抄録
In this paper, we propose a novel learning method which can estimate self-location of a robot and concepts of location simultaneously. A robot performs a probabilistic self-localization from sensor data. We integrate ambiguous speech recognition results with the model for self-localization on Bayesian approach. Experimental results show that a robot can obtain words for several locations and make use of them in self-localization task. In addition, we evaluate the performance of lexical acquisition task about words for places and show its effectiveness.
