Hierarchical learning has been successful at learning generalizable
locomotion skills on walking robots in a sample-efficient manner. However, the
low-dimensional "latent" action used to communicate between two layers of the
hierarchy is typically user-designed. In this work, we present a fully-learned
hierarchical framework, that is capable of jointly learning the low-level
controller and the high-level latent action space. Once this latent space is
learned, we plan over continuous latent actions in a model-predictive control
fashion, using a learned high-level dynamics model. This framework generalizes
to multiple robots, and we present results on a Daisy hexapod simulation, A1
quadruped simulation, and Daisy robot hardware. We compare a range of learned
hierarchical approaches from literature, and show that our framework
outperforms baselines on multiple tasks and two simulations. In addition to
learning approaches, we also compare to inverse-kinematics (IK) acting on
desired robot motion, and show that our fully-learned framework outperforms IK
in adverse settings on both A1 and Daisy simulations. On hardware, we show the
Daisy hexapod achieve multiple locomotion tasks, in an unstructured outdoor
setting, with only 2000 hardware samples, reinforcing the robustness and
sample-efficiency of our approach.

本篇论文介绍了一种全学习的层次化框架，可以同时学习低层控制器和高层潜在动作空间，并对多个机器人的运动进行泛化，并利用其学习的高级动力学模型，通过模型预测控制方法计划连续潜在动作，成功实现了多个任务和两个模拟器上的优越表现，并将其与基于反向运动学（IK）的运动规划方法进行比较。在 Daisy 六足机器人实验上，表现出了很好的稳健性和样本效率。