Cross-Situational Learning with Bayesian Generative Models for Multimodal Category and Word Learning in Robots.

ABSTRACT: In this paper, we propose a Bayesian generative model that can form multiple categories based on each sensory-channel and can associate words with any of the four sensory-channels (action, position, object, and color). This paper focuses on cross-situational learning using the co-occurrence between words and information of sensory-channels in complex situations rather than conventional situations of cross-situational learning. We conducted a learning scenario using a simulator and a real humanoid iCub robot. In the scenario, a human tutor provided a sentence that describes an object of visual attention and an accompanying action to the robot. The scenario was set as follows: the number of words per sensory-channel was three or four, and the number of trials for learning was 20 and 40 for t

SUBMITTER: Taniguchi A 

PROVIDER: S-EPMC5742219 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature