WMRA skill learning through segmentation of demonstration

Abstract

In order to let the wheelchair mounted robotic arm (WMRA) be more intelligent and adaptable, aiming to offer simple and convenient assistance for the elders and disabilities to cope with the complex tasks in the daily life, we present a learning method based on robot learning from demonstration to solve the problem. This method adopts the Beta Process Autoregressive Hidden Markov Model to segment the demonstrations of related task, acquire the contained skills and recognize the repeated skills. After that, it uses the Dynamic Movement Primitives to adjust the related skill according to the given goal position, so as to replay the demonstrated task in a new environment. This learning framework was validated on a six-degree-of-freedom JACO robotic arm, performing the task of drinking water from the bottle through a straw.