Confidence-Based Multi-Robot Learning from Demonstration

Abstract

Learning from demonstration algorithms enable a robot to learn a new policy based on demonstrations provided by a teacher. In this article, we explore a novel research direction, multi-robot learning from demonstration, which extends demonstration based learning methods to collaborative multi-robot domains. Specifically, we study the problem of enabling a single person to teach individual policies to multiple robots at the same time. We present flexMLfD, a task and platform independent multi-robot demonstration learning framework that supports both independent and collaborative multi-robot behaviors. Building upon this framework, we contribute three approaches to teaching collaborative multi-robot behaviors based on different information sharing strategies, and evaluate these approaches by teaching two Sony QRIO humanoid robots to perform three collaborative ball sorting tasks. We then present scalability analysis of flexMLfD using up to seven Sony AIBO robots. We conclude the article by proposing a formalization for a broader multi-robot learning from demonstration research area.