Online learning of exploratory behavior through human-robot interaction
Manabu Gouko, Yuichi Kobayashi, Chyon Hae Kim · 2014
Currently, many studies have been conducted on robot interactions with humans. Object recognition and feature extraction are essential functions for such robots. Discernment behavior is a type of exploratory behavior that supports object feature extraction. We have proposed an active perception model that autonomously learns discernment behaviors. We have shown the effectiveness of our model using a mobile robot simulation. In this study, we applied our model to a real humanoid robot and confirmed that the robot successfully learns exploratory behaviors. We show that the robot can learn suitable exploratory behaviors by online learning applicable to real-world environments.