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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:

Sim-To-Real via Sim-To-Sim: Data-Efficient Robotic Grasping via Randomized-To-Canonical Adaptation Networks
Stephen James, Paul Wohlhart, Mrinal Kalakrishnan, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019), pp. 12619-12629
Closed Access | Times Cited: 366

Showing 1-25 of 366 citing articles:

Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey
Wenshuai Zhao, Jorge Peña Queralta, Tomi Westerlund
2021 IEEE Symposium Series on Computational Intelligence (SSCI) (2020), pp. 737-744
Open Access | Times Cited: 487
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
A Survey on Learning-Based Robotic Grasping
Kilian Kleeberger, Richard Bormann, Werner Kraus, et al.
Current Robotics Reports (2020) Vol. 1, Iss. 4, pp. 239-249
Open Access | Times Cited: 208
RLBench: The Robot Learning Benchmark & Learning Environment
Stephen James, Zicong Ma, David Rovick Arrojo, et al.
IEEE Robotics and Automation Letters (2020) Vol. 5, Iss. 2, pp. 3019-3026
Closed Access | Times Cited: 193
LiDARsim: Realistic LiDAR Simulation by Leveraging the Real World
Sivabalan Manivasagam, Shenlong Wang, Kelvin Wong, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), pp. 11164-11173
Open Access | Times Cited: 170
Kubric: A scalable dataset generator
Klaus Greff, Francois Belletti, Lucas Beyer, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022), pp. 3739-3751
Open Access | Times Cited: 116
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: 91
Learning high-DOF reaching-and-grasping via dynamic representation of gripper-object interaction
Qijin She, Ruizhen Hu, Juzhan Xu, et al.
ACM Transactions on Graphics (2022) Vol. 41, Iss. 4, pp. 1-14
Closed Access | Times Cited: 76
Deep Learning on Monocular Object Pose Detection and Tracking: A Comprehensive Overview
Zhaoxin Fan, Yazhi Zhu, Yulin He, et al.
ACM Computing Surveys (2022) Vol. 55, Iss. 4, pp. 1-40
Open Access | Times Cited: 72
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
Robert Kirk, Amy Zhang, Edward Grefenstette, et al.
Journal of Artificial Intelligence Research (2023) Vol. 76, pp. 201-264
Open Access | Times Cited: 72
Parallel Learning: Overview and Perspective for Computational Learning Across Syn2Real and Sim2Real
Qinghai Miao, Yisheng Lv, Min Huang, et al.
IEEE/CAA Journal of Automatica Sinica (2023) Vol. 10, Iss. 3, pp. 603-631
Closed Access | Times Cited: 54
A Survey on Policy Search Algorithms for Learning Robot Controllers in a Handful of Trials
Konstantinos Chatzilygeroudis, Vassilis Vassiliades, Freek Stulp, et al.
IEEE Transactions on Robotics (2019) Vol. 36, Iss. 2, pp. 328-347
Open Access | Times Cited: 133
RL-CycleGAN: Reinforcement Learning Aware Simulation-to-Real
Kanishka Rao, C.J. Harris, Alex Irpan, et al.
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020), pp. 11154-11163
Open Access | Times Cited: 130
PointNet++ Grasping: Learning An End-to-end Spatial Grasp Generation Algorithm from Sparse Point Clouds
Peiyuan Ni, Wenguang Zhang, Xiaoxiao Zhu, et al.
(2020), pp. 3619-3625
Open Access | Times Cited: 108
Object Detection Recognition and Robot Grasping Based on Machine Learning: A Survey
Qiang Bai, Shaobo Li, Jing Yang, et al.
IEEE Access (2020) Vol. 8, pp. 181855-181879
Open Access | Times Cited: 101
iGibson 1.0: A Simulation Environment for Interactive Tasks in Large Realistic Scenes
Bokui Shen, Fei Xia, Chengshu Li, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2021)
Open Access | Times Cited: 98
Crossing the Reality Gap: A Survey on Sim-to-Real Transferability of Robot Controllers in Reinforcement Learning
Erica Salvato, Gianfranco Fenu, Eric Medvet, et al.
IEEE Access (2021) Vol. 9, pp. 153171-153187
Open Access | Times Cited: 95
Simulation-Based Reinforcement Learning for Real-World Autonomous Driving
Błażej Osiński, Adam Jakubowski, Paweł Zięcina, et al.
(2020)
Open Access | Times Cited: 93
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
Learning Fast Adaptation With Meta Strategy Optimization
Wenhao Yu, Jie Tan, Yunfei Bai, et al.
IEEE Robotics and Automation Letters (2020) Vol. 5, Iss. 2, pp. 2950-2957
Open Access | Times Cited: 74
A Survey of Generalisation in Deep Reinforcement Learning
Robert Kirk, Amy Zhang, Edward Grefenstette, et al.
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 57
RetinaGAN: An Object-aware Approach to Sim-to-Real Transfer
Daniel E. Ho, Kanishka Rao, Zhuo Xu, et al.
(2021), pp. 10920-10926
Open Access | Times Cited: 56
BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning
Eric Jang, Alex Irpan, Mohi Khansari, et al.
arXiv (Cornell University) (2022)
Open Access | Times Cited: 55
Robot Learning From Randomized Simulations: A Review
Fabio Muratore, Fábio Ramos, Greg Turk, et al.
Frontiers in Robotics and AI (2022) Vol. 9
Open Access | Times Cited: 48
Transferring policy of deep reinforcement learning from simulation to reality for robotics
Hao Ju, Rongshun Juan, Randy Gómez, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 12, pp. 1077-1087
Closed Access | Times Cited: 47
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