Roberto Calandra

35 papers receiving 1.3k citations

Peers

Roberto Calandra
Comparison fields: 5 of 103
  • Control and Systems Engineering 624
  • Human-Computer Interaction 119
  • Cognitive Neuroscience 402
  • Biomedical Engineering 539
  • Artificial Intelligence 376
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Heni Ben Amor United States
Freek Stulp Germany
Eric Rohmer Brazil
Petar Kormushev Italy
Yan Wu Singapore
Olivier Gibaru France
Di‐Hua Zhai China
Adham Atyabi United States
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Duy Nguyen-Tuong Germany
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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 36 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018235
2 2015152
3 2016100
4 202293
5 201974
6 202167
7 201656
8 202248
9 201446
10 201746
11 201642
12 202242
13 201940
14
The Feeling of Success: Does Touch Sensing Help Predict Grasp Outcomes?
201736
15 201529
16 201828
17 202028
18 202424
19 202422
20 202122

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 36 papers that have together received 1.4k indexed citations. Recurring topics across this work include Robot Manipulation and Learning (13 papers), Reinforcement Learning in Robotics (9 papers), Tactile and Sensory Interactions (9 papers), Robotic Locomotion and Control (5 papers), Advanced Multi-Objective Optimization Algorithms (5 papers), Muscle activation and electromyography studies (5 papers), Gaussian Processes and Bayesian Inference (4 papers) and Evolutionary Algorithms and Applications (3 papers). The work is most often cited by research in Control and Systems Engineering (624 citations), Human-Computer Interaction (119 citations), Cognitive Neuroscience (402 citations), Biomedical Engineering (539 citations) and Artificial Intelligence (376 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, Wenzhen Yuan, Andrew Owens, Edward H. Adelson and Jitendra Malik. Their work appears in journals such as IEEE Robotics and Automation Letters, IEEE Transactions on Robotics, IEEE Transactions on Biomedical Engineering, Soft Robotics and Robotics and Autonomous Systems.

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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