Ultra-fast multimodal and online transfer learning on humanoid robots

Authors:
Daiki Kimura;Ryutaro Nishimura;Akihiro Oguro;Osamu Hasegawa
Affiliations:
Tokyo Institute of Technology, Yokohama, Japan;Tokyo Institute of Technology, Yokohama, Japan;Tokyo Institute of Technology, Yokohama, Japan;Tokyo Institute of Technology, Yokohama, Japan
Venue:
Proceedings of the 8th ACM/IEEE international conference on Human-robot interaction
Year:
2013

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Abstract

To build an intelligent robot, we must develop an autonomous mental development system that incrementally and speedily learns from humans, its environments, and electronic data. This paper presents an ultra-fast, multimodal, and online incremental transfer learning method using the STAR-SOINN. We conducted two experiments to evaluate our method. The results suggest that recognition accuracy is higher than the system that simply adds modalities. The proposed method can work very quickly (approximately 1.5[s] to learn one object, and 30[ms] for a single estimation). We implemented this method on an actual robot that could estimate attributes of "unknown" objects by transferring attribute information of known objects. We believe this method can become a base technology for future robots.