From learning assistance to companionship, social robots promise to enhance many aspects of daily life. However, social robots have not seen widespread adoption, in part because (1) they do not adapt their behavior to new users, and (2) they do not provide sufficient privacy protections. Centralized learning, whereby robots develop skills by gathering data on a server, contributes to these limitations by preventing online learning of new experiences and requiring storage of privacy-sensitive data. In this work, we propose a decentralized learning alternative that improves the privacy and personalization of social robots. We combine two machine learning approaches, Federated Learning and Continual Learning, to capture interaction dynamics distributed physically across robots and temporally across repeated robot encounters. We define a set of criteria that should be balanced in decentralized robot learning scenarios. We also develop a new algorithm -- Elastic Transfer -- that leverages importance-based regularization to preserve relevant parameters across robots and interactions with multiple humans. We show that decentralized learning is a viable alternative to centralized learning in a proof-of-concept Socially-Aware Navigation domain, and demonstrate how Elastic Transfer improves several of the proposed criteria.

翻译：从学习辅助到情感陪伴，社交机器人有望提升日常生活的诸多方面。然而，社交机器人尚未得到广泛普及，部分原因在于：（1）它们无法根据新用户调整自身行为；（2）它们未能提供充分的隐私保护。集中式学习——即机器人通过将数据汇集至服务器来习得技能——既阻碍了在线学习新经验的能力，又要求存储隐私敏感数据，从而加剧了上述局限。本研究提出一种去中心化学习方案，旨在提升社交机器人的隐私保护与个性化能力。我们融合联邦学习与持续学习两种机器学习方法，以捕捉跨机器人空间分布、跨重复交互时间演变的交互动态。我们定义了去中心化机器人学习场景中应平衡的一组准则，并开发了一种新算法——弹性迁移（Elastic Transfer）——该算法利用基于重要性的正则化机制，在机器人与多人类交互过程中保留关键参数。通过概念验证型社交感知导航任务，我们证明去中心化学习是集中式学习的可行替代方案，并展示了弹性迁移如何改进所提出的多项准则。