Training a robot via human feedback: A case study

Abstract

We present a case study of applying a framework for learning from numeric human feedback - TAMER - to a physically embodied robot. In doing so, we also provide the first demonstration of the ability to train multiple behaviors by such feedback without algorithmic modifications and of a robot learning from free-form human-generated feedback without any further guidance or evaluative feedback.We describe transparency challenges specific to a physically embodied robot learning from human feedback and adjustments that address these challenges. © Springer International Publishing 2013.