From passive to interactive object learning and recognition through self-identification on a humanoid robot
Identifieur interne : 000708 ( Main/Exploration ); précédent : 000707; suivant : 000709From passive to interactive object learning and recognition through self-identification on a humanoid robot
Auteurs : Natalia Lyubova [France] ; Serena Ivaldi [France] ; David Filliat [France]Source :
- Autonomous Robots [ 0929-5593 ] ; 2015.
Abstract
Service robots, working in evolving human environments , need the ability to continuously learn to recognize new objects. Ideally, they should act as humans do, by observing their environment and interacting with objects, without specific supervision. Taking inspiration from infant development, we propose a developmental approach that enables a robot to progressively learn objects appearances in a social environment: first, only through observation, then through active object manipulation. We focus on incremen-tal, continuous, and unsupervised learning that does not require prior knowledge about the environment or the robot. In the first phase, we analyse the visual space and detect proto-objects as units of attention that are learned and recognized as possible physical entities. The appearance of each entity is represented as a multi-view model based on complementary visual features. In the second phase, entities are classified into three categories: parts of the body of the robot, parts of a human partner, and manipulable objects. The cate-gorization approach is based on mutual information between the visual and proprioceptive data, and on motion behaviour of entities. The ability to categorize entities is then used during interactive object exploration to improve the previously acquired objects models. The proposed system is implemented and evaluated with an iCub and a Meka robot learning 20 objects. The system is able to recognize objects with 88.5% success and create coherent representation models that are further improved by interactive learning.
Url:
DOI: 10.1007/s10514-015-9445-0
Affiliations:
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Paris-Saclay, ENSTA ParisTech</orgName><desc><address><addrLine>828, boulevard des Maréchaux 91762 Palaiseau Cedex</addrLine><country key="FR"></country></address><ref type="url">http://www.ensta-paristech.fr/fr</ref></desc></org></tutelle></tutelles></hal:affiliation><country>France</country></affiliation></author></analytic><idno type="DOI">10.1007/s10514-015-9445-0</idno><series><title level="j">Autonomous Robots</title><idno type="ISSN">0929-5593</idno><imprint><date type="datePub">2015</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">Service robots, working in evolving human environments , need the ability to continuously learn to recognize new objects. Ideally, they should act as humans do, by observing their environment and interacting with objects, without specific supervision. Taking inspiration from infant development, we propose a developmental approach that enables a robot to progressively learn objects appearances in a social environment: first, only through observation, then through active object manipulation. We focus on incremen-tal, continuous, and unsupervised learning that does not require prior knowledge about the environment or the robot. In the first phase, we analyse the visual space and detect proto-objects as units of attention that are learned and recognized as possible physical entities. The appearance of each entity is represented as a multi-view model based on complementary visual features. In the second phase, entities are classified into three categories: parts of the body of the robot, parts of a human partner, and manipulable objects. The cate-gorization approach is based on mutual information between the visual and proprioceptive data, and on motion behaviour of entities. The ability to categorize entities is then used during interactive object exploration to improve the previously acquired objects models. The proposed system is implemented and evaluated with an iCub and a Meka robot learning 20 objects. The system is able to recognize objects with 88.5% success and create coherent representation models that are further improved by interactive learning.</div></front></TEI><affiliations><list><country><li>France</li></country><region><li>Grand Est</li><li>Lorraine (région)</li></region><settlement><li>Metz</li><li>Nancy</li></settlement><orgName><li>Université de Lorraine</li></orgName></list><tree><country name="France"><noRegion><name sortKey="Lyubova, Natalia" sort="Lyubova, Natalia" uniqKey="Lyubova N" first="Natalia" last="Lyubova">Natalia Lyubova</name></noRegion><name sortKey="Filliat, David" sort="Filliat, David" uniqKey="Filliat D" first="David" last="Filliat">David Filliat</name><name sortKey="Ivaldi, Serena" sort="Ivaldi, Serena" uniqKey="Ivaldi S" first="Serena" last="Ivaldi">Serena Ivaldi</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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