Title :
Ultra-fast multimodal and online transfer learning on humanoid robots
Author :
Kimura, Daisuke ; Nishimura, Ryota ; Oguro, A. ; Hasegawa, Osamu
Author_Institution :
Interdiscipl. Grad. Sch. of Sci. & Eng., Tokyo Inst. of Technol., Yokohama, Japan
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.
Keywords :
humanoid robots; intelligent robots; learning (artificial intelligence); learning systems; STAR-SOINN; attribute information; autonomous mental development system; electronic data; humanoid robot; intelligent robot; online transfer learning; recognition accuracy; ultrafast multimodal learning; unknown object attribute estimation; Accuracy; Estimation; Feature extraction; Intelligent robots; Learning systems; Robot sensing systems; Multimodal; Online; SOINN; Transfer learning;
Conference_Titel :
Human-Robot Interaction (HRI), 2013 8th ACM/IEEE International Conference on
Conference_Location :
Tokyo
Print_ISBN :
978-1-4673-3099-2
Electronic_ISBN :
2167-2121
DOI :
10.1109/HRI.2013.6483553