Two fundamental problems in robotics are (1) planning a motion for a
robot to accomplish a specified task in an uncertain environment, and
(2) controlling a robot so that its motion maximally reduces its
uncertainty about the environment. These two problems are intimately
linked; stochastic motion planners implicitly drive robots to reduce
their uncertainty about the environment to ensure that the goal is
reached. Despite this strong link, research in these two topics has
largely proceeded independently from one another. This workshop will
seek to bring together leading researchers in stochastic motion
planning and in information-based control to fuel an exchange of ideas
between these two communities. We will design a full day program of
invited talks and submitted posters aimed at illuminating synergies
between these problems, and spurring advances in both of them. We
will solicit presentations in current stochastic motion planning (SMP)
research areas, including SMP in unknown or uncertain environments;
SMP formulated as a chance constrained optimization program,
randomized SMP techniques, SMP in non-Gaussian belief spaces,
multi-robot SMP, and SMP applications in robotic navigation, grasping
and surgery. We will also solicit presentations in information-based
control research topics, for example control with mutual information
gradients, information surfing, informative path planning, model
predictive control with entropy-based cost, active sensing, and
multi-robot information-based control. Sessions will include a
dedicated time for discussion among the speakers and the audience,
directed by the organizers to address areas of common interest between
stochastic motion planning and information-based control.