Abstract

This paper presents a method for personalizing human-robot interaction by using emotive facial expressions to generate affective robot movements. The authors use autoencoder neural networks to compress robot movement data and facial expression images into a shared latent embedding space, then generate novel movements from these embeddings, aligned via a triplet loss so robots can respond to multimodal user input with newly generated, affectively-matched movements rather than relying solely on fixed preprogrammed behaviors.