  Robot understanding of human intentions is essential for fluid human-robot
interaction. Intentions, however, cannot be directly observed and must be
inferred from behaviors. We learn a model of adaptive human behavior
conditioned on the intention as a latent variable. We then embed the human
behavior model into a principled probabilistic decision model, which enables
the robot to (i) explore actively in order to infer human intentions and (ii)
choose actions that maximize its performance. Furthermore, the robot learns
from the demonstrated actions of human experts to further improve exploration.
Preliminary experiments in simulation indicate that our approach, when applied
to autonomous driving, improves the efficiency and safety of driving in common
interactive driving scenarios.
