Behavior-based Task Learning by Demonstration on Mobile Manipulation

Task learning for behavior‐based mobile manipulation is formalized as a behavior recognition problem in this paper. The goal is to generate a behavior diagram through human demonstration, which can be used as a template for execution of a specified task. In order to extract significant and meaningfu...

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Bibliographic Details
Published inInternational journal of automation and smart technology Vol. 3; no. 1; pp. 19 - 28
Main Authors Shu Huang, Erwin Aertbeliën, Herman Bruyninckx, and Hendrik Van Brussel
Format Journal Article
LanguageEnglish
Published Chinese Institute of Automation Engineers (CIAE) & Taiwan Smart Living Space Association (SMART LISA) 16.07.2025
Subjects
Behavior-based
Mobile manipulation
Programming by demonstration
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ISSN2223-9766
2223-9766
DOI10.5875/ausmt.v3i1.161

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Summary:Task learning for behavior‐based mobile manipulation is formalized as a behavior recognition problem in this paper. The goal is to generate a behavior diagram through human demonstration, which can be used as a template for execution of a specified task. In order to extract significant and meaningful information from sensory data, various features are defined to convert the sensory data to the feature space. A generic task learning approach based on combined machine learning techniques is used to classify behaviors from the feature space. Not only can this approach recognize already‐known behaviors, it can also provide an easy method to add new behaviors to a system by combining new training datasets with the existing ones through retraining a support vector machine kernel (SVM) model. Common household applications, such as door‐opening and window‐cleaning tasks, are implemented for the evaluation of the proposed method. 
ISSN:2223-9766
2223-9766
DOI:10.5875/ausmt.v3i1.161