Online learning of exploratory behavior through human-robot interaction

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 mode...

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Bibliographic Details
Published in:HRI '14 : proceedings of the 2014 ACM/IEEE International Conference on Human-Robot Interaction : March 3-6, 2014, Bielefeld, Germany pp. 166 - 167
Main Authors: Gouko, Manabu, Kobayashi, Yuichi, Kim, Chyon Hae
Format: Conference Proceeding
Language:English
Japanese
Published: New York, NY, USA ACM 03.03.2014
Series:ACM Conferences
Subjects:
Cameras
Computing methodologies > Machine learning > Learning paradigms
Computing methodologies > Machine learning > Machine learning approaches > Markov decision processes
Feature extraction
Human-robot interaction
Robot vision systems
Theory of computation > Theory and algorithms for application domains > Machine learning theory > Markov decision processes
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ISBN:1450326587, 9781450326582
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Summary: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.
ISBN:1450326587
9781450326582
DOI:10.1145/2559636.2563686