Parametric POMDPs

This book is concerned with planning and acting underuncertainty inpartially-observable continuous domains. It focusseson the problemof mobile robot navigation given a known map. Thedominant paradigmfor robot localisation is to use Bayesian estimationto maintain aprobability distribution over possible robot poses. In contrast,control algorithms often base their decisions on theassumption thatthe most likely state is correct, rather thanconsidering the entiredistribution.This book formulates an approach to planning in thespace ofcontinuous parameterised approximations toprobability distributions.Theoretical and practical results are presented whichshow that, whencompared with similar methods from the literature,this approach iscapable of scaling to larger and more realistic problems.The algorithms have been implemented and demonstratedduring real-timecontrol of a mobile robot in a challenging navigationtask. Resultsshow that this approach produces significantly morerobust behaviourwhen compared with heuristic planners which consideronly the mostlikely states and outcomes.