Learning motion primitives from demonstration

Abstract

With the purpose to offer simple and convenient assistance for the elders with disabilities to take care of themselves in activities of daily living, we present a motion primitives learning method based on robot learning from demonstration to improve the intelligence and adaptability of the wheelchair-mounted robotic arm. This method adopts the beta process autoregressive hidden Markov model to segment the demonstrations of related task, acquire the contained motion primitives, and recognize the repeated motion primitives. After that, it adopts the dynamic movement primitives to adjust the related motion primitives according to the task instructions by the wheelchair-mounted robotic arm users, so as to replay the demonstrated task in a new environment. This learning framework is validated on a 6-degree-of-freedom JACO robotic arm, performing the tasks of drinking water from the bottle through a straw and pouring water from the bottle to the cup.