  This work presents a methodology for modeling and predicting human behavior
in settings with N humans interacting in highly multimodal scenarios (i.e.
where there are many possible highly-distinct futures). A motivating example
includes robots interacting with humans in crowded environments, such as
self-driving cars operating alongside human-driven vehicles or human-robot
collaborative bin packing in a warehouse. Our approach to model human behavior
in such uncertain environments is to model humans in the scene as nodes in a
graphical model, with edges encoding relationships between them. For each
human, we learn a multimodal probability distribution over future actions from
a dataset of multi-human interactions. Learning such distributions is made
possible by recent advances in the theory of conditional variational
autoencoders and deep learning approximations of probabilistic graphical
models. Specifically, we learn action distributions conditioned on interaction
history, neighboring human behavior, and candidate future agent behavior in
order to take into account response dynamics. We demonstrate the performance of
such a modeling approach in modeling basketball player trajectories, a highly
multimodal, multi-human scenario which serves as a proxy for many robotic
applications.
