Kuan‐Ting Yu
Impact in
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- Robot Manipulation and Learning
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- Robotic Path Planning Algorithms
- Advanced Neural Network Applications
- Image and Object Detection Techniques
Papers in
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- Robot Manipulation and Learning 5
- Machine Fault Diagnosis Techniques 2
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- Robotics and Sensor-Based Localization 4
- Co-authors
- Alberto Rodríguez (4 shared papers)Daniel Suo (1 shared paper)Andy Zeng (1 shared paper)Shuran Song (1 shared paper)Jianxiong Xiao (1 shared paper)John J. Leonard (1 shared paper)Twan Koolen (1 shared paper)Russ Tedrake (1 shared paper)
- Journals
- IEEE Access (2 papers)PLoS Pathogens (1 paper)Journal of Field Robotics (1 paper)Sensors and Materials (1 paper)DSpace@MIT (Massachusetts Institute of Technology) (1 paper)
- Partner nations
- United StatesTaiwanGermany
In The Last Decade
Kuan‐Ting Yu
11 papers receiving 550 citations
Kuan‐Ting Yu's Hit Papers
Peers
Comparison fields: 5 of 70
- Control and Systems Engineering 361
- Computer Vision and Pattern Recognition 242
- Aerospace Engineering 189
- Human-Computer Interaction 38
- Geology 34
Countries citing papers authored by Kuan‐Ting Yu
This map shows the geographic impact of Kuan‐Ting Yu's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Kuan‐Ting Yu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kuan‐Ting Yu more than expected).
Fields of papers citing papers by Kuan‐Ting Yu
This network shows the impact of papers produced by Kuan‐Ting Yu. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Kuan‐Ting Yu. The network helps show where Kuan‐Ting Yu may publish in the future.
Co-authors
The 25 scholars most cited alongside Kuan‐Ting Yu, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Multi-view self-supervised deep learning for 6D pose estimation in the Amazon Picking Challenge Hit paper breakdown → | 2017 | 336 |
| 2 | 2014 | 105 | |
| 3 | 2010 | 26 | |
| 4 | 2018 | 23 | |
| 5 | 2015 | 21 | |
| 6 | 2017 | 20 | |
| 7 | 2018 | 20 | |
| 8 | 2020 | 10 | |
| 9 | 2024 | 7 | |
| 10 | 2012 | 6 | |
| 11 | 2020 | 2 | |
| 12 | 2020 | 0 |
About Kuan‐Ting Yu
Kuan‐Ting Yu is a scholar working on Control and Systems Engineering, Aerospace Engineering, Computer Vision and Pattern Recognition, Mechanical Engineering and Cognitive Neuroscience, having authored 12 papers that have together received 576 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (5 papers), Robotics and Sensor-Based Localization (4 papers), Gear and Bearing Dynamics Analysis (3 papers), Soft Robotics and Applications (2 papers), Industrial Vision Systems and Defect Detection (2 papers), Tactile and Sensory Interactions (2 papers), Machine Fault Diagnosis Techniques (2 papers) and Context-Aware Activity Recognition Systems (1 paper). The work is most often cited by research in Control and Systems Engineering (361 citations), Computer Vision and Pattern Recognition (242 citations), Aerospace Engineering (189 citations), Human-Computer Interaction (38 citations) and Geology (34 citations). Kuan‐Ting Yu has collaborated with scholars based in United States, Taiwan and Germany. Frequent co-authors include Alberto Rodríguez, Daniel Suo, Andy Zeng, Shuran Song, Jianxiong Xiao, John J. Leonard, Twan Koolen, Russ Tedrake, Hongkai Dai and Seth Teller. Their work appears in journals such as IEEE Access, PLoS Pathogens, Journal of Field Robotics, Sensors and Materials and DSpace@MIT (Massachusetts Institute of Technology).
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.