Multi-modal integration of dynamic audiovisual patterns for an interactive reinforcement learning scenario

IEEE International Conference on Intelligent Robots and Systems

Volumn 2016-November, Issue , 2016, Pages 759-766

  Cruz, Francisco ^a   Parisi, German I ^a   Twiefel, Johannes ^a   Wermter, Stefan ^a  

^a UNIVERSITY OF HAMBURG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

INTEGRATION; INTELLIGENT ROBOTS; ROBOTS; SCAFFOLDS;

DOMESTIC ENVIRONMENTS; INTERACTIVE REINFORCEMENT LEARNING; LEARNING CYCLE; LEARNING PROCESS; MULTI-MODAL INFORMATION; MULTIMODAL INTEGRATION; MULTIPLE SENSORS; NETWORK-BASED APPROACH;

REINFORCEMENT LEARNING;

EID: 85006421852     PISSN: 21530858     EISSN: 21530866     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2016.7759137     Document Type: Conference Paper

References (25)

1. F. Cruz, J. Twiefel, S. Magg, C. Weber, and S. Wermter. Interactive reinforcement learning through speech guidance in a domestic scenario. In Proceedings of International Joint Conference on Neural Networks IJCNN, pp. 1341-1348, Killarney, Ireland, 2015.
2. E. Ugur, Y. Nagai, H. Celikkanat, and E. Oztop. Parental scaffolding as a bootstrapping mechanism for learning grasp affordances and imitation skills. In Robotica, vol. 33, pp. 1163-1180, 2015.
3. J. Bauer, J. Dávila-Chacón, and S. Wermter. Modeling development of natural multi-sensory integration using neural self-organisation and probabilistic population codes. In Connection Science, vol. 27, no. 4, pp. 358-376, 2015.
4. F. Cruz, S. Magg, C. Weber, and S. Wermter. Training agents with interactive reinforcement learning and contextual affordances. Accepted to IEEE Transactions on Autonomous Mental Development, 2016, doi: 10.1109/TCDS.2016.2543839.
5. R. S. Sutton and A. G. Barto. Reinforcement learning: An introduction, A Bradford Book, 1998.
6. A. L. Thomaz and C. Breazeal. Asymmetric interpretations of positive and negative human feedback for a social learning agent. In Proceedings of IEEE International Symposium on Robot and Human Interactive Communication RO-MAN, pp. 720-725, 2007.
7. S. Griffith, K. Subramanian, J. Scholz, C. Isbell, and A. Thomaz. Policy shaping: Integrating human feedback with reinforcement learning. In Advances in Neural Information Processing Systems, pp. 2625-2633, 2013.
8. H. B. Suay and S. Chernova. Effect of human guidance and state space size on interactive reinforcement learning. In Proceedings of IEEE International Symposium on Robot and Human Interactive Communication RO-MAN, pp. 1-6, 2011.
9. W. B. Knox, P. Stone, and C. Breazeal. Training a robot via human feedback: A case study. In Proceedings of the International Conference on Social Robotics, pp. 460-470, 2013.
10. M. Andre, V. G. Popescu, A. Shaikh, A. Medl, I. Marsic, C. Kulikowski, and J. Flanagan. Integration of speech and gesture for multimodal Human-Computer interaction. In International Conference on Cooperative Multimodal Communication, pp. 28-30, 1998.
11. S. Wermter, M. Elshaw, C. Weber, C. Panchev, and H. Erwin. Towards integrating learning by demonstration and learning by instruction in a multimodal robot. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, Workshop on Robot Learning by Demonstration, pp. 72-79. 2003.
12. L. Lacheze, Y. Guo, R. Benosman, B. Gas, and C. Couverture. Audio/video fusion for objects recognition. In Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems IROS, pp. 652-657, 2009.
13. J. Sanchez-Riera, X. Alameda-Pineda, J. Wienke, A. Deleforge, S. Arias, J. Cech, S. Wrede, and R. Horaud. Online multimodal speaker detection for humanoid robots. In Proceedings of IEEE-RAS International Conference on Humanoid Robots, pp. 126-133, 2012.
14. D. Kimura and O. Hasegawa. Estimating multimodal attributes for unknown objects. In Proceedings of the International Joint Conference on Neural Networks IJCNN, pp. 1-8, 2015.
15. Y. Ozasa, Y. Ariki, M. Nakano, and N. Iwahashi. Disambiguation in unknown object detection by integrating image and speech recognition confidences. In Computer Vision - ACCV 2012, pp. 85-96, 2012.
16. M. Lapeyre, P. Rouanet, J. Grizou, S. N'Guyen, A. Le Falher, F. Depraetre, and P.-Y. Oudeyer. Poppy: Open source 3D printed robot for experiments in developmental robotics. In Proceedings of the Joint IEEE International Conferences on Development and Learning and Epigenetic Robotics ICDL-EpiRob, pp. 173-174, 2014.
17. J. Schalkwyk, D. Beeferman, F. Beaufays, B. Byrne, C. Chelba, M. Cohen, M. Kamvar, and B. Strope. Your word is my command: Google search by voice: A case study. In Advances in Speech Recognition, Springer US, pp. 61-90, 2010.
18. J. Twiefel, T. Baumann, S. Heinrich, and S. Wermter. Improving domain-independent cloud-based speech recognition with domaindependent phonetic post-processing. In Proceedings of the 28th AAAI Conference on Artificial Intelligence AAAI, pp. 1529-1535, 2014.
19. V. I. Levenshtein. Binary codes capable of correcting deletions, insertions and reversals. In Soviet Physics Doklady, Vol. 10, pp. 707, 1966.
20. G. I. Parisi, D. Jirak, and S. Wermter. HandSOM - Neural clustering of hand motion for gesture recognition in real time. In Proceedings of IEEE International Symposium on Robot and Human Interactive Communication RO-MAN, pp. 981-986, 2014.
21. G. I. Parisi, C. Weber, and S. Wermter. Self-organizing neural integration of pose-motion features for human action recognition. In Frontiers in Neurorobotics 9:3, 2015.
22. T. Martinetz. Competitive Hebbian learning rule forms perfectly topology preserving maps. In Proceedings of the International Conference on Artificial Neural Networks ICANN, pp. 427-434, 1993.
23. S. Marsland, J. Shapiro, and U. Nehmzow. A self-organising network that grows when required. In Neural Networks, Vol. 15, pp. 1041-1058, 2002.
24. G. Georgiou. Exact interpolation and learning in quadratic neural networks. In Proceedings of the International Joint Conference on Neural Networks IJCNN, pp. 230-234, 2006.
25. G. Georgiou and K. Voigt. Self-organizing maps with a single neuron. In Proceedings of the International Joint Conference on Neural Networks IJCNN, pp. 1-6, 2013.