Methodology for learning multimodal instructions in the context of human-robot interaction using machine learning

dc.contributor.authorRodríguez, Saithspa
dc.contributor.authorQuintero, Carlos A.spa
dc.contributor.authorPérez, Andrea K.spa
dc.contributor.authorRojas, Eyberthspa
dc.contributor.authorPeña, Oswaldospa
dc.contributor.authorDe La Rosa, Fernandospa
dc.coverage.campusCRAI-USTA Bogotáspa
dc.date.accessioned2019-07-08T14:20:42Zspa
dc.date.available2019-07-08T14:20:42Zspa
dc.date.issued2018-02-17spa
dc.description.abstractThis work shows the design, implementation and evaluation of a human-robot interaction system where a robot is capable of learning multimodal instructions through gestures and voice issued by a human user. The learning procedure can be performed in two ways: an instruction learning phase, where the human aims at teaching one instruction to the robot by performing several repetitions and an instruction receiving phase where the robot reacts to the instructions given by the human and possibly asks for feedback from the user to strengthen the instruction’s model.spa
dc.description.domainhttp://unidadinvestigacion.usta.edu.cospa
dc.format.mimetypeapplication/pdfspa
dc.identifier.citationRodríguez, S., Quintero, C. A., Pérez, A. K., Rojas, E., Peña, O., & De La Rosa, F. (2018). Methodology for learning multimodal instructions in the context of human-robot interaction using machine learning. Bogotá: doi:10.1007/978-3-319-76261-6_4spa
dc.identifier.doihttps://doi.org/10.1007/978-3-319-76261-6_4spa
dc.identifier.urihttp://hdl.handle.net/11634/17504
dc.relation.referencesTee, K.P., Yan, R., Chu, Y.: Gesture-based attention direction for a telepresence robot: design and experimental study. In: Proceedings 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE (2014)spa
dc.relation.referencesBhattacharya, S., Czejdo, B., Perez, N.: Gesture classification with machine learning using kinect sensor data. In: Third International Conference on Emerging Applications of Information Technology (EAIT) (2012)spa
dc.relation.referencesHuang, J., Lee, C.W., Ma, J.: Gesture recognition and classification using the microsoft kinect. In: 5th International Conference on Automation, Robotics and Applications, ICARA 2011 (2009)spa
dc.relation.referencesZhang, H., Du, W.X., Li, H.: Kinect Gesture Recognition for Interactive System (2009)spa
dc.relation.referencesDhanalakshmi, P., Palanivel, S., Ramalingam, V.: Classification of audio signals using SVM and RBFNN. Expert Syst. Appl. 36(3), 6069–6075 (2009)spa
dc.relation.referencesSarchaga, G., Sartori, V., Vignoli, L.: Identificacin automtica de resumen en canciones (2006)spa
dc.relation.referencesSuresh, V., Mohan, C.K., Swamy, R.K., Yegnanarayana, B.: Content-based video classification using support vector machines. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 726–731. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-30499-9 111spa
dc.relation.referencesSubashini, K., Palanivel, S., Ramaligam, V.: Audio-video based segmentation and classification using SVM (2012)spa
dc.relation.referencesKittler, J., Hatef, M., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)spa
dc.relation.referencesBabushkin, V., Oudah, M., Chenlinangjia, T., Alshaer, A., Crandall, J.: Online learning in repeated human-robot interactions. In: Artificial Intelligence for Human-robot Interaction: Papers from the AAAI Fall Symposium, pp. 42–44. AAAI Press (2014spa
dc.relation.referencesMolau, S., Pitz, M., Schluter, R., Ney, H.: Computing mel-frequency cepstral coefficients on the power spectrum. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 73–76. IEEE Press (2001)spa
dc.relation.referencesRodriguez, S., P´erez, K., Quintero, C., L´opez, J., Rojas, E., Calder´on, J.: Identification of multimodal human-robot interaction using combined kernels. In: Sn´aˇsel, V., Abraham, A., Kr¨omer, P., Pant, M., Muda, A.K. (eds.) Innovations in Bio-Inspired Computing and Applications. AISC, vol. 424, pp. 263–273. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-28031-8 23spa
dc.relation.referencesHsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Technical report, Department of Computer Science, National Taiwan University (2003)spa
dc.rightsAtribución-NoComercial-CompartirIgual 2.5 Colombia*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/co/*
dc.subject.keywordHuman-robot interaction systemspa
dc.subject.keywordLearning multimodalspa
dc.subject.keywordMachine Learningspa
dc.titleMethodology for learning multimodal instructions in the context of human-robot interaction using machine learningspa
dc.type.categoryGeneración de Nuevo Conocimiento: Artículos publicados en revistas especializadas - Electrónicosspa

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