.
(2020)
Open Access | Times Cited: 88
“Touching to See” and “Seeing to Feel”: Robotic Cross-modal Sensory Data Generation for Visual-Tactile Perception
Jet-Tsyn Lee, Danushka Bollegala, Shan Luo
2022 International Conference on Robotics and Automation (ICRA) (2019), pp. 4276-4282
Open Access | Times Cited: 85
A Review of Robot Learning for Manipulation: Challenges, Representations, and Algorithms
Oliver Kroemer, Scott Niekum, George Konidaris
arXiv (Cornell University) (2019)
Open Access | Times Cited: 80
Tactile-Sensing Technologies: Trends, Challenges and Outlook in Agri-Food Manipulation
Willow Mandil, Vishnu Rajendran, Kiyanoush Nazari, et al.
Sensors (2023) Vol. 23, Iss. 17, pp. 7362-7362
Open Access | Times Cited: 25
Visuo-haptic object perception for robots: an overview
Nicolás Navarro-Guerrero, Sibel Toprak, Josip Josifovski, et al.
Autonomous Robots (2023) Vol. 47, Iss. 4, pp. 377-403
Open Access | Times Cited: 23
Weakly Paired Multimodal Fusion for Object Recognition
Huaping Liu, Yupei Wu, Fuchun Sun, et al.
IEEE Transactions on Automation Science and Engineering (2017) Vol. 15, Iss. 2, pp. 784-795
Closed Access | Times Cited: 79
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