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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:

Human-Inspired Robotic Grasp Control With Tactile Sensing
Joseph M. Romano, Kaijen Hsiao, Günter Niemeyer, et al.
IEEE Transactions on Robotics (2011) Vol. 27, Iss. 6, pp. 1067-1079
Closed Access | Times Cited: 382

Showing 1-25 of 382 citing articles:

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
A hierarchically patterned, bioinspired e-skin able to detect the direction of applied pressure for robotics
Clémentine M. Boutry, Marc Negre, Mikael Jorda, et al.
Science Robotics (2018) Vol. 3, Iss. 24
Open Access | Times Cited: 738
Tactile sensing in dexterous robot hands — Review
Zhanat Kappassov, Juan Antonio Corrales Ramón, Véronique Perdereau
Robotics and Autonomous Systems (2015) Vol. 74, pp. 195-220
Open Access | Times Cited: 661
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
Skin-inspired quadruple tactile sensors integrated on a robot hand enable object recognition
Guozhen Li, Shiqiang Liu, Liangqi Wang, et al.
Science Robotics (2020) Vol. 5, Iss. 49
Closed Access | Times Cited: 332
Integrated task and motion planning in belief space
Leslie Pack Kaelbling, Tomás Lozano‐Pérez
The International Journal of Robotics Research (2013) Vol. 32, Iss. 9-10, pp. 1194-1227
Open Access | Times Cited: 321
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
Leveraging big data for grasp planning
Daniel Kappler, Jeannette Bohg, Stefan Schaal
(2015), pp. 4304-4311
Closed Access | Times Cited: 274
Flexible and stretchable fabric-based tactile sensor
Gereon Büscher, Risto Kõiva, Carsten Schürmann, et al.
Robotics and Autonomous Systems (2014) Vol. 63, pp. 244-252
Open Access | Times Cited: 216
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
Force estimation and slip detection/classification for grip control using a biomimetic tactile sensor
Zhe Su, Karol Hausman, Yevgen Chebotar, et al.
(2015), pp. 297-303
Closed Access | Times Cited: 203
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
Review of machine learning in robotic grasping control in space application
Hadi Jahanshahi, Zheng Zhu
Acta Astronautica (2024) Vol. 220, pp. 37-61
Open Access | Times Cited: 18
Tactile sensors for robotic applications
Pedro Silva Girão, Pedro M. Ramos, Octavian Postolache, et al.
Measurement (2012) Vol. 46, Iss. 3, pp. 1257-1271
Open Access | Times Cited: 185
Towards Reliable Grasping and Manipulation in Household Environments
Matei Ciocarlie, Kaijen Hsiao, E. Gil Jones, et al.
Springer tracts in advanced robotics (2013), pp. 241-252
Closed Access | Times Cited: 172
More Than a Feeling: Learning to Grasp and Regrasp Using Vision and Touch
Roberto Calandra, Andrew Owens, Dinesh Jayaraman, et al.
IEEE Robotics and Automation Letters (2018) Vol. 3, Iss. 4, pp. 3300-3307
Open Access | Times Cited: 157
Pressure Sensor: State of the Art, Design, and Application for Robotic Hand
Ahmed M. M. Almassri, Wan Zuha Wan Hasan, Siti Anom Ahmad, et al.
Journal of Sensors (2015) Vol. 2015, pp. 1-12
Open Access | Times Cited: 155
Learning robot in-hand manipulation with tactile features
Herke van Hoof, Tucker Hermans, Gerhard Neumann, et al.
(2015)
Open Access | Times Cited: 152
Single-Grasp Object Classification and Feature Extraction with Simple Robot Hands and Tactile Sensors
Adam J. Spiers, Minas Liarokapis, Berk Çallı, et al.
IEEE Transactions on Haptics (2016) Vol. 9, Iss. 2, pp. 207-220
Closed Access | Times Cited: 141
Robotic learning of haptic adjectives through physical interaction
Vivian Chu, Ian McMahon, Lorenzo Riano, et al.
Robotics and Autonomous Systems (2014) Vol. 63, pp. 279-292
Closed Access | Times Cited: 132
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
Recent progress in tactile sensing and sensors for robotic manipulation: can we turn tactile sensing into vision?
Akihiko Yamaguchi, Christopher G. Atkeson
Advanced Robotics (2019) Vol. 33, Iss. 14, pp. 661-673
Closed Access | Times Cited: 116
Mobile Manipulation in Unstructured Environments: Perception, Planning, and Execution
Sachin Chitta, Edward C. Jones, Matei Ciocarlie, et al.
IEEE Robotics & Automation Magazine (2012) Vol. 19, Iss. 2, pp. 58-71
Closed Access | Times Cited: 114
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
Roberto Calandra, Andrew Owens, Manu Upadhyaya, et al.
arXiv (Cornell University) (2017)
Open Access | Times Cited: 112
Towards learning hierarchical skills for multi-phase manipulation tasks
Oliver Kroemer, Christian Daniel, Gerhard Neumann, et al.
(2015)
Open Access | Times Cited: 111
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