Open AccessBehavior-based Task Learning by Demonstration on Mobile Manipulation
Author(s) -
Shu-Chuan Huang,
Erwin Aertbeliën,
Herman Bruyninckx,
Hendrik Van Brussel
Publication year - 2013
Publication title -
international journal of automation and smart technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.148
H-Index - 10
ISSN - 2223-9766
DOI - 10.5875/ausmt.v3i1.161
Subject(s) - artificial intelligence , computer vision , computer science , surface (topology) , point cloud , orientation (vector space) , projection (relational algebra) , process (computing) , point (geometry) , pattern recognition (psychology) , algorithm , mathematics , geometry , operating system
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.status: publishe