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

翻译：我们提出分层策略混合作为最优传输（HiPBOT）。HiPBOT通过在参数空间上添加前瞻规划层，分层调整不同智能体低层反应式专家策略的权重。高层规划器将策略混合呈现为非平衡最优传输，整合了底层黎曼运动策略的缩放。因此，HiPBOT有效决定了专家策略与智能体之间的优先级，确保任务成功并保障安全性。从低维导航到高维全身控制等多个应用场景的实验结果表明，HiPBOT具有高效性与有效性。我们的方法优于当前最先进的基线方法——无论是采用概率推理还是定义专家树结构——为最优传输在机器人控制中的新应用铺平了道路。更多资料请访问 https://sites.google.com/view/hipobot