We propose and investigate a general framework for hierarchical modeling of partially observable environments, such as oce buildings, using Hierarchical Hidden Markov Models (HHMMs). Our main goal is to explore hierarchical modeling as a basis for designing more ecient methods for model construction and useage.
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 dierent 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 modied hierarchical Baum-Welch algorithm to learn these models. We train dierent families of hierarchical models for a simulated and a real world corridor environment and compare them with the standard \at" representation of the same environment.
We show that the hierarchical POMDP approach, combined with model reuse, allows learning hierarchical models that t the data better and train faster than at models.