Roberto Capobianco

833 total citations
25 papers, 205 citations indexed

About

Roberto Capobianco is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Control and Systems Engineering. According to data from OpenAlex, Roberto Capobianco has authored 25 papers receiving a total of 205 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 7 papers in Computer Vision and Pattern Recognition and 5 papers in Control and Systems Engineering. Recurrent topics in Roberto Capobianco's work include Robotic Path Planning Algorithms (5 papers), Reinforcement Learning in Robotics (5 papers) and Explainable Artificial Intelligence (XAI) (4 papers). Roberto Capobianco is often cited by papers focused on Robotic Path Planning Algorithms (5 papers), Reinforcement Learning in Robotics (5 papers) and Explainable Artificial Intelligence (XAI) (4 papers). Roberto Capobianco collaborates with scholars based in Italy, United States and France. Roberto Capobianco's co-authors include Daniele Nardi, Domenico D. Bloisi, María T. Lázaro, Giorgio Grisetti, Luca Iocchi, Emanuele Bastianelli, Shreyansh Daftry, Giuseppe Santucci, Romain Giot and Romain Bourqui and has published in prestigious journals such as Machine Learning, Robotics and Autonomous Systems and Computer Graphics Forum.

In The Last Decade

Roberto Capobianco

23 papers receiving 199 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Roberto Capobianco Italy 9 82 72 50 26 18 25 205
Musad Haque United States 8 32 0.4× 35 0.5× 54 1.1× 108 4.2× 13 0.7× 17 350
Amir‐massoud Farahmand United States 9 84 1.0× 55 0.8× 17 0.3× 52 2.0× 29 1.6× 23 204
Karthik Narayan United States 6 63 0.8× 186 2.6× 106 2.1× 68 2.6× 5 0.3× 9 283
V. Vaithiyanathan India 10 81 1.0× 132 1.8× 22 0.4× 7 0.3× 17 0.9× 58 330
Minyi Zhong United States 5 35 0.4× 36 0.5× 47 0.9× 58 2.2× 18 1.0× 7 307
Chuanyan Hao China 9 74 0.9× 122 1.7× 20 0.4× 14 0.5× 2 0.1× 28 279
Norimasa Yoshida Japan 11 15 0.2× 50 0.7× 14 0.3× 22 0.8× 10 0.6× 42 401
Martin Rosalie France 8 16 0.2× 81 1.1× 82 1.6× 23 0.9× 18 1.0× 16 247
Xun Yuan China 9 111 1.4× 319 4.4× 37 0.7× 8 0.3× 4 0.2× 31 401
N.C. Gupta India 6 127 1.5× 55 0.8× 40 0.8× 6 0.2× 15 0.8× 24 269

Countries citing papers authored by Roberto Capobianco

Since Specialization
Citations

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

Fields of papers citing papers by Roberto Capobianco

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Roberto Capobianco

This figure shows the co-authorship network connecting the top 25 collaborators of Roberto Capobianco. A scholar is included among the top collaborators of Roberto Capobianco 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 Roberto Capobianco. Roberto Capobianco 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.
Capobianco, Roberto, et al.. (2025). XAI ‐Guided Continual Learning: Rationale, Methods, and Future Directions. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 15(4).
2.
Weaver, Catherine, Roberto Capobianco, Peter R. Wurman, Peter Stone, & Masayoshi Tomizuka. (2024). Real-Time Trajectory Generation via Dynamic Movement Primitives for Autonomous Racing. 352–359. 1 indexed citations
3.
Capobianco, Roberto, et al.. (2023). An Overview of Environmental Features that Impact Deep Reinforcement Learning in Sparse-Reward Domains. Journal of Artificial Intelligence Research. 76. 1181–1218. 8 indexed citations
4.
Bourqui, Romain, David Auber, Giuseppe Santucci, et al.. (2023). State of the Art of Visual Analytics for eXplainable Deep Learning. Computer Graphics Forum. 42(1). 319–355. 36 indexed citations
5.
Capobianco, Roberto, et al.. (2023). Grounding LTLf Specifications in Image Sequences. IRIS Research product catalog (Sapienza University of Rome). 668–678. 3 indexed citations
6.
Ragno, Rino, et al.. (2023). Explainable AI in drug discovery: self-interpretable graph neural network for molecular property prediction using concept whitening. Machine Learning. 113(4). 2013–2044. 7 indexed citations
7.
Capobianco, Roberto, et al.. (2023). Memory Replay For Continual Learning With Spiking Neural Networks. IRIS Research product catalog (Sapienza University of Rome). 1–6. 2 indexed citations
8.
Capobianco, Roberto, et al.. (2022). A self-interpretable module for deep image classification on small data. Applied Intelligence. 53(8). 9115–9147. 5 indexed citations
9.
Capobianco, Roberto, et al.. (2022). Prototype-Based Interpretable Graph Neural Networks. IEEE Transactions on Artificial Intelligence. 5(4). 1486–1495. 9 indexed citations
10.
Sabatino, Manuela, et al.. (2022). Ligand-based and structure-based studies to develop predictive models for SARS-CoV-2 main protease inhibitors through the 3d-qsar.com portal. Journal of Computer-Aided Molecular Design. 36(7). 483–505. 6 indexed citations
11.
Daftry, Shreyansh, et al.. (2021). Learning Transferable Policies for Autonomous Planetary Landing via Deep Reinforcement Learning. IRIS Research product catalog (Sapienza University of Rome). 2 indexed citations
12.
Capobianco, Roberto, et al.. (2020). Explainable Inference on Sequential Data via Memory-Tracking. IRIS Research product catalog (Sapienza University of Rome). 2006–2013. 4 indexed citations
13.
Capobianco, Roberto, et al.. (2020). LoOP: Iterative learning for optimistic planning on robots. Robotics and Autonomous Systems. 136. 103693–103693. 2 indexed citations
14.
Capobianco, Roberto, et al.. (2019). Cooperative Multi-Agent Deep Reinforcement Learning in Soccer Domains. Adaptive Agents and Multi-Agents Systems. 1865–1867. 1 indexed citations
15.
Lázaro, María T., Roberto Capobianco, & Giorgio Grisetti. (2018). Efficient Long-term Mapping in Dynamic Environments. IRIS Research product catalog (Sapienza University of Rome). 153–160. 25 indexed citations
16.
Saffiotti, Alessandro, Tijn van der Zant, Pedro U. Lima, et al.. (2017). RoCKIn - Benchmarking Through Robot Competitions. InTech eBooks. 3 indexed citations
17.
Sun, Wen, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, & Byron Boots. (2016). Learning to smooth with bidirectional predictive state inference machines. IRIS Research product catalog (Sapienza University of Rome). 706–715. 2 indexed citations
18.
Capobianco, Roberto, et al.. (2016). Learning human-robot handovers through π-STAM: Policy improvement with spatio-temporal affordance maps. IRIS Research product catalog (Sapienza University of Rome). 857–863. 4 indexed citations
19.
Capobianco, Roberto, et al.. (2014). Knowledge-Based Reasoning on Semantic Maps. CINECA IRIS Institutional Research Information System (University of Basilicata).
20.
Capobianco, Roberto. (2014). Robust and Incremental Robot Learning by Imitation.. IRIS Research product catalog (Sapienza University of Rome). 82–91. 1 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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