Parametric POMDPs

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This book is concerned with planning and acting under uncertainty in partially-observable continuous domains. It focusses on the problem of mobile robot navigation given a known map. The dominant paradigm for robot localisation is to use Bayesian estimation to maintain a probability distribution over possible robot poses. In contrast, control algorithms often base their decisions on the assumption that the most likely state is correct, rather than considering the entire distribution. This book formulates an approach to planning in the space of continuous parameterised approximations to probability distributions. Theoretical and practical results are presented which show that, when compared with similar methods from the literature, this approach is capable of scaling to larger and more realistic problems. The algorithms have been implemented and demonstrated during real-time control of a mobile robot in a challenging navigation task. Results show that this approach produces significantly more robust behaviour when compared with heuristic planners which consider only the most likely states and outcomes.