Jun Morimoto
Impact in
- Control and Systems Engineering top 0.5%
- Robot Manipulation and Learning
- Rehabilitation top 0.5%
- Stroke Rehabilitation and Recovery
Papers in
-
- Prosthetics and Rehabilitation Robotics 71
- Robotic Locomotion and Control 61
- Muscle activation and electromyography studies 56
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- Chalcogenide Semiconductor Thin Films 26
- Co-authors
- Takamitsu Matsubara (38 shared papers)Gordon Cheng (22 shared papers)Aleš Ude (21 shared papers)Tomoyuki Noda (45 shared papers)Gen Endo (14 shared papers)Sang-Ho Hyon (32 shared papers)Jun Nakanishi (17 shared papers)Kenji Doya (13 shared papers)
- Journals
- Japanese Journal of Applied Physics (60 papers)Neural Networks (12 papers)IEEE Robotics and Automation Letters (9 papers)Advanced Robotics (6 papers)Applied Physics A (6 papers)
- Partner nations
- JapanIndiaUnited States
In The Last Decade
Jun Morimoto
320 papers receiving 6.6k citations
Jun Morimoto's Hit Papers
Peers
Comparison fields: 5 of 170
- Control and Systems Engineering 2.0k
- Rehabilitation 506
- Biomedical Engineering 3.1k
- Cognitive Neuroscience 1.0k
- Artificial Intelligence 1.2k
Countries citing papers authored by Jun Morimoto
This map shows the geographic impact of Jun Morimoto'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 Jun Morimoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jun Morimoto more than expected).
Fields of papers citing papers by Jun Morimoto
This network shows the impact of papers produced by Jun Morimoto. 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 Jun Morimoto. The network helps show where Jun Morimoto may publish in the future.
Co-authors
The 25 scholars most cited alongside Jun Morimoto, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 336 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2004 | 331 | |
| 2 | 2010 | 306 | |
| 3 | Deep learning, reinforcement learning, and world models Hit paper breakdown → | 2022 | 303 |
| 4 | 2016 | 216 | |
| 5 | 2007 | 187 | |
| 6 | 2008 | 169 | |
| 7 | 2014 | 158 | |
| 8 | 2013 | 148 | |
| 9 | 2016 | 138 | |
| 10 | 2005 | 129 | |
| 11 | 2015 | 118 | |
| 12 | 2017 | 114 | |
| 13 | 2006 | 109 | |
| 14 | 2015 | 95 | |
| 15 | 2003 | 95 | |
| 16 | 2008 | 93 | |
| 17 | 2004 | 91 | |
| 18 | 2011 | 87 | |
| 19 | 2012 | 84 | |
| 20 | 2006 | 76 |
About Jun Morimoto
Jun Morimoto is a scholar working on Biomedical Engineering, Electrical and Electronic Engineering, Control and Systems Engineering, Materials Chemistry and Cognitive Neuroscience, having authored 336 papers that have together received 6.8k indexed citations. Recurring topics across this work include Prosthetics and Rehabilitation Robotics (71 papers), Robotic Locomotion and Control (61 papers), Muscle activation and electromyography studies (56 papers), Robot Manipulation and Learning (49 papers), Reinforcement Learning in Robotics (37 papers), Thermography and Photoacoustic Techniques (28 papers), Stroke Rehabilitation and Recovery (27 papers) and Chalcogenide Semiconductor Thin Films (26 papers). The work is most often cited by research in Control and Systems Engineering (2.0k citations), Rehabilitation (506 citations), Biomedical Engineering (3.1k citations), Cognitive Neuroscience (1.0k citations) and Artificial Intelligence (1.2k citations). Jun Morimoto has collaborated with scholars based in Japan, India and United States. Frequent co-authors include Takamitsu Matsubara, Gordon Cheng, Aleš Ude, Tomoyuki Noda, Gen Endo, Sang-Ho Hyon, Jun Nakanishi, Kenji Doya, Christopher G. Atkeson and Tatsuya Teramae. Their work appears in journals such as Japanese Journal of Applied Physics, Neural Networks, IEEE Robotics and Automation Letters, Advanced Robotics and Applied Physics A.
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.