  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.
