  In human-robot interaction (HRI) systems, such as autonomous vehicles,
understanding and representing human behavior are important. Human behavior is
naturally rich and diverse. Cost/reward learning, as an efficient way to learn
and represent human behavior, has been successfully applied in many domains.
Most of traditional inverse reinforcement learning (IRL) algorithms, however,
cannot adequately capture the diversity of human behavior since they assume
that all behavior in a given dataset is generated by a single cost function.In
this paper, we propose a probabilistic IRL framework that directly learns a
distribution of cost functions in continuous domain. Evaluations on both
synthetic data and real human driving data are conducted. Both the quantitative
and subjective results show that our proposed framework can better express
diverse human driving behaviors, as well as extracting different driving styles
that match what human participants interpret in our user study.
