Abstract-The ability to act in a socially-aware way is a key skill for robots that share a space with humans. In this paper we address the problem of socially-aware navigation among people that meets objective criteria such as travel time or path length as well as subjective criteria such as social comfort. Opposed to modelbased approaches typically taken in related work, we pose the problem as an unsupervised learning problem. We learn a set of dynamic motion prototypes from observations of relative motion behavior of humans found in publicly available surveillance data sets. The learned motion prototypes are then used to compute dynamic cost maps for path planning using an any-angle A* algorithm. In the evaluation we demonstrate that the learned behaviors are better in reproducing human relative motion in both criteria than a Proxemics-based baseline method.

Human activity recognition is a key component for socially enabled robots to effectively and naturally interact with humans. In this paper we exploit the fact that many human activities produce characteristic sounds from which a robot can infer the corresponding actions. We propose a novel recognition approach called Non-Markovian Ensemble Voting (NEV) able to classify multiple human activities in an online fashion without the need for silence detection or audio stream segmentation. Moreover, the method can deal with activities that are extended over undefined periods in time.In a series of experiments in real reverberant environments, we are able to robustly recognize 22 different sounds that correspond to a number of human activities in a bathroom and kitchen context. Our method outperforms several established classification techniques.