A learning approach to integration of layers of a hybrid control architecture

Abstract

Hybrid deliberative-reactive control architectures are a popular and effective approach to the control of robotic navigation applications. However, the design of said architectures is difficult, due to the fundamental differences in the design of the reactive and deliberative layers of the architecture. We propose a novel approach to improving system-level performance of said architectures, by improving the deliberative layer's model of the reactive layer's execution of its plans through the use of machine learning techniques. Quantitative and qualitative results from a physics-based simulator are presented.