  We present hierarchical policy blending as optimal transport (HiPBOT). HiPBOT
hierarchically adjusts the weights of low-level reactive expert policies of
different agents by adding a look-ahead planning layer on the parameter space.
The high-level planner renders policy blending as unbalanced optimal transport
consolidating the scaling of the underlying Riemannian motion policies. As a
result, HiPBOT effectively decides the priorities between expert policies and
agents, ensuring the task's success and guaranteeing safety. Experimental
results in several application scenarios, from low-dimensional navigation to
high-dimensional whole-body control, show the efficacy and efficiency of
HiPBOT. Our method outperforms state-of-the-art baselines -- either adopting
probabilistic inference or defining a tree structure of experts -- paving the
way for new applications of optimal transport to robot control. More material
at https://sites.google.com/view/hipobot
