RoboExNovo SIGNED
Robots learning about objects from externalized knowledge sources
Total Cost €
0EC-Contrib. €
0Partnership
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0RoboExNovo project word cloud
Explore the words cloud of the RoboExNovo project. It provides you a very rough idea of what is the project "RoboExNovo" about.
Project "RoboExNovo" data sheet
The following table provides information about the project.
| Coordinator |
FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA
Organization address contact info |
| Coordinator Country | Italy [IT] |
| Total cost | 1˙496˙277 € |
| EC max contribution | 1˙496˙277 € (100%) |
| Programme |
1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC)) |
| Code Call | ERC-2014-STG |
| Funding Scheme | ERC-STG |
| Starting year | 2015 |
| Duration (year-month-day) | from 2015-06-01 to 2021-05-31 |
Partnership
Take a look of project's partnership.
| # | ||||
|---|---|---|---|---|
| 1 | FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA | IT (GENOVA) | coordinator | 1˙084˙873.00 |
| 2 | UNIVERSITA DEGLI STUDI DI ROMA LA SAPIENZA | IT (ROMA) | participant | 411˙404.00 |
Map
Project objective
While today’s robots are able to perform sophisticated tasks, they can only act on objects they have been trained to recognize. This is a severe limitation: any robot will inevitably face novel situations in unconstrained settings, and thus will always have knowledge gaps. This calls for robots able to learn continuously about objects by themselves. The learning paradigm of state-of-the-art robots is the sensorimotor toil, i.e. the process of acquiring knowledge by generalization over observed stimuli. This is in line with cognitive theories that claim that cognition is embodied and situated, so that all knowledge acquired by a robot is specific to its sensorimotor capabilities and to the situation in which it has been acquired. Still, humans are also capable of learning from externalized sources – like books, illustrations, etc – containing knowledge that is necessarily unembodied and unsituated. To overcome this gap, RoboExNovo proposes a paradigm shift. I will develop a new generation of robots able to acquire perceptual and semantic knowledge about object from externalized, unembodied resources, to be used in situated settings. As the largest existing body of externalized knowledge, I will consider the Web as the source from which to learn from. To achieve this, I propose to build a translation framework between the representations used by robots in their situated experience and those used on the Web, based on relational structures establishing links between related percepts and between percepts and the semantics they support. My leading expertise in machine learning applied to multi modal data and robot vision puts me in a strong position to realize this project. By enabling robots to use knowledge resources on the Web that were not explicitly designed to be accessed for this purpose, RoboExNovo will pave the way for ground-breaking technological advances in home and service robotics, driver assistant systems, and in general any Web-connected situated device.
Publications
| year | authors and title | journal | last update |
|---|---|---|---|
| 2017 |
Fabio Maria Carlucci, Lorenzo Porzi, Barbara Caputo, Elisa Ricci, Samuel Rota Bulò Just DIAL: Domain alignment layers for unsupervised domain adaptation published pages: 357-369, ISSN: , DOI: 10.1007/978-3-319-68560-1_32 |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2019-10-29 |
| 2017 |
Barbara Caputo, Claudio Cusano, Martina Lanzi, Paolo Napoletano, Raimondo Schettini On the importance of domain adaptation in texture classification published pages: 380-390, ISSN: , DOI: 10.1007/978-3-319-68560-1_34 |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | 2019-10-29 |
| 2017 |
Igor Barros Barbosa, Marco Cristani, Barbara Caputo, Aleksander Rognhaugen, Theoharis Theoharis Looking beyond appearances: Synthetic training data for deep CNNs in re-identification published pages: , ISSN: 1077-3142, DOI: 10.1016/j.cviu.2017.12.002 |
Computer Vision and Image Understanding | 2019-10-29 |
| 2017 |
Tatiana Tommasi, Novi Patricia, Barbara Caputo, Tinne Tuytelaars A deeper look at dataset bias published pages: 37-55, ISSN: , DOI: 10.1007/978-3-319-58347-1_2 |
2019-10-29 | |
| 2017 |
Antonio D’Innocente, Fabio Maria Carlucci, Mirco Colosi, Barbara Caputo Bridging between computer and robot vision through data augmentation: A case study on object recognition published pages: 384-393, ISSN: , DOI: 10.1007/978-3-319-68345-4_34 |
2019-10-29 | |
| 2016 |
Ilja Kuzborskij, Francesco Orabona, Barbara Caputo Scalable greedy algorithms for transfer learning published pages: , ISSN: 1077-3142, DOI: 10.1016/j.cviu.2016.09.003 |
Computer Vision and Image Understanding | 2019-10-29 |
| 2017 |
Massimiliano Mancini, Samuel Rota Bulo, Elisa Ricci, Barbara Caputo Learning Deep NBNN Representations for Robust Place Categorization published pages: 1794-1801, ISSN: 2377-3766, DOI: 10.1109/LRA.2017.2705282 |
IEEE Robotics and Automation Letters 2/3 | 2019-10-29 |
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The information about "ROBOEXNOVO" are provided by the European Opendata Portal: CORDIS opendata.