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Robot person following (RPF) is a crucial capability in human-robotinteraction (HRI) applications, allowing a robot to persistently follow adesignated person. In practical RPF scenarios, the person often be occluded byother objects or people. Consequently, it is necessary to re-identify theperson when he/she re-appears within the robot's field of view. Previous personre-identification (ReID) approaches to person following rely on offline-trainedfeatures and short-term experiences. Such an approach i) has a limited capacityto generalize across scenarios; and ii) often fails to re-identify the personwhen his re-appearance is out of the learned domain represented by theshort-term experiences. Based on this observation, in this work, we propose aReID framework for RPF that leverages long-term experiences. The experiencesare maintained by a loss-guided keyframe selection strategy, to enable onlinecontinual learning of the appearance model. Our experiments demonstrate thateven in the presence of severe appearance changes and distractions fromvisually similar people, the proposed method can still re-identify the personmore accurately than the state-of-the-art methods.

Person Re-Identification for Robot Person Following with Online Continual Learning