Learning intentions for improved human motion prediction

Abstract

For many tasks, robots need to operate in human populated environments. Human motion prediction therefore is gaining importance. The concept of social forces defines virtual repelling and attracting forces from and to obstacles and points of interest. These social forces can then be used to model typical human movements given an environment and a person's intention. This work shows how such models can exploit typical motion patterns summarized by growing hidden Markov models (GHMMs) that can be learned from data online and without human intervention. An extensive series of experiments shows that exploiting the intended position estimated using a GHMM within a social forces based motion model yields a significant performance gain in comparison with the standard constant velocity-based models.