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.

翻译：持续学习（CL）近年来已成为机器学习（ML）与人机交互（HRI）交叉领域的重要研究方向，旨在使机器人能够在与人类的长期互动中持续学习其环境。然而，大多数持续学习研究以机器人为中心，开发能在静态数据集上快速学习新信息的持续学习算法。本文采取以人为本的持续学习研究路径，旨在理解人类如何长期教导持续学习机器人，以及其教学风格是否存在差异。我们开展了一项包含40名参与者的实地研究，每位参与者与持续学习机器人进行200次交互。这项被试间研究部署了两种不同的持续学习模型于Fetch移动操作机器人。对收集数据的广泛定性和定量分析表明，个体用户的教学风格存在显著差异，这表明需要针对其独特教学风格进行个性化适配。研究结果还显示，尽管专家与非专家用户的教学风格存在差异，但风格差异并未影响持续学习机器人的性能。最终分析表明，目前广泛用于测试大多数持续学习技术的受限实验设置并不充分，因为真实用户会以多种方式与持续学习机器人互动并指导其学习。我们的代码已开源：https://github.com/aliayub7/cl_hri