Structured Learning for A Prediction-based Perceptual System of Partner Robots

This paper discusses structured learning for the prediction-based control of perceptual modules of partner robots. A partner robot should classify and predict human behavior patterns to control perceptual modules for natural communication with a human. Therefore we proposed a prediction-based perceptual system. The proposed system has three main functions; (1) the clustering of perceptual information (the extraction of spatial patterns), (2) the prediction of transition among the clusters (the extraction of temporal patterns), and (3) selection of perceptual modules (the control of sampling intervals). Finally, we show experimental results on the interaction with a human to discuss the effectiveness of our proposed method.

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