Computationally Affordable Hierarchical Framework for Humanoid Robot Control

Abstract

We propose a hierarchical control framework for generating versatile motions by a humanoid robot. The central feature of our framework is computational affordability: a large amount of computation time is allowable in the upper-level hierarchy. Consequently, whole-body trajectory optimization for a long time horizon becomes feasible. To ensure such affordability, a fast feedback loop is established in the lower-level hierarchy to increase the robustness against the large latency in the upper level. We experimentally examined the advantages of the achieved computational affordability. Our framework allowed a large computational time of 100 ms in each control cycle. This enables online trajectory optimization to predict 50 time steps ahead while taking full-body dynamics into account. Due to such a long prediction range, 20 motions were successfully generated in real time with our computation-ally affordable framework.