João Bimbo

36 papers receiving 1.1k citations

Hit Papers

Robotic tactile perception of object properties: A review20172026202020232017100200300

Peers

João Bimbo
Comparison fields: 5 of 69
  • Biomedical Engineering 666
  • Cognitive Neuroscience 551
  • Control and Systems Engineering 511
  • Mechanical Engineering 266
  • Human-Computer Interaction 158
Replace Perla Maiolino with:
Perla Maiolino United Kingdom
Vincent Duchaine Canada
Véronique Perdereau France
Alexander Schmitz Japan
Adam J. Spiers United Kingdom
Shan Luo United Kingdom
Van Anh Ho Japan
Salvatore Pirozzi Italy
Philipp Mittendorfer Germany
João Bimbo relative to Perla Maiolino United Kingdom Perla Maiolino's profile →
Citations per field
00.5×1.5×
Perla Maiolino · 1×
Citations per year

Countries citing papers authored by João Bimbo

Since Specialization
Citations

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

Fields of papers citing papers by João Bimbo

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of João Bimbo

This figure shows the co-authorship network connecting the top 25 collaborators of João Bimbo. A scholar is included among the top collaborators of João Bimbo 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 João Bimbo. João Bimbo 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 6
2 11
3 2
4 15
5 28
6 8
7 15
8 5
9 62
10
Robotic tactile perception of object properties: A reviewbreakdown →
301
11 51
12 6
13 17
14 31
15 51
16 16
17
Object surface classificaiton based on friction properties for intelligent robotic hands
6
18 19
19 52
20 6

About João Bimbo

João Bimbo is a scholar working on Control and Systems Engineering, Cognitive Neuroscience and Human-Computer Interaction, having authored 36 papers that have together received 1.1k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (23 papers), Soft Robotics and Applications (15 papers) and Tactile and Sensory Interactions (15 papers). The work is most often cited by research in Human-Computer Interaction (158 citations), Cognitive Neuroscience (551 citations) and Control and Systems Engineering (511 citations). João Bimbo has collaborated with scholars based in Italy, United Kingdom and United States. Frequent co-authors include Hongbin Liu, Shan Luo, Kaspar Althoefer, Ravinder Dahiya, Domenico Prattichizzo, Lakmal Seneviratne, Claudio Pacchierotti, Nikos G. Tsagarakis, Xiaojing Song and Helge Würdemann. Their work appears in journals such as Sensors, The International Journal of Robotics Research and IEEE Transactions on Robotics.

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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2026