Bag of Multimodal LDA Models for Concept Formation
In this paper a novel framework for multimodal categorization using Bag of multimodal LDA models is proposed. The main issue, which is tackled in this paper, is granularity of categories. The categories are not fixed but varied according to context. Selective attention is the key to model this granularity of categories. This fact motivates us to introduce various sets of weights to the perceptual information. Obviously, as the weights change, the categories vary. In the proposed model, various sets of weights and model structures are assumed. Then the multimodal LDA-based categorization is carried out many times that results in a variety of models. In order to make the categories (concepts) useful for inference, significant models should be selected. The selection process is carried out through the interaction between the robot and the user. These selected models enable the robot to infer unobserved properties of the object. For example, the robot can infer audio information only from its appearance. Furthermore, the robot can describe appearance of any objects using some suitable words, thanks to the connection between words and perceptual information. The proposed algorithm is implemented on a robot platform and preliminary experiment is carried out to validate the proposed algorithm.
Tomoaki Nakamura Takayuki Nagai Naoto Iwahashi
Department of Electronic Engineering,The University of Electro-Communications 1-5-1 Chofugaoka Chofu NICT Knowledge Creating Communication Research Center 2-2-2 Hikaridai,Seika-cho Souraku-gun,Kyoto 61
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
6233-6238
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）