Ugo Pattacini

1.7k total citations
45 papers, 1.0k citations indexed

About

Ugo Pattacini is a scholar working on Control and Systems Engineering, Biomedical Engineering and Social Psychology. According to data from OpenAlex, Ugo Pattacini has authored 45 papers receiving a total of 1.0k indexed citations (citations by other indexed papers that have themselves been cited), including 29 papers in Control and Systems Engineering, 18 papers in Biomedical Engineering and 12 papers in Social Psychology. Recurrent topics in Ugo Pattacini's work include Robot Manipulation and Learning (27 papers), Robotic Locomotion and Control (13 papers) and Social Robot Interaction and HRI (10 papers). Ugo Pattacini is often cited by papers focused on Robot Manipulation and Learning (27 papers), Robotic Locomotion and Control (13 papers) and Social Robot Interaction and HRI (10 papers). Ugo Pattacini collaborates with scholars based in Italy, United Kingdom and United States. Ugo Pattacini's co-authors include Giorgio Metta, Lorenzo Natale, Francesco Nori, Vadim Tikhanoff, Giulio Sandini, Alessandro Roncone, Matej Hoffmann, Peter Ford Dominey, Ilaria Gori and Frédéric Elisei and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Robotics and IEEE Robotics and Automation Letters.

In The Last Decade

Ugo Pattacini

45 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Ugo Pattacini Italy 18 510 334 259 254 248 45 1.0k
Vadim Tikhanoff Italy 17 331 0.6× 202 0.6× 153 0.6× 268 1.1× 192 0.8× 40 856
Tetsunari Inamura Japan 18 624 1.2× 265 0.8× 210 0.8× 389 1.5× 499 2.0× 112 1.2k
Paul Fitzpatrick United States 15 655 1.3× 342 1.0× 258 1.0× 366 1.4× 469 1.9× 27 1.4k
Matej Hoffmann Czechia 17 288 0.6× 193 0.6× 270 1.0× 164 0.6× 131 0.5× 56 895
Alexander Stoytchev United States 17 444 0.9× 107 0.3× 161 0.6× 404 1.6× 255 1.0× 41 881
Aaron Edsinger United States 13 693 1.4× 210 0.6× 548 2.1× 144 0.6× 305 1.2× 21 1.2k
Tevfik Metin Sezgin Türkiye 19 307 0.6× 246 0.7× 136 0.5× 183 0.7× 429 1.7× 81 1.2k
Eris Chinellato Spain 16 258 0.5× 252 0.8× 180 0.7× 119 0.5× 103 0.4× 45 754
F. Guenter Switzerland 11 1.1k 2.2× 139 0.4× 399 1.5× 546 2.1× 386 1.6× 13 1.4k
Magda Bugajska United States 12 289 0.6× 360 1.1× 48 0.2× 311 1.2× 235 0.9× 17 806

Countries citing papers authored by Ugo Pattacini

Since Specialization
Citations

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

Fields of papers citing papers by Ugo Pattacini

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ugo Pattacini

This figure shows the co-authorship network connecting the top 25 collaborators of Ugo Pattacini. A scholar is included among the top collaborators of Ugo Pattacini 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 Ugo Pattacini. Ugo Pattacini 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.
Fantacci, Claudio, et al.. (2021). MaskUKF: An Instance Segmentation Aided Unscented Kalman Filter for 6D Object Pose and Velocity Tracking. Frontiers in Robotics and AI. 8. 594583–594583. 9 indexed citations
2.
Kim, Wansoo, Marta Lorenzini, Pietro Balatti, et al.. (2019). Adaptable Workstations for Human-Robot Collaboration: A Reconfigurable Framework for Improving Worker Ergonomics and Productivity. IEEE Robotics & Automation Magazine. 26(3). 14–26. 68 indexed citations
3.
Pattacini, Ugo, et al.. (2018). Merging Physical and Social Interaction for Effective Human-Robot Collaboration. 1–9. 7 indexed citations
4.
Pattacini, Ugo, et al.. (2018). Improving Superquadric Modeling and Grasping with Prior on Object Shapes. 20. 6875–6882. 11 indexed citations
5.
Fischer, Tobias, et al.. (2018). Transferring Visuomotor Learning from Simulation to the Real World for Robotics Manipulation Tasks. QUT ePrints (Queensland University of Technology). 6667–6674. 13 indexed citations
6.
Moulin-Frier, Clément, Tobias Fischer, Ugo Pattacini, et al.. (2017). DAC-h3: A Proactive Robot Cognitive Architecture to Acquire and Express Knowledge About the World and the Self. IEEE Transactions on Cognitive and Developmental Systems. 10(4). 1005–1022. 52 indexed citations
7.
Pattacini, Ugo, et al.. (2017). A novel pipeline for bi-manual handover task. Advanced Robotics. 31(23-24). 1267–1280. 3 indexed citations
8.
Pattacini, Ugo, et al.. (2017). A grasping approach based on superquadric models. 1579–1586. 37 indexed citations
9.
Roncone, Alessandro, Matej Hoffmann, Ugo Pattacini, Luciano Fadiga, & Giorgio Metta. (2016). Peripersonal Space and Margin of Safety around the Body: Learning Visuo-Tactile Associations in a Humanoid Robot with Artificial Skin. PLoS ONE. 11(10). e0163713–e0163713. 39 indexed citations
10.
Jamali, Nawid, et al.. (2016). A novel Bayesian filtering approach to tactile object recognition. Florence Research (University of Florence). 1989. 256–263. 6 indexed citations
11.
12.
Roncone, Alessandro, Matej Hoffmann, Ugo Pattacini, & Giorgio Metta. (2015). Learning peripersonal space representation through artificial skin for avoidance and reaching with whole body surface. 76. 3366–3373. 11 indexed citations
13.
Lallée, Stéphane, et al.. (2014). EFAA. Repositori digital de la UPF (Universitat Pompeu Fabra). 105–105. 2 indexed citations
14.
Fanello, Sean, Cem Keskin, Pushmeet Kohli, et al.. (2014). Filter Forests for Learning Data-Dependent Convolutional Kernels. 1709–1716. 31 indexed citations
15.
Gori, Ilaria, Ugo Pattacini, Vadim Tikhanoff, & Giorgio Metta. (2014). Three-finger precision grasp on incomplete 3D point clouds. 5366–5373. 22 indexed citations
16.
Lallée, Stéphane, Katharina Hamann, Felix Warneken, et al.. (2013). Cooperative human robot interaction systems: IV. Communication of shared plans with Naïve humans using gaze and speech. INFM-OAR (INFN Catania). 129–136. 27 indexed citations
17.
Pattacini, Ugo, et al.. (2012). I Reach Faster When I See You Look: Gaze Effects in Human–Human and Human–Robot Face-to-Face Cooperation. Frontiers in Neurorobotics. 6. 3–3. 99 indexed citations
18.
Gori, Ilaria, Ugo Pattacini, Francesco Nori, Giorgio Metta, & Giulio Sandini. (2012). DForC: A real-time method for reaching, tracking and obstacle avoidance in humanoid robots. CINECA IRIS Institutial Research Information System (University of Genoa). 253. 544–551. 7 indexed citations
19.
Lallée, Stéphane, Dimitri Ognibene, Eris Chinellato, et al.. (2012). The Coordinating Role of Language in Real-Time Multimodal Learning of Cooperative Tasks. BOA (University of Milano-Bicocca). 5(1). 3–17. 36 indexed citations
20.
Ciliberto, Carlo, Ugo Pattacini, Lorenzo Natale, Francesco Nori, & Giorgio Metta. (2011). Reexamining Lucas-Kanade method for real-time independent motion detection: Application to the iCub humanoid robot. 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems. 7. 4154–4160. 17 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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