Transfer learning in assistive robotics: from human to robot domain

Transfer Learning (TL) aims to learn a problem from a source reference to improve on the performance achieved in a target reference. Recently, this concept has been applied in different domains, especially, when the data in the target is insufficient. TL can be applied across domains or across tasks. However, the challenges related to what to transfer, how to transfer and when to transfer create limitations in the realisation of this concept in day to day applications. To address the challenges, this paper presents an overview of the concept of TL and how it can be applied in human-robot interaction for assistive robots requiring to learn human tasks in Ambient Assisted Living environments. The differences in feature spaces between a human (source domain) and robot (target domain) makes it difficult for tasks to be directly learned by robots. To address the challenges of this task, we propose a model for learning across feature spaces by mapping the features in the source domain to the target domain features.