A Survey on Perception Methods for Human–Robot Interaction in Social Robots

For human–robot interaction (HRI), perception is one of the most important capabilities. This paper reviews several widely used perception methods of HRI in social robots. Specifically, we investigate general perception tasks crucial for HRI, such as where the objects are located in the rooms, what...

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Bibliographic Details
Published in:International journal of social robotics 2014-01, Vol.6 (1), p.85-119
Main Authors: Yan, Haibin, Ang, Marcelo H., Poo, Aun Neow
Format: Article
Language:English
Subjects:
Control
Discriminant analysis
Engineering
Feature extraction
Mechatronics
Object recognition
Perception
Principal components analysis
Reduction
Robotics
Robots
Segmentation
Semantics
Survey
Visual signals
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Summary:For human–robot interaction (HRI), perception is one of the most important capabilities. This paper reviews several widely used perception methods of HRI in social robots. Specifically, we investigate general perception tasks crucial for HRI, such as where the objects are located in the rooms, what objects are in the scene, and how they interact with humans. We first enumerate representative social robots and summarize the most three important perception methods from these robots: feature extraction, dimensionality reduction, and semantic understanding. For feature extraction, four widely used signals including visual-based, audio-based, tactile-based and rang sensors-based are reviewed, and they are compared based on their advantages and disadvantages. For dimensionality reduction, representative methods including principle component analysis (PCA), linear discriminant analysis (LDA), and locality preserving projections (LPP) are reviewed. For semantic understanding, conventional techniques for several typical applications such as object recognition, object tracking, object segmentation, and speaker localization are discussed, and their characteristics and limitations are also analyzed. Moreover, several popular data sets used in social robotics and published semantic understanding results are analyzed and compared in light of our analysis of HRI perception methods. Lastly, we suggest important future work to analyze fundamental questions on perception methods in HRI.
ISSN:1875-4791
1875-4805
DOI:10.1007/s12369-013-0199-6