Sho Sakaino

1.7k citations
153 papers · 1.2k indexed · h-index 17
Topics
Robot Manipulation and Learning (84 papers)Teleoperation and Haptic Systems (75 papers)Soft Robotics and Applications (34 papers)
Partner nations
JapanIndiaGermany

In The Last Decade

Sho Sakaino

140 papers receiving 1.2k citations

Peers

Sho Sakaino
Comparison fields: 5 of 72
  • Control and Systems Engineering 761
  • Mechanical Engineering 645
  • Biomedical Engineering 564
  • Cognitive Neuroscience 168
  • Computer Vision and Pattern Recognition 90
Replace Ryo Kikuuwe with:
Ryo Kikuuwe Japan
Naoyuki Takesue Japan
Guowu Wei United Kingdom
Hiroyuki Nabae Japan
Kazuhiko Terashima Japan
Sungchul Kang South Korea
Yuichi Tsumaki Japan
Takahiro Nozaki Japan
Manolo Garabini Italy
Pyung‐Hun Chang South Korea
Sho Sakaino relative to Ryo Kikuuwe Japan Ryo Kikuuwe's profile →
Citations per field
00.5×1.5×2.2×
Ryo Kikuuwe · 1×
Citations per year

Countries citing papers authored by Sho Sakaino

Since Specialization
Citations

This map shows the geographic impact of Sho Sakaino'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 Sho Sakaino with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sho Sakaino more than expected).

Fields of papers citing papers by Sho Sakaino

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sho Sakaino. 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 Sho Sakaino. The network helps show where Sho Sakaino may publish in the future.

Co-authorship network of co-authors of Sho Sakaino

This figure shows the co-authorship network connecting the top 25 collaborators of Sho Sakaino. A scholar is included among the top collaborators of Sho Sakaino based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sho Sakaino. Sho Sakaino is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1 5
2 2
3 0
4 1
5
Imitation Learning for Human-robot Cooperation Using Bilateral Control.
0
6 1
7 0
8 5
9 2
10 0
11 3
12 8
13 1
14 12
15 3
16 1
17 5
18 2
19 11
20 2

About Sho Sakaino

Sho Sakaino is a scholar working on Control and Systems Engineering, Mechanical Engineering and Human-Computer Interaction, having authored 153 papers that have together received 1.2k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (84 papers), Teleoperation and Haptic Systems (75 papers) and Soft Robotics and Applications (34 papers). The work is most often cited by research in Control and Systems Engineering (761 citations), Mechanical Engineering (645 citations) and Human-Computer Interaction (89 citations). Sho Sakaino has collaborated with scholars based in Japan, India and Germany. Frequent co-authors include Toshiaki Tsuji, Tomoya Sato, Kouhei Ohnishi, Tomoya Kitamura, Kouhei Ohnishi, Takeshi Kaneko, Tsuyoshi Adachi, Shigeru Abe, Yasuyoshi Kaneko and Naoyuki Kurita. Their work appears in journals such as IEEE Transactions on Industrial Electronics, IEEE Access and Sensors.

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.

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