Incremental adaptive integration of layers of a hybrid control architecture

Hybrid deliberative-reactive control architectures are a popular and effective approach to the control of robotic navigation applications. However, due to the fundamental differences in the design of the reactive and deliberative layers, the design of hybrid control architectures can pose significant difficulties. We propose a novel approach to improving system-level performance of hybrid control architectures by incrementally improving the deliberative layer´s model of the reactive layer´s execution of its plans. Incremental supervised learning techniques are employed to learn the model. Quantitative and qualitative results from a physics-based simulator are presented.