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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:

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

Showing 1-25 of 80 citing articles:

How to train your robot with deep reinforcement learning: lessons we have learned
Julian Ibarz, Jie Tan, Chelsea Finn, et al.
The International Journal of Robotics Research (2021) Vol. 40, Iss. 4-5, pp. 698-721
Open Access | Times Cited: 380
Meta-Learning in Neural Networks: A Survey
Timothy M. Hospedales, Antreas Antoniou, Paul Micaelli, et al.
arXiv (Cornell University) (2020)
Closed Access | Times Cited: 227
A Survey of Robot Learning Strategies for Human-Robot Collaboration in Industrial Settings
Debasmita Mukherjee, Kashish Gupta, Li Chang, et al.
Robotics and Computer-Integrated Manufacturing (2021) Vol. 73, pp. 102231-102231
Closed Access | Times Cited: 202
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
Intelligent disassembly of electric-vehicle batteries: a forward-looking overview
Kai Meng, Guiyin Xu, Xianghui Peng, et al.
Resources Conservation and Recycling (2022) Vol. 182, pp. 106207-106207
Closed Access | Times Cited: 100
Variable Compliance Control for Robotic Peg-in-Hole Assembly: A Deep-Reinforcement-Learning Approach
Cristian C. Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar, et al.
Applied Sciences (2020) Vol. 10, Iss. 19, pp. 6923-6923
Open Access | Times Cited: 134
Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning
Kelsey R. Allen, Kevin A. Smith, Joshua B. Tenenbaum
Proceedings of the National Academy of Sciences (2020) Vol. 117, Iss. 47, pp. 29302-29310
Open Access | Times Cited: 94
Self-Supervised Correspondence in Visuomotor Policy Learning
Pete Florence, Lucas Manuelli, Russ Tedrake
IEEE Robotics and Automation Letters (2019) Vol. 5, Iss. 2, pp. 492-499
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
A review on manipulation skill acquisition through teleoperation‐based learning from demonstration
Weiyong Si, Ning Wang, Chenguang Yang
Cognitive Computation and Systems (2021) Vol. 3, Iss. 1, pp. 1-16
Open Access | Times Cited: 62
“Good Robot!”: Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer
Andrew Hundt, Benjamin D. Killeen, Nicholas D. E. Greene, et al.
IEEE Robotics and Automation Letters (2020) Vol. 5, Iss. 4, pp. 6724-6731
Open Access | Times Cited: 56
ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theory
Ajinkya Jain, Rudolf Lioutikov, Caleb Chuck, et al.
(2021), pp. 13670-13677
Open Access | Times Cited: 44
Learning Dense Visual Correspondences in Simulation to Smooth and Fold Real Fabrics
Aditya Ganapathi, Priya Sundaresan, Brijen Thananjeyan, et al.
(2021), pp. 11515-11522
Open Access | Times Cited: 43
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: 42
Residual Learning From Demonstration: Adapting DMPs for Contact-Rich Manipulation
Todor Davchev, Kevin Sebastian Luck, Michael Burke, et al.
IEEE Robotics and Automation Letters (2022) Vol. 7, Iss. 2, pp. 4488-4495
Open Access | Times Cited: 28
Vision-driven Compliant Manipulation for Reliable; High-Precision Assembly Tasks
Andrew S. Morgan, Bowen Wen, Junchi Liang, et al.
(2021)
Open Access | Times Cited: 36
A Novel Hybrid Robot Configuration for Enhanced Accessibility and Space Efficiency
Y. P. D. Kaluarachchi, T. D. S. S. Senarathna, Anjana Lakshan, et al.
(2025), pp. 263-271
Closed Access
Multi-frequency-band deep CNN model for tool wear prediction
Jian Duan, Jie Duan, Hongdi Zhou, et al.
Measurement Science and Technology (2020) Vol. 32, Iss. 6, pp. 065009-065009
Closed Access | Times Cited: 36
A Multi-Level Optimization Framework for Simultaneous Grasping and Motion Planning
Simon Zimmermann, Ghazal Hakimifard, Miguel Zamora, et al.
IEEE Robotics and Automation Letters (2020) Vol. 5, Iss. 2, pp. 2966-2972
Closed Access | Times Cited: 31
Learning Force Control for Contact-Rich Manipulation Tasks With Rigid Position-Controlled Robots
Cristian C. Beltran-Hernandez, Damien Petit, Ixchel G. Ramirez-Alpizar, et al.
IEEE Robotics and Automation Letters (2020) Vol. 5, Iss. 4, pp. 5709-5716
Open Access | Times Cited: 28
Robotic Disassembly Task Training and Skill Transfer Using Reinforcement Learning
Mo Qu, Yongjing Wang, Duc Truong Pham
IEEE Transactions on Industrial Informatics (2023) Vol. 19, Iss. 11, pp. 10934-10943
Closed Access | Times Cited: 10
Learning Precise 3D Manipulation from Multiple Uncalibrated Cameras
Iretiayo Akinola, Jacob Varley, Dmitry Kalashnikov
(2020), pp. 4616-4622
Open Access | Times Cited: 18
A Survey of Multifingered Robotic Manipulation: Biological Results, Structural Evolvements, and Learning Methods
Yinlin Li, Peng Wang, Rui Li, et al.
Frontiers in Neurorobotics (2022) Vol. 16
Open Access | Times Cited: 12
Better-than-Demonstrator Imitation Learning via Automatically-Ranked Demonstrations
Daniel S. Brown, Wonjoon Goo, Scott Niekum
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 16
Learning Hybrid Object Kinematics for Efficient Hierarchical Planning Under Uncertainty
Ajinkya Jain, Scott Niekum
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020), pp. 5253-5260
Open Access | Times Cited: 14
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