João Silvério

1.1k total citations
31 papers, 638 citations indexed

About

João Silvério is a scholar working on Control and Systems Engineering, Artificial Intelligence and Biomedical Engineering. According to data from OpenAlex, João Silvério has authored 31 papers receiving a total of 638 indexed citations (citations by other indexed papers that have themselves been cited), including 28 papers in Control and Systems Engineering, 15 papers in Artificial Intelligence and 10 papers in Biomedical Engineering. Recurrent topics in João Silvério's work include Robot Manipulation and Learning (27 papers), Reinforcement Learning in Robotics (12 papers) and Robotic Mechanisms and Dynamics (11 papers). João Silvério is often cited by papers focused on Robot Manipulation and Learning (27 papers), Reinforcement Learning in Robotics (12 papers) and Robotic Mechanisms and Dynamics (11 papers). João Silvério collaborates with scholars based in Italy, Switzerland and Germany. João Silvério's co-authors include Darwin G. Caldwell, Leonel Rozo, Yanlong Huang, Sylvain Calinon, Fares J. Abu‐Dakka, Ioannis Havoutis, Ville Kyrki, Xiaohui Xiao, Miao Li and Freek Stulp and has published in prestigious journals such as The International Journal of Robotics Research, IEEE Transactions on Robotics and Robotics and Autonomous Systems.

In The Last Decade

João Silvério

29 papers receiving 631 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
João Silvério Italy 13 511 215 184 123 98 31 638
Alexandros Paraschos Germany 12 506 1.0× 321 1.5× 183 1.0× 162 1.3× 75 0.8× 19 658
Matteo Saveriano Italy 13 391 0.8× 161 0.7× 158 0.9× 191 1.6× 75 0.8× 65 609
Rudolf Lioutikov Germany 15 471 0.9× 271 1.3× 155 0.8× 184 1.5× 59 0.6× 24 661
Marco Ewerton Germany 12 374 0.7× 176 0.8× 122 0.7× 122 1.0× 73 0.7× 22 512
Yanlong Huang Italy 12 396 0.8× 168 0.8× 209 1.1× 132 1.1× 56 0.6× 27 522
Jacky Liang United States 7 258 0.5× 192 0.9× 98 0.5× 174 1.4× 92 0.9× 12 516
Daniel Kappler Germany 12 393 0.8× 168 0.8× 189 1.0× 198 1.6× 61 0.6× 20 555
Balakumar Sundaralingam United States 12 345 0.7× 107 0.5× 129 0.7× 206 1.7× 54 0.6× 17 482
Benjamin Burchfiel United States 9 244 0.5× 120 0.6× 81 0.4× 150 1.2× 56 0.6× 16 506
Vincent Padois France 14 357 0.7× 96 0.4× 272 1.5× 102 0.8× 79 0.8× 36 593

Countries citing papers authored by João Silvério

Since Specialization
Citations

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

Fields of papers citing papers by João Silvério

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of João Silvério

This figure shows the co-authorship network connecting the top 25 collaborators of João Silvério. A scholar is included among the top collaborators of João Silvério 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 Silvério. João Silvério 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
1.
Knauer, Markus, Alin Albu‐Schäffer, Freek Stulp, & João Silvério. (2025). Interactive Incremental Learning of Generalizable Skills With Local Trajectory Modulation. IEEE Robotics and Automation Letters. 10(4). 3398–3405. 1 indexed citations
2.
Stulp, Freek, et al.. (2025). Towards Safe and Efficient Learning in the Wild: Guiding RL With Constrained Uncertainty-Aware Movement Primitives. IEEE Robotics and Automation Letters. 10(7). 6880–6887.
3.
Hulin, Thomas, Bernhard Weber, Sylvain Calinon, et al.. (2024). A Probabilistic Approach to Multi-Modal Adaptive Virtual Fixtures. IEEE Robotics and Automation Letters. 9(6). 5298–5305. 2 indexed citations
4.
Stulp, Freek, et al.. (2024). A probabilistic approach for learning and adapting shared control skills with the human in the loop. elib (German Aerospace Center). 15728–15734. 2 indexed citations
5.
Giordano, Alessandro, João Silvério, Thomas Hulin, et al.. (2024). AI-Based Robust and Failure-Tolerant Processes for In-Orbit Manufacturing of Modular Small Satellites. elib (German Aerospace Center). 93–108.
6.
Raffin, Antonin, et al.. (2023). Guiding Reinforcement Learning with Shared Control Templates. elib (German Aerospace Center). 1 indexed citations
7.
Silvério, João, et al.. (2021). Bilateral teleoperation with object-adaptive mapping. Complex & Intelligent Systems. 8(4). 2983–2990. 5 indexed citations
8.
Silvério, João, et al.. (2021). Motion Mappings for Continuous Bilateral Teleoperation. IEEE Robotics and Automation Letters. 6(3). 5048–5055. 15 indexed citations
9.
Abu‐Dakka, Fares J., Yanlong Huang, João Silvério, & Ville Kyrki. (2021). A probabilistic framework for learning geometry-based robot manipulation skills. Robotics and Autonomous Systems. 141. 103761–103761. 25 indexed citations
10.
Silvério, João, et al.. (2020). Fourier movement primitives: an approach for learning rhythmic robot skills from demonstrations. Zenodo (CERN European Organization for Nuclear Research). 9 indexed citations
11.
Huang, Yanlong, Leonel Rozo, João Silvério, & Darwin G. Caldwell. (2019). Non-parametric Imitation Learning of Robot Motor Skills. 6 indexed citations
12.
Silvério, João, Yanlong Huang, Fares J. Abu‐Dakka, Leonel Rozo, & Darwin G. Caldwell. (2019). Uncertainty-Aware Imitation Learning using Kernelized Movement Primitives. arXiv (Cornell University). 90–97. 24 indexed citations
13.
Huang, Yanlong, João Silvério, Leonel Rozo, & Darwin G. Caldwell. (2018). Hybrid Probabilistic Trajectory Optimization Using Null-Space Exploration. 7226–7232. 11 indexed citations
14.
Silvério, João, Yanlong Huang, Leonel Rozo, Sylvain Calinon, & Darwin G. Caldwell. (2018). Probabilistic Learning of Torque Controllers from Kinematic and Force Constraints. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 1–8. 24 indexed citations
15.
Huang, Yanlong, João Silvério, & Darwin G. Caldwell. (2018). Towards Minimal Intervention Control with Competing Constraints. 11. 733–738. 9 indexed citations
16.
Silvério, João, Yanlong Huang, Leonel Rozo, & Darwin G. Caldwell. (2018). An Uncertainty-Aware Minimal Intervention Control Strategy Learned from Demonstrations. 6065–6071. 7 indexed citations
17.
Silvério, João, Sylvain Calinon, Leonel Rozo, & Darwin G. Caldwell. (2018). Learning Task Priorities from Demonstrations. IEEE Transactions on Robotics. 35(1). 78–94. 41 indexed citations
18.
Silvério, João, et al.. (2017). Learning Competing Constraints and Task Priorities from Demonstrations of Bimanual Skills.. 6 indexed citations
19.
Silvério, João, et al.. (2017). A Learning from Demonstration Approach fusing Torque Controllers.. 1 indexed citations
20.
Silvério, João, Leonel Rozo, Sylvain Calinon, & Darwin G. Caldwell. (2015). Learning bimanual end-effector poses from demonstrations using task-parameterized dynamical systems. Infoscience (Ecole Polytechnique Fédérale de Lausanne). 464–470. 45 indexed citations

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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