Adaptive Information Belief Space Planning
This work addresses the computational burden in uncertainty-aware planning for autonomous systems, representing an incremental improvement over existing methods.
The paper tackles the problem of efficient planning under uncertainty by proposing an approximation using an abstract observation model with aggregation to reduce computational costs, achieving identical action selection with a fraction of the computational time.
Reasoning about uncertainty is vital in many real-life autonomous systems. However, current state-of-the-art planning algorithms cannot either reason about uncertainty explicitly, or do so with a high computational burden. Here, we focus on making informed decisions efficiently, using reward functions that explicitly deal with uncertainty. We formulate an approximation, namely an abstract observation model, that uses an aggregation scheme to alleviate computational costs. We derive bounds on the expected information-theoretic reward function and, as a consequence, on the value function. We then propose a method to refine aggregation to achieve identical action selection with a fraction of the computational time.