Dogged Learning for Robots

Abstract

Ubiquitous robots need the ability to adapt their behaviour to the changing situations and demands they will encounter during their lifetimes. In particular, non-technical users must be able to modify a robot's behaviour to enable it to perform new, previously unknown tasks. Learning from demonstration is a viable means to transfer a desired control policy onto a robot and mixed-initiative control provides a method for smooth transitioning between learning and acting. We present a learning system (dogged learning) that combines learning from demonstration and mixed initiative control to enable lifelong learning for unknown tasks. We have implemented dogged learning on a Sony Aibo and successfully taught it behaviours such as mimicry and ball seeking