Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/25845
Registo completo
Campo DC | Valor | Idioma |
---|---|---|
dc.contributor.author | Baleia, J. | - |
dc.contributor.author | Santana, P. | - |
dc.contributor.author | Barata, J. | - |
dc.contributor.editor | Nuno Lau | - |
dc.contributor.editor | António Paulo Moreira | - |
dc.contributor.editor | Rodrigo Ventura | - |
dc.contributor.editor | Brígida Mónica Faria | - |
dc.contributor.editor | Sociedade Portuguesa de Robotica | - |
dc.contributor.editor | IEEE Robotics and Automation Society | - |
dc.contributor.editor | Institute of Electrical and Electronics Engineers. Portugal Section | - |
dc.date.accessioned | 2022-07-15T14:23:13Z | - |
dc.date.available | 2022-07-15T14:23:13Z | - |
dc.date.issued | 2014 | - |
dc.identifier.citation | Baleia, J., Santana, P., & Barata, J. (2014). Self-supervised learning of depth-based navigation affordances from haptic cues. Em Nuno Lau; António Paulo Moreira; Rodrigo Ventura; Brígida Mónica Faria; Sociedade Portuguesa de Robotica,; IEEE Robotics and Automation Society,; Institute of Electrical and Electronics Engineers. Portugal Section (Eds.), Proceedings of IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC).IEEE. http://hdl.handle.net/10071/25845 | - |
dc.identifier.isbn | 978-1-4799-4254-1 | - |
dc.identifier.uri | http://hdl.handle.net/10071/25845 | - |
dc.description.abstract | This paper presents a ground vehicle capable of exploiting haptic cues to learn navigation affordances from depth cues. A simple pan-tilt telescopic antenna and a Kinect sensor, both fitted to the robot’s body frame, provide the required haptic and depth sensory feedback, respectively. With the antenna, the robot determines whether an object is traversable by the robot. Then, the interaction outcome is associated to the object’s depth-based descriptor. Later on, the robot to predict if a newly observed object is traversable just by inspecting its depth-based appearance uses this acquired knowledge. A set of field trials show the ability of the to robot progressively learn which elements of the environment are traversable. | eng |
dc.language.iso | eng | - |
dc.publisher | IEEE | - |
dc.relation | LISBOA-01-0202-FEDER-024961 | - |
dc.relation.ispartof | Proceedings of IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC) | - |
dc.rights | openAccess | - |
dc.subject | Autonomous robots | eng |
dc.subject | Self-supervised learning | eng |
dc.subject | Affordances | eng |
dc.subject | Terrain assessment | eng |
dc.subject | Depth sensing | eng |
dc.subject | Robotic antenna | eng |
dc.title | Self-supervised learning of depth-based navigation affordances from haptic cues | eng |
dc.type | conferenceObject | - |
dc.event.type | Conferência | pt |
dc.event.location | Espinho | eng |
dc.event.date | 2014 | - |
dc.peerreviewed | yes | - |
dc.date.updated | 2022-07-05T13:34:15Z | - |
dc.description.version | info:eu-repo/semantics/acceptedVersion | - |
dc.subject.fos | Domínio/Área Científica::Ciências Naturais::Ciências Físicas | por |
iscte.identifier.ciencia | https://ciencia.iscte-iul.pt/id/ci-pub-18837 | - |
iscte.alternateIdentifiers.wos | WOS:000343584000025 | - |
iscte.alternateIdentifiers.scopus | 2-s2.0-84904976539 | - |
Aparece nas coleções: | ISTAR-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
---|---|---|---|
conferenceobject_18837.pdf | 4 MB | Adobe PDF | Ver/Abrir |
Todos os registos no repositório estão protegidos por leis de copyright, com todos os direitos reservados.