Darrin C. Bentivegna

748 citations
18 papers · 501 indexed · h-index 10

Darrin C. Bentivegna

16 papers receiving 463 citations

Peers

Darrin C. Bentivegna
Comparison fields: 5 of 64
  • Control and Systems Engineering 246
  • Computer Vision and Pattern Recognition 120
  • Artificial Intelligence 186
  • Human-Computer Interaction 26
  • Biomedical Engineering 189
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Mark Edmonds United States
Hiroaki Arie Japan
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Michael Dose Germany
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Citations per field
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Citations per year

Countries citing papers authored by Darrin C. Bentivegna

Since Specialization
Citations

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

Fields of papers citing papers by Darrin C. Bentivegna

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Darrin C. Bentivegna. 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 Darrin C. Bentivegna. The network helps show where Darrin C. Bentivegna may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Darrin C. Bentivegna, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Darrin C. Bentivegna Line = papers co-authored together Darrin C. Bentivegna links everyone, so they are left out of the graph.

All Works

18 of 18 papers shown
#Work
1 202026
2 20167
3 200820
4 200734
5 200772
6 20077
7 200628
8 200661
9 20043
10 20046
11 20046
12 200466
13 200440
14 200328
15
Using Primitives in Learning From Observation: A Preliminary Report
20031
16
A Framework for Learning From Observation Using Primitives
20021
17 200291
18 19984

About Darrin C. Bentivegna

Darrin C. Bentivegna is a scholar working on Control and Systems Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Hardware and Architecture and Biomedical Engineering, having authored 18 papers that have together received 501 indexed citations. Recurring topics across this work include Robot Manipulation and Learning (6 papers), Reinforcement Learning in Robotics (5 papers), Prosthetics and Rehabilitation Robotics (4 papers), Robotic Locomotion and Control (4 papers), Human Pose and Action Recognition (3 papers), Robotic Path Planning Algorithms (3 papers), Soft Robotics and Applications (2 papers) and Modular Robots and Swarm Intelligence (1 paper). The work is most often cited by research in Control and Systems Engineering (246 citations), Computer Vision and Pattern Recognition (120 citations), Artificial Intelligence (186 citations), Human-Computer Interaction (26 citations) and Biomedical Engineering (189 citations). Darrin C. Bentivegna has collaborated with scholars based in United States, Japan and United Kingdom. Frequent co-authors include Christopher G. Atkeson, Gordon Cheng, Aleš Ude, Jung-Yup Kim, Jun Nakanishi, Jun Morimoto, Sang-Ho Hyon, William K. Durfee, Gen Endo and Rajesh P. N. Rao. Their work appears in journals such as Robotics and Autonomous Systems, Soft Robotics, International Journal of Humanoid Robotics, Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE and mediaTUM (Technical University of Munich).

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