  The ability to accurately predict human behavior is central to the safety and
efficiency of robot autonomy in interactive settings. Unfortunately, robots
often lack access to key information on which these predictions may hinge, such
as people's goals, attention, and willingness to cooperate. Dual control theory
addresses this challenge by treating unknown parameters of a predictive model
as stochastic hidden states and inferring their values at runtime using
information gathered during system operation. While able to optimally and
automatically trade off exploration and exploitation, dual control is
computationally intractable for general interactive motion planning, mainly due
to the fundamental coupling between robot trajectory optimization and human
intent inference. In this paper, we present a novel algorithmic approach to
enable active uncertainty reduction for interactive motion planning based on
the implicit dual control paradigm. Our approach relies on sampling-based
approximation of stochastic dynamic programming, leading to a model predictive
control problem that can be readily solved by real-time gradient-based
optimization methods. The resulting policy is shown to preserve the dual
control effect for a broad class of predictive human models with both
continuous and categorical uncertainty. The efficacy of our approach is
demonstrated with simulated driving examples.
