Interactive Task Learning for Social Robots: A Pilot Study

Abstract

For socially assistive robots to achieve widespread adoption, the ability to learn new tasks in the wild is critical. Learning from Demonstration (LfD) approaches are a popular method for learning in the wild, but current methods require significant amounts of data and can be difficult to interpret. Interactive Task Learning (ITL) is an emerging learning paradigm that aims to teach tasks in a structured manner, minimizing the need for data and increasing transparency. However, to date ITL has only been explored for physical robotics applications. Additionally, minimal research has explored how usable existing ITL systems are for non-expert users. In this work, we propose a novel approach to learn social tasks via ITL. This system utilizes recent advances in Natural Language Understanding (NLU) to learn from natural dialogue. We conducted a pilot study to compare the ITL system against an LfD approach to investigate differences in teaching performance as well as teachers' perceptions of trust and workload towards these systems. Additionally, we analyzed the teaching behavior of participants to identify successful and unsuccessful teaching strategies. Our findings suggest ITL could provide more transparency to users and improve performance by correcting speech recognition errors. However, participants generally preferred LfD and found it an easier teaching method. From the observed teaching behavior, we identify existing challenges in ITL for non-experts to teach social tasks. Using this, we propose areas of improvement toward future ITL learning paradigms that are intuitive, transparent, and performant.