  Motion generation in cluttered, dense, and dynamic environments is a central
topic in robotics, rendered as a multi-objective decision-making problem.
Current approaches trade-off between safety and performance. On the one hand,
reactive policies guarantee fast response to environmental changes at the risk
of suboptimal behavior. On the other hand, planning-based motion generation
provides feasible trajectories, but the high computational cost may limit the
control frequency and thus safety. To combine the benefits of reactive policies
and planning, we propose a hierarchical motion generation method. Moreover, we
adopt probabilistic inference methods to formalize the hierarchical model and
stochastic optimization. We realize this approach as a weighted product of
stochastic, reactive expert policies, where planning is used to adaptively
compute the optimal weights over the task horizon. This stochastic optimization
avoids local optima and proposes feasible reactive plans that find paths in
cluttered and dense environments. Our extensive experimental study in planar
navigation and 6DoF manipulation shows that our proposed hierarchical motion
generation method outperforms both myopic reactive controllers and online
re-planning methods.
