Hierarchical Spatial Concept Formation Based on Multimodal Information for Human Support Robots.

ABSTRACT: In this paper, we propose a hierarchical spatial concept formation method based on the Bayesian generative model with multimodal information e.g., vision, position and word information. Since humans have the ability to select an appropriate level of abstraction according to the situation and describe their position linguistically, e.g., "I am in my home" and "I am in front of the table," a hierarchical structure of spatial concepts is necessary in order for human support robots to communicate smoothly with users. The proposed method enables a robot to form hierarchical spatial concepts by categorizing multimodal information using hierarchical multimodal latent Dirichlet allocation (hMLDA). Object recognition results using convolutional neural network (CNN), hierarchical k-means clustering

SUBMITTER: Hagiwara Y 

PROVIDER: S-EPMC5859180 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature