Learning motion primitives from demonstration

• Published in: Advances in mechanical engineering Vol. 9; no. 12; p. 168781401773726
• Main Authors: Chi, Mingshan, Yao, Yufeng, Liu, Yaxin, Teng, Yiqian, Zhong, Ming
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.12.2017; Sage Publications Ltd; SAGE Publishing
• Subjects: Activities of daily living; Algorithms; Automation; Autoregressive models; Autoregressive processes; Disabilities; Drinking water; Dynamical systems; International conferences; Knowledge; Learning; Markov chains; Movement; Neural networks; Rehabilitation; Robot arms; Robot learning; Robotics; Robots; Skills; Straw; Wheelchairs; learning from demonstration; Assistive robot; motion primitive; wheelchair-mounted robotic arm; skill learning
• ISSN: 1687-8132; 1687-8140; 1687-8140
• DOI: 10.1177/1687814017737260

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.