Learning hierarchical observable Markov decision process models for robot navigation
We propose and investigate a general framework for hierarchical modeling of partially observable environments, such as office buildings, using hierarchical hidden Markov models (HHMMs). Our main goal is to explore hierarchical modeling as a basis for designing more efficient methods for model construction and usage. As a case study we focus on indoor robot navigation and show how this framework can be used to learn a hierarchy of models of the environment at different levels of spatial abstraction. We introduce the idea of model reuse that can be used to combine already learned models into a larger model. We describe an extension of the HHMM model to includes actions, which we call hierarchical POMDPs, and describe a modified hierarchical Baum-Welch algorithm to learn these models. We train different families of hierarchical models for a simulated and a real world corridor environment and compare them with the standard "flat" representation of the same environment. We show that the hierarchical POMDP approach, combined with model reuse, allows learning hierarchical models that fit the data better and train faster than flat models.