Learning Skills From Demonstrations: A Trend From Motion Primitives to Experience Abstraction

Abstract

The uses of robots are changing from static environments in factories to encompass novel concepts such as human–robot collaboration in unstructured settings. Preprogramming all the functionalities for robots becomes impractical, and hence, robots need to learn how to react to new events autonomously, just like humans. However, humans, unlike machines, are naturally skilled in responding to unexpected circumstances based on either experiences or observations. Hence, embedding such anthropoid behaviors into robots entails the development of neuro-cognitive models that emulate motor skills under a robot learning paradigm. Effective encoding of these skills is bound to the proper choice of tools and techniques. This survey paper studies different motion and behavior learning methods ranging from movement primitives (MPs) to experience abstraction (EA), applied to different robotic tasks. These methods are scrutinized and then experimentally benchmarked by reconstructing a standard pick-n-place task. Apart from providing a standard guideline for the selection of strategies and algorithms, this article aims to draw a perspective on their possible extensions and improvements.