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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 to Manipulate Unknown Objects in Clutter by Reinforcement
Abdeslam Boularias, J. Andrew Bagnell, Anthony Stentz
Proceedings of the AAAI Conference on Artificial Intelligence (2015) Vol. 29, Iss. 1
Open Access | Times Cited: 102

Showing 26-50 of 102 citing articles:

Inferring Occluded Geometry Improves Performance When Retrieving an Object from Dense Clutter
Andrew Price, Linyi Jin, Dmitry Berenson
Springer proceedings in advanced robotics (2022), pp. 376-392
Open Access | Times Cited: 14
Algorithms and Systems for Manipulating Multiple Objects
Zherong Pan, Andy Zeng, Yunzhu Li, et al.
IEEE Transactions on Robotics (2022) Vol. 39, Iss. 1, pp. 2-20
Closed Access | Times Cited: 14
Learning to Slide Unknown Objects with Differentiable Physics Simulations
Chang‐Kyu Song, Abdeslam Boularias
(2020)
Open Access | Times Cited: 23
Efficient push-grasping for multiple target objects in clutter environments
Liangdong Wu, Yurou Chen, Zhengwei Li, et al.
Frontiers in Neurorobotics (2023) Vol. 17
Open Access | Times Cited: 7
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
Reconfigurable Grasp Planning Pipeline with Grasp Synthesis and Selection Applied to Picking Operations in Aerospace Factories
J. P. Carvalho, Carlos M. Costa, Luís F. Rocha, et al.
Robotics and Computer-Integrated Manufacturing (2020) Vol. 67, pp. 102032-102032
Open Access | Times Cited: 19
Learning Bifunctional Push-Grasping Synergistic Strategy for Goal-Agnostic and Goal-Oriented Tasks
Dafa Ren, Shuang Wu, Xiaofan Wang, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2023)
Open Access | Times Cited: 6
Learning Visual Affordances for Robotic Manipulation
Andy Zeng
(2019)
Closed Access | Times Cited: 19
A Probabilistic Model for Planar Sliding of Objects with Unknown Material Properties: Identification and Robust Planning
Chang‐Kyu Song, Abdeslam Boularias
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020), pp. 5311-5318
Open Access | Times Cited: 16
Push-to-See: Learning Non-Prehensile Manipulation to Enhance Instance Segmentation via Deep Q-Learning
Baris Serhan, Harit Pandya, Ayşe Küçükyılmaz, et al.
2022 International Conference on Robotics and Automation (ICRA) (2022), pp. 1513-1519
Open Access | Times Cited: 10
A Logic-Based Computational Framework for Inferring Cognitive Affordances
Vasanth Sarathy, Matthias Scheutz
IEEE Transactions on Cognitive and Developmental Systems (2016) Vol. 10, Iss. 1, pp. 26-43
Closed Access | Times Cited: 17
Knowledge Induced Deep Q-Network for a Slide-to-Wall Object Grasping.
Hengyue Liang, Xibai Lou, Changhyun Choi
arXiv (Cornell University) (2019)
Closed Access | Times Cited: 16
On Minimizing the Number of Running Buffers for Tabletop Rearrangement
Kaizhou Gao, Siwei Feng, Jingjin Yu
(2021)
Open Access | Times Cited: 13
A Hybrid Approach for Learning to Shift and Grasp with Elaborate Motion Primitives
Zohar Feldman, Hanna Ziesche, Ngo Anh Vien, et al.
2022 International Conference on Robotics and Automation (ICRA) (2022), pp. 6365-6371
Open Access | Times Cited: 9
Saliency-guided adaptive seeding for supervoxel segmentation
Ge Gao, Mikko Lauri, Jianwei Zhang, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2017), pp. 4938-4943
Open Access | Times Cited: 16
Parallel Monte Carlo Tree Search with Batched Rigid-body Simulations for Speeding up Long-Horizon Episodic Robot Planning
Baichuan Huang, Abdeslam Boularias, Jingjin Yu
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2022), pp. 1153-1160
Open Access | Times Cited: 8
DGCM-Net: Dense Geometrical Correspondence Matching Network for Incremental Experience-Based Robotic Grasping
Timothy Patten, Kiru Park, Markus Vincze
Frontiers in Robotics and AI (2020) Vol. 7
Open Access | Times Cited: 12
Domain-Independent Unsupervised Detection of Grasp Regions to grasp Novel Objects
Siddhartha Vibhu Pharswan, Mohit Vohra, Ashish Kumar, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2019), pp. 640-645
Closed Access | Times Cited: 12
An Efficient Robotic Pushing and Grasping Method in Cluttered Scene
Sheng Yu, Di‐Hua Zhai, Yuanqing Xia, et al.
IEEE Transactions on Cybernetics (2024) Vol. 54, Iss. 9, pp. 4889-4902
Closed Access | Times Cited: 1
A Real-Time Technique for Positioning a Wheelchair-Mounted Robotic Arm for Household Manipulation Tasks
Pooya Abolghasemi, Rouhollah Rahmatizadeh, Aman Behal, et al.
National Conference on Artificial Intelligence (2016)
Closed Access | Times Cited: 10
Object manipulation without hands
Shoko Sugasawa, Barbara Webb, Susan D. Healy
Proceedings of the Royal Society B Biological Sciences (2021) Vol. 288, Iss. 1947
Open Access | Times Cited: 10
Towards Robotic Picking of Targets with Background Distractors using Deep Reinforcement Learning
Chen Chen, Hsieh-Yu Li, Xuewen Zhang, et al.
(2019), pp. 166-171
Closed Access | Times Cited: 10
Distributed Reinforcement Learning of Targeted Grasping with Active Vision for Mobile Manipulators
Yasuhiro Fujita, Kota Uenishi, Avinash Ummadisingu, et al.
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2020), pp. 9712-9719
Open Access | Times Cited: 10
Minimizing running buffers for tabletop object rearrangement: Complexity, fast algorithms, and applications
Kai Gao, Si Wei Feng, Baichuan Huang, et al.
The International Journal of Robotics Research (2023) Vol. 42, Iss. 10, pp. 755-776
Open Access | Times Cited: 3
Learning to Singulate Objects using a Push Proposal Network
Andreas Eitel, Nico Hauff, Wolfram Burgard
arXiv (Cornell University) (2017)
Closed Access | Times Cited: 9

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