文摘
Nowadays, the research that robots learn the meaning of a word by relating speech instructions with signals of the sensorimotor system has started. Lots of pre-research on learning ‘concept’ that means the meaning of a word by robot have used the co-occurrence information of words and sensor signals directly. However, relative concepts, such as color, size or location, require comparing with other objects or concepts. The objects or concepts used for comparison are called “reference object.” In human-computer interaction, reference point may not be explicitly communicated, because human utterance is ambiguous. Therefore, estimating a true reference object from the multi-candidate is necessary to learn relative concepts. In this paper, we propose a learning method based on EM algorithm to estimate the reference point and learn relative concepts. In our experiments, a robot learned concepts expressing relative location, such as ‘in front of or ‘behind.’ The robot was given text labels expressing its locations from a human teacher in several situations. Nevertheless, the instructions didn’t include the true reference points and the coordinate system of each concept. Under these conditions, the robot could learn the concepts based on coordinate systems have the orientations of the reference objects or the teacher.
