A Method of Recognition of Human Hand and Body Gestures for Task-Level Learning from Demonstration

Abstract

Different problems of robot learning and planning have received considerable attention, recently. In particular, we can mention robot task learning. Robot learning from demonstration is especially important for robots that operate in unstructured environments. The effectiveness of such learning depends strongly on the quality of vision-based analysis of human hand and body gestures. In this paper, we consider a method of recognition of human hand and body gestures that based on a modified longest common subsequence algorithm with adaptive parameters.