  Human behavior prediction models enable robots to anticipate how humans may
react to their actions, and hence are instrumental to devising safe and
proactive robot planning algorithms. However, modeling complex interaction
dynamics and capturing the possibility of many possible outcomes in such
interactive settings is very challenging, which has recently prompted the study
of several different approaches. In this work, we provide a self-contained
tutorial on a conditional variational autoencoder (CVAE) approach to human
behavior prediction which, at its core, can produce a multimodal probability
distribution over future human trajectories conditioned on past interactions
and candidate robot future actions. Specifically, the goals of this tutorial
paper are to review and build a taxonomy of state-of-the-art methods in human
behavior prediction, from physics-based to purely data-driven methods, provide
a rigorous yet easily accessible description of a data-driven, CVAE-based
approach, highlight important design characteristics that make this an
attractive model to use in the context of model-based planning for human-robot
interactions, and provide important design considerations when using this class
of models.
