How Do Human Users Teach a Continual Learning Robot in Repeated Interactions?

TL;DR

This study explores how humans teach continual learning robots over long-term interactions, revealing significant variation in teaching styles and emphasizing the need for personalized adaptation in human-robot teaching scenarios.

Contribution

It provides the first comprehensive analysis of human teaching behaviors in long-term continual learning robot interactions, highlighting the diversity of teaching styles and their impact.

Findings

Significant variation exists in individual teaching styles.

Teaching style differences do not affect robot performance.

Current experimental setups are inadequate for real-world human-robot teaching studies.

Abstract

Continual learning (CL) has emerged as an important avenue of research in recent years, at the intersection of Machine Learning (ML) and Human-Robot Interaction (HRI), to allow robots to continually learn in their environments over long-term interactions with humans. Most research in continual learning, however, has been robot-centered to develop continual learning algorithms that can quickly learn new information on static datasets. In this paper, we take a human-centered approach to continual learning, to understand how humans teach continual learning robots over the long term and if there are variations in their teaching styles. We conducted an in-person study with 40 participants that interacted with a continual learning robot in 200 sessions. In this between-participant study, we used two different CL models deployed on a Fetch mobile manipulator robot. An extensive qualitative and quantitative analysis of the data collected in the study shows that there is significant variation among the teaching styles of individual users indicating the need for personalized adaptation to their distinct teaching styles. The results also show that although there is a difference in the teaching styles between expert and non-expert users, the style does not have an effect on the performance of the continual learning robot. Finally, our analysis shows that the constrained experimental setups that have been widely used to test most continual learning techniques are not adequate, as real users interact with and teach continual learning robots in a variety of ways. Our code is available at https://github.com/aliayub7/cl_hri.