Measurement Simplification in ρ-POMDP with Performance Guarantees
This work addresses the problem of efficient online decision-making for autonomous systems with imperfect information, offering incremental improvements in planning speed with guarantees.
The paper tackles the computational cost of decision-making under uncertainty by partitioning high-dimensional observation spaces, providing analytical bounds on expected reward with performance guarantees. It demonstrates a theoretical performance improvement of at least a factor of 4 for Gaussian beliefs and shows significant speed-up in planning in active SLAM scenarios.
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.