Social navigationIntermediate
STARS: From Spatiotemporal Dynamics to Social Representations in Human-Robot Interaction
STARS is a preprint method that learns compact, reusable social representations from short multi-agent navigation windows by treating agents as nodes in a spatiotemporal graph and training a self-supervised graph variational autoencoder. In downstream tests, its heterogeneous variant reaches 0.503 SNS macro F1 for pedestrian action classification versus about 0.30–0.33 for unstructured baselines, while remaining competitive on SEAN-T human perception tasks. This matters because it shows structured graph representations can offset scarce human-robot-interaction data, enabling data-efficient social navigation.