Roberto Calandra

38 papers receiving 1.5k citations

Peers

Roberto Calandra
Comparison fields: 5 of 108
  • Control and Systems Engineering 679
  • Cognitive Neuroscience 427
  • Human-Computer Interaction 123
  • Biomedical Engineering 598
  • Artificial Intelligence 455
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Heni Ben Amor United States
Petar Kormushev Italy
Duy Nguyen-Tuong Germany
Adham Atyabi United States
Yan Wu Singapore
Olivier Gibaru France
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Richard Alan Peters United States
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Countries citing papers authored by Roberto Calandra

Since Specialization
Citations

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

Fields of papers citing papers by Roberto Calandra

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Roberto Calandra, 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 Roberto Calandra Line = papers co-authored together Roberto Calandra links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 40 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018247
2 2015161
3 2016110
4 202296
5 201981
6 202170
7 201659
8 202254
9 201451
10 201750
11 201646
12 202245
13 201940
14 201237
15
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
201736
16 201435
17 201833
18 201532
19 202431
20 202429

About Roberto Calandra

Roberto Calandra is a scholar working on Control and Systems Engineering, Artificial Intelligence, Biomedical Engineering, Cognitive Neuroscience and Computational Theory and Mathematics, having authored 40 papers that have together received 1.5k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (13 papers), Reinforcement Learning in Robotics (10 papers), Tactile and Sensory Interactions (9 papers), Robotic Locomotion and Control (6 papers), Advanced Multi-Objective Optimization Algorithms (6 papers), Muscle activation and electromyography studies (5 papers), Machine Learning and Data Classification (4 papers) and Gaussian Processes and Bayesian Inference (4 papers). The work is most often cited by research in Control and Systems Engineering (679 citations), Cognitive Neuroscience (427 citations), Human-Computer Interaction (123 citations), Biomedical Engineering (598 citations) and Artificial Intelligence (455 citations). Roberto Calandra has collaborated with scholars based in United States, Germany and United Kingdom. Frequent co-authors include Jan Peters, Marc Peter Deisenroth, Sergey Levine, André Seyfarth, Justin Lin, Dinesh Jayaraman, Edward H. Adelson, Andrew Owens, Wenzhen Yuan and Jitendra Malik. Their work appears in journals such as IEEE Robotics and Automation Letters, IEEE Transactions on Robotics, IEEE Transactions on Biomedical Engineering, Robotics and Autonomous Systems and Journal of Artificial Intelligence Research.

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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