Federated Continual Learning for Socially Aware Robotics

Abstract

From learning assistance to companionship, socially aware and socially assistive robotics is promising for enhancing many aspects of daily life. However, socially aware robots face many challenges preventing their widespread public adoption. Two such major challenges are (1) lack in behavior adaptation to new environments, contexts and users, and (2) insufficient capability for privacy protection. The commonly employed centralized learning paradigm, whereby training data is gathered and centralized in a single location (i.e., machine/ server) and the centralized entity trains and hosts the model, 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 paradigm that aims to improve the personalization capability of social robots while also paving the way towards privacy preservation. First, we present a new framework by capitalising on two machine learning approaches, Federated Learning and Continual Learning, to capture interaction dynamics distributed physically across robots and temporally across repeated robot encounters. Second, we introduce four criteria (adaptation quality, adaptation time, knowledge sharing, and model overhead) that should be balanced within our decentralized robot learning framework. Third, we develop a new algorithm – Elastic Transfer – that leverages importance-based regularization to preserve relevant parameters across robots and interactions with multiple humans (users). We show that decentralized learning is a viable alternative to centralized learning in a proof-of-concept Socially-Aware Navigation domain, and demonstrate the efficacy of Elastic Transfer across our proposed evaluation criteria.