Measurement Simplification in \rho-POMDP with Performance Guarantees

TL;DR

This paper introduces a novel partitioning approach to simplify decision-making in -POMDPs, providing performance guarantees and significant computational speed-ups in active SLAM tasks.

Contribution

It presents a new observation space partitioning method with analytical bounds, extending to Gaussian beliefs, improving planning efficiency with theoretical guarantees.

Findings

Bounds are adaptive and converge to the original solution.

The Gaussian belief variant offers at least 4x performance improvement.

Experiments show significant speed-up in active SLAM with maintained performance.

Abstract

Decision making under uncertainty is at the heart of any autonomous system acting with imperfect information. The cost of solving the decision making problem is exponential in the action and observation spaces, thus rendering it unfeasible for many online systems. This paper introduces a novel approach to efficient decision-making, by partitioning the high-dimensional observation space. Using the partitioned observation space, we formulate analytical bounds on the expected information-theoretic reward, for general belief distributions. These bounds are then used to plan efficiently while keeping performance guarantees. We show that the bounds are adaptive, computationally efficient, and that they converge to the original solution. We extend the partitioning paradigm and present a hierarchy of partitioned spaces that allows greater efficiency in planning. We then propose a specific variant of these bounds for Gaussian beliefs and show a theoretical performance improvement of at least a factor of 4. Finally, we compare our novel method to other state of the art algorithms in active SLAM scenarios, in simulation and in real experiments. In both cases we show a significant speed-up in planning with performance guarantees.