2024/03/12 by Saksham Checker, Checker, Saksham, Nikhil Churamani +3 · 1 citation
Physics and Astronomy · #Artificial Intelligence (cs.AI) #Computers and Society (cs.CY) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Opinion Dynamics and Social Influence #Robotics (cs.RO)
paper · pdf · doi:10.48550/arxiv.2403.07586
openalex publication_date 2024/03/12 · openalex created_date 2024/03/14 · openalex updated_date 2026/07/28
As social robots become increasingly integrated into daily life, ensuring their behaviours align with social norms is crucial. For their widespread open-world application, it is important to explore Federated Learning (FL) settings where individual robots can learn about their unique environments while also learning from each others' experiences. In this paper, we present a novel FL benchmark that evaluates different strategies, using multi-label regression objectives, where each client individually learns to predict the social appropriateness of different robot actions while also sharing their learning with others. Furthermore, splitting the training data by different contexts such that each client incrementally learns across contexts, we present a novel Federated Continual Learning (FCL) benchmark that adapts FL-based methods to use state-of-the-art Continual Learning (CL) methods to continually learn socially appropriate agent behaviours under different contextual settings. Federated Averaging (FedAvg) of weights emerges as a robust FL strategy while rehearsal-based FCL enables incrementally learning the social appropriateness of robot actions, across contextual splits.