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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:

Learning Collaborative Pushing and Grasping Policies in Dense Clutter
Bingjie Tang, Matthew Corsaro, George Konidaris, et al.
(2021), pp. 6177-6184
Closed Access | Times Cited: 23

Showing 23 citing articles:

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: 92
Collaborative Pushing and Grasping of Tightly Stacked Objects via Deep Reinforcement Learning
Yuxiang Yang, Zhihao Ni, Mingyu Gao, et al.
IEEE/CAA Journal of Automatica Sinica (2021) Vol. 9, Iss. 1, pp. 135-145
Closed Access | Times Cited: 41
Visual Foresight Trees for Object Retrieval From Clutter With Nonprehensile Rearrangement
Baichuan Huang, Shuai D. Han, Jingjin Yu, et al.
IEEE Robotics and Automation Letters (2021) Vol. 7, Iss. 1, pp. 231-238
Open Access | Times Cited: 31
PLOT: Human-like Push-grasping Synergy Learning in Clutter with One-shot Target Recognition
Xiaoge Cao, Tao Lu, Liming Zheng, et al.
IEEE Transactions on Cognitive and Developmental Systems (2024) Vol. 16, Iss. 4, pp. 1391-1404
Closed Access | Times Cited: 2
Learning Push-Grasping in Dense Clutter
Marios Kiatos, Iason Sarantopoulos, Leonidas Koutras, et al.
IEEE Robotics and Automation Letters (2022) Vol. 7, Iss. 4, pp. 8783-8790
Open Access | Times Cited: 12
Bin Picking for Ship-Building Logistics Using Perception and Grasping Systems
Artur Cordeiro, J. P. Carvalho, Carlos M. Costa, et al.
Robotics (2023) Vol. 12, Iss. 1, pp. 15-15
Open Access | Times Cited: 5
Review of Reinforcement Learning for Robotic Grasping: Analysis and Recommendations
Hiba Sekkat, Oumaima Moutik, Loubna Ourabah, et al.
Statistics Optimization & Information Computing (2023) Vol. 12, Iss. 2, pp. 571-601
Open Access | Times Cited: 4
Self-supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments
Yongliang Wang, Kamal Mokhtar, Cock Heemskerk, et al.
(2024), pp. 13840-13847
Open Access | Times Cited: 1
Learn multi-step object sorting tasks through deep reinforcement learning
J. Bao, Guoqing Zhang, Yi Peng, et al.
Robotica (2022) Vol. 40, Iss. 11, pp. 3878-3894
Closed Access | Times Cited: 6
Position‐aware pushing and grasping synergy with deep reinforcement learning in clutter
Min Zhao, Guoyu Zuo, Shuangyue Yu, et al.
CAAI Transactions on Intelligence Technology (2023) Vol. 9, Iss. 3, pp. 738-755
Open Access | Times Cited: 3
Grasp Planning Based on Deep Reinforcement Learning: A Brief Survey
Zixin Tang, Xin Xu, Yifei Shi
2021 China Automation Congress (CAC) (2021)
Closed Access | Times Cited: 4
Learning Sliding Policy of Flat Multi-target Objects in Clutter Scenes
Liangdong Wu, Jiaxi Wu, Zhengwei Li, et al.
Information Technology And Control (2024) Vol. 53, Iss. 1, pp. 5-18
Open Access
Investigation of a single object shape for efficient learning in bin picking of multiple types of objects
Isamu Bungo, Tomohiro Hayakawa, Toshiyuki Yasuda
Artificial Life and Robotics (2024) Vol. 29, Iss. 2, pp. 372-379
Closed Access
Learning Extrinsic Dexterity with Parameterized Manipulation Primitives
Shih-Min Yang, Martin Magnusson, Johannes A. Stork, et al.
(2024), pp. 5404-5410
Open Access
Dual-Critic Deep Reinforcement Learning for Push-Grasping Synergy in Cluttered Environment
Jincheng Zhong, Yew Wee Wong, Jiong Jin, et al.
(2024), pp. 3138-3144
Closed Access
Systematic Review of Smart Robotic Manufacturing in the Context of Industry 4.0
Lu Anh Duy Phan, Ha Quang Thinh Ngo
(2024), pp. 19-42
Closed Access
An Adapter for Interactive Object Retrieval on the Shelf
Hang Li, Peixing You, Guokang Wang, et al.
(2024), pp. 505-510
Closed Access
Environment Based Multi-Object Grasp Planning in Bin Picking Process
Heng Xiong, Fengying Xie, Long Bai, et al.
(2024), pp. 493-498
Closed Access
Learning Pre-Grasp Manipulation of Multiple Flat Target Objects in Clutter
Liangdong Wu, Jiaxi Wu, Yurou Chen, et al.
(2023), pp. 371-376
Closed Access | Times Cited: 1
Collaborative Learning of Deep Reinforcement Pushing and Grasping based on Coordinate Attention in Clutter
Min Zhao, Guoyu Zuo, Gao Huang
(2022), pp. 156-161
Closed Access | Times Cited: 2
Sim-to-Real Transfer of Environment-Constrained Visuomotor Policy with Pixel-Level Adversarial Domain Adaptation
Xingshuo Jing, Kun Qian, Boyi Duan, et al.
(2023)
Closed Access
Probabilistic Slide-support Manipulation Planning in Clutter
Shusei Nagato, Tomohiro Motoda, Takao Nishi, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023), pp. 1016-1022
Open Access
Optimization of Industrial Robot Grasping Processes with Q-Learning
Manuel Belke, Till Joeressen, Oliver Petrović, et al.
(2023), pp. 113-119
Closed Access
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