Hierarchical Policy Blending As Optimal Transport
Abstract
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
Cite
@article{arxiv.2212.01938,
title = {Hierarchical Policy Blending As Optimal Transport},
author = {An T. Le and Kay Hansel and Jan Peters and Georgia Chalvatzaki},
journal= {arXiv preprint arXiv:2212.01938},
year = {2023}
}
Comments
16 pages, 5 figures, accepted to the 5th Annual Learning for Dynamics & Control Conference (L4DC)