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- W4386908136 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." @default.
- W4386908136 created "2023-09-21" @default.
- W4386908136 creator A5079397023 @default.
- W4386908136 creator A5092912386 @default.
- W4386908136 date "2023-09-19" @default.
- W4386908136 modified "2023-09-28" @default.
- W4386908136 title "Measurement Simplification in rho-POMDP with Performance Guarantees" @default.
- W4386908136 doi "https://doi.org/10.48550/arxiv.2309.10701" @default.
- W4386908136 hasPublicationYear "2023" @default.
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