Efficient Personalization of Robot Behaviors through Preference-based Action Representation Learning

Personalization in human-robot interaction can be achieved efficiently by learning a latent action space that maximizes the mutual information between the pre-trained robot policy and the user preference-aligned domain, without significantly compromising the original task performance.