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31 results on '"Cauli, Nino"'

2. Human-Centric Artificial Intelligence Architecture for Industry 5.0 Applications

Author

Rožanec, Jože M., Novalija, Inna, Zajec, Patrik, Kenda, Klemen, Tavakoli, Hooman, Suh, Sungho, Veliou, Entso, Papamartzivanos, Dimitrios, Giannetsos, Thanassis, Menesidou, Sofia Anna, Alonso, Ruben, Cauli, Nino, Meloni, Antonello, Recupero, Diego Reforgiato, Kyriazis, Dimosthenis, Sofianidis, Georgios, Theodoropoulos, Spyros, Fortuna, Blaž, Mladenić, Dunja, and Soldatos, John

Subjects
Computer Science - Artificial Intelligence
Abstract

Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focusing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we validate it on three use cases from real-world case studies.

Published
2022

3. STARdom: an architecture for trusted and secure human-centered manufacturing systems

Author

Rožanec, Jože M., Zajec, Patrik, Kenda, Klemen, Novalija, Inna, Fortuna, Blaž, Mladenić, Dunja, Veliou, Entso, Papamartzivanos, Dimitrios, Giannetsos, Thanassis, Menesidou, Sofia Anna, Alonso, Rubén, Cauli, Nino, Recupero, Diego Reforgiato, Kyriazis, Dimosthenis, Sofianidis, Georgios, Theodoropoulos, Spyros, and Soldatos, John

Subjects
Computer Science - Artificial Intelligence, Computer Science - Software Engineering
Abstract

There is a lack of a single architecture specification that addresses the needs of trusted and secure Artificial Intelligence systems with humans in the loop, such as human-centered manufacturing systems at the core of the evolution towards Industry 5.0. To realize this, we propose an architecture that integrates forecasts, Explainable Artificial Intelligence, supports collecting users' feedback, and uses Active Learning and Simulated Reality to enhance forecasts and provide decision-making recommendations. The architecture security is addressed as a general concern. We align the proposed architecture with the Big Data Value Association Reference Architecture Model. We tailor it for the domain of demand forecasting and validate it on a real-world case study.

Published
2021

4. Sensor Datasets for Human Daily Safety and Well-Being

Author

Cauli, Nino, Massa, Silvia M., Recupero, Diego Reforgiato, Riboni, Daniele, Kacprzyk, Janusz, Series Editor, Barsocchi, Paolo, editor, Parvathaneni, Naga Srinivasu, editor, Garg, Amik, editor, Bhoi, Akash Kumar, editor, and Palumbo, Filippo, editor

Published
2023
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5. Cleaning tasks knowledge transfer between heterogeneous robots: a deep learning approach

Author

Kim, Jaeseok, Cauli, Nino, Vicente, Pedro, Damas, Bruno, Bernardino, Alexandre, Santos-Victor, José, and Cavallo, Filippo

Subjects
Computer Science - Robotics, Computer Science - Machine Learning
Abstract

Nowadays, autonomous service robots are becoming an important topic in robotic research. Differently from typical industrial scenarios, with highly controlled environments, service robots must show an additional robustness to task perturbations and changes in the characteristics of their sensory feedback. In this paper, a robot is taught to perform two different cleaning tasks over a table, using a learning from demonstration paradigm. However, differently from other approaches, a convolutional neural network is used to generalize the demonstrations to different, not yet seen dirt or stain patterns on the same table using only visual feedback, and to perform cleaning movements accordingly. Robustness to robot posture and illumination changes is achieved using data augmentation techniques and camera images transformation. This robustness allows the transfer of knowledge regarding execution of cleaning tasks between heterogeneous robots operating in different environmental settings. To demonstrate the viability of the proposed approach, a network trained in Lisbon to perform cleaning tasks, using the iCub robot, is successfully employed by the DoRo robot in Peccioli, Italy., Comment: This paper was published in the Journal of Intelligent & Robotic Systems on August 29th, 2019. Link: https://doi.org/10.1007/s10846-019-01072-4

Published
2019
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6. Fooling a Face Recognition System with a Marker-Free Label-Consistent Backdoor Attack

Author

Cauli, Nino, Ortis, Alessandro, Battiato, Sebastiano, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Sclaroff, Stan, editor, Distante, Cosimo, editor, Leo, Marco, editor, Farinella, Giovanni M., editor, and Tombari, Federico, editor

Published
2022
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8. Human-centric artificial intelligence architecture for industry 5.0 applications.

Author

Rožanec, Jože M., Novalija, Inna, Zajec, Patrik, Kenda, Klemen, Tavakoli Ghinani, Hooman, Suh, Sungho, Veliou, Entso, Papamartzivanos, Dimitrios, Giannetsos, Thanassis, Menesidou, Sofia Anna, Alonso, Ruben, Cauli, Nino, Meloni, Antonello, Recupero, Diego Reforgiato, Kyriazis, Dimosthenis, Sofianidis, Georgios, Theodoropoulos, Spyros, Fortuna, Blaž, Mladenić, Dunja, and Soldatos, John

Subjects
ARTIFICIAL intelligence, ACTIVE learning, TRUST, BIG data, MANUFACTURING industries
Abstract

Human-centricity is the core value behind the evolution of manufacturing towards Industry 5.0. Nevertheless, there is a lack of architecture that considers safety, trustworthiness, and human-centricity at its core. Therefore, we propose an architecture that integrates Artificial Intelligence (Active Learning, Forecasting, Explainable Artificial Intelligence), simulated reality, decision-making, and users' feedback, focussing on synergies between humans and machines. Furthermore, we align the proposed architecture with the Big Data Value Association Reference Architecture Model. Finally, we validate it on three use cases from real-world case studies. [ABSTRACT FROM AUTHOR]

Published
2023
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10. STARdom: An Architecture for Trusted and Secure Human-Centered Manufacturing Systems

Author

Rožanec, Jože M., primary, Zajec, Patrik, additional, Kenda, Klemen, additional, Novalija, Inna, additional, Fortuna, Blaž, additional, Mladenić, Dunja, additional, Veliou, Entso, additional, Papamartzivanos, Dimitrios, additional, Giannetsos, Thanassis, additional, Menesidou, Sofia Anna, additional, Alonso, Rubén, additional, Cauli, Nino, additional, Recupero, Diego Reforgiato, additional, Kyriazis, Dimosthenis, additional, Sofianidis, Georgios, additional, Theodoropoulos, Spyros, additional, and Soldatos, John, additional

Published
2021
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14. Human-centric artificial intelligence architecture for industry 5.0 applications

Author

Rožanec, Jože M., primary, Novalija, Inna, additional, Zajec, Patrik, additional, Kenda, Klemen, additional, Tavakoli Ghinani, Hooman, additional, Suh, Sungho, additional, Veliou, Entso, additional, Papamartzivanos, Dimitrios, additional, Giannetsos, Thanassis, additional, Menesidou, Sofia Anna, additional, Alonso, Ruben, additional, Cauli, Nino, additional, Meloni, Antonello, additional, Recupero, Diego Reforgiato, additional, Kyriazis, Dimosthenis, additional, Sofianidis, Georgios, additional, Theodoropoulos, Spyros, additional, Fortuna, Blaž, additional, Mladenić, Dunja, additional, and Soldatos, John, additional

Published
2022
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17. Learning Deep Features for Robotic Inference From Physical Interactions

Author

Dehban, Atabak, Zhang, Shanghang, Cauli, Nino, Jamone, Lorenzo, and Santos, Jose

Abstract

In order to effectively handle multiple tasks that are not predefined, a robotic agent needs to automatically map its high-dimensional sensory inputs into useful features. As a solution, feature learning has empirically shown substantial improvements in obtaining representations that are generalizable to different tasks, compared to feature engineering approaches, but it requires a large amount of data and computational capacity. These challenges are specifically relevant in robotics due to the low signal-to-noise ratios inherent to robotic data, and to the cost typically associated with collecting this type of input. In this article, we propose a deep probabilistic method based on convolutional variational autoencoders (CVAEs) to learn visual features suitable for interaction and recognition tasks. We run our experiments on a self-supervised robotic sensorimotor data set. Our data were acquired with the iCub humanoid and are based on a standard object collection, thus being readily extensible. We evaluated the learned features in terms of usability for: 1) object recognition; 2) capturing the statistics of the effects; and 3) planning. In addition, where applicable, we compared the performance of the proposed architecture with other state-of-the-art models. These experiments demonstrate that our model is capable of capturing the functional statistics of action and perception (i.e., images) which performs better than existing baselines, without requiring millions of samples or any hand-engineered features.

Published
2023
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20. Cleaning Tasks Knowledge Transfer Between Heterogeneous Robots: a Deep Learning Approach.

Author

Kim, Jaeseok, Cauli, Nino, Vicente, Pedro, Damas, Bruno, Bernardino, Alexandre, Santos-Victor, José, and Cavallo, Filippo

Abstract

Nowadays, autonomous service robots are becoming an important topic in robotic research. Differently from typical industrial scenarios, with highly controlled environments, service robots must show an additional robustness to task perturbations and changes in the characteristics of their sensory feedback. In this paper, a robot is taught to perform two different cleaning tasks over a table, using a learning from demonstration paradigm. However, differently from other approaches, a convolutional neural network is used to generalize the demonstrations to different, not yet seen dirt or stain patterns on the same table using only visual feedback, and to perform cleaning movements accordingly. Robustness to robot posture and illumination changes is achieved using data augmentation techniques and camera images transformation. This robustness allows the transfer of knowledge regarding execution of cleaning tasks between heterogeneous robots operating in different environmental settings. To demonstrate the viability of the proposed approach, a network trained in Lisbon to perform cleaning tasks, using the iCub robot, is successfully employed by the DoRo robot in Peccioli, Italy. [ABSTRACT FROM AUTHOR]

Published
2020
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23. Connecting Artificial Brains to Robots in a Comprehensive Simulation Framework: The Neurorobotics Platform

Author

Falotico, Egidio, primary, Vannucci, Lorenzo, additional, Ambrosano, Alessandro, additional, Albanese, Ugo, additional, Ulbrich, Stefan, additional, Vasquez Tieck, Juan Camilo, additional, Hinkel, Georg, additional, Kaiser, Jacques, additional, Peric, Igor, additional, Denninger, Oliver, additional, Cauli, Nino, additional, Kirtay, Murat, additional, Roennau, Arne, additional, Klinker, Gudrun, additional, Von Arnim, Axel, additional, Guyot, Luc, additional, Peppicelli, Daniel, additional, Martínez-Cañada, Pablo, additional, Ros, Eduardo, additional, Maier, Patrick, additional, Weber, Sandro, additional, Huber, Manuel, additional, Plecher, David, additional, Röhrbein, Florian, additional, Deser, Stefan, additional, Roitberg, Alina, additional, van der Smagt, Patrick, additional, Dillman, Rüdiger, additional, Levi, Paul, additional, Laschi, Cecilia, additional, Knoll, Alois C., additional, and Gewaltig, Marc-Oliver, additional

Published
2017
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24. A Framework for Coupled Simulations of Robots and Spiking Neuronal Networks

Author

Hinkel, Georg, primary, Groenda, Henning, additional, Krach, Sebastian, additional, Vannucci, Lorenzo, additional, Denninger, Oliver, additional, Cauli, Nino, additional, Ulbrich, Stefan, additional, Roennau, Arne, additional, Falotico, Egidio, additional, Gewaltig, Marc-Oliver, additional, Knoll, Alois, additional, Dillmann, Rüdiger, additional, Laschi, Cecilia, additional, and Reussner, Ralf, additional

Published
2016
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27. A visual tracking model implemented on the iCub robot as a use case for a novel neurorobotic toolkit integrating brain and physics simulation

Author

Vannucci, Lorenzo, primary, Ambrosano, Alessandro, additional, Cauli, Nino, additional, Albanese, Ugo, additional, Falotico, Egidio, additional, Ulbrich, Stefan, additional, Pfotzer, Lars, additional, Hinkel, Georg, additional, Denninger, Oliver, additional, Peppicelli, Daniel, additional, Guyot, Luc, additional, Von Arnim, Axel, additional, Deser, Stefan, additional, Maier, Patrick, additional, Dillman, Rudiger, additional, Klinker, Gundrun, additional, Levi, Paul, additional, Knoll, Alois, additional, Gewaltig, Marc-Oliver, additional, and Laschi, Cecilia, additional

Published
2015
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