In situ interactive teaching of trustworthy robotic assistants

Abstract

In this paper we discuss a method for transferring human knowledge to a robotic platform via teleoperation. The method combines unsupervised clustering and classification with interactive instruction to enable behavior capture in a transferable form. We discuss the approach in both simulation and robotic hardware platform to show the capability of the learning system. In this work we also present a definition and associated metric for trustworthiness, and relate this quantity to system performance. Improved performance and trustworthiness are motivations for our application of interactive learning, and we present results that indicate that these were indeed attained.