  We focus on the problem of how we can enable a robot to collaborate
seamlessly with a human partner, specifically in scenarios where preexisting
data is sparse. Much prior work in human-robot collaboration uses observational
models of humans (i.e. models that treat the robot purely as an observer) to
choose the robot's behavior, but such models do not account for the influence
the robot has on the human's actions, which may lead to inefficient
interactions. We instead formulate the problem of optimally choosing a
collaborative robot's behavior based on a conditional model of the human that
depends on the robot's future behavior. First, we propose a novel model-based
formulation of conditional behavior prediction that allows the robot to infer
the human's intentions based on its future plan in data-sparse environments. We
then show how to utilize a conditional model for proactive goal selection and
safe trajectory generation around human collaborators. Finally, we use our
proposed proactive controller in a collaborative task with real users to show
that it can improve users' interactions with a robot collaborator
quantitatively and qualitatively.
