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We present hierarchical policy blending as optimal transport (HiPBOT). This
hierarchical framework adapts the weights of low-level reactive expert
policies, adding a look-ahead planning layer on the parameter space of a
product of expert policies and agents. Our high-level planner realizes a policy
blending via unbalanced optimal transport, consolidating the scaling of
underlying Riemannian motion policies, effectively adjusting their Riemannian
matrix, and deciding over the priorities between experts and agents,
guaranteeing safety and task success. Our experimental results in a range of
application scenarios from low-dimensional navigation to high-dimensional
whole-body control showcase the efficacy and efficiency of HiPBOT, which
outperforms state-of-the-art baselines that either perform probabilistic
inference or define 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
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