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Online decision making under uncertainty in partially observable domains,also known as Belief Space Planning, is a fundamental problem in robotics andArtificial Intelligence. Due to an abundance of plausible future unravelings,calculating an optimal course of action inflicts an enormous computationalburden on the agent. Moreover, in many scenarios, e.g., information gathering,it is required to introduce a belief-dependent constraint. Prompted by thisdemand, in this paper, we consider a recently introduced probabilisticbelief-dependent constrained POMDP. We present a technique to adaptively acceptor discard a candidate action sequence with respect to a probabilisticbelief-dependent constraint, before expanding a complete set of futureobservations samples and without any loss in accuracy. Moreover, using ourproposed framework, we contribute an adaptive method to find a maximal feasiblereturn (e.g., information gain) in terms of Value at Risk for the candidateaction sequence with substantial acceleration. On top of that, we introduce anadaptive simplification technique for a probabilistically constrained setting.Such an approach provably returns an identical-quality solution whiledramatically accelerating online decision making. Our universal frameworkapplies to any belief-dependent constrained continuous POMDP with parametricbeliefs, as well as nonparametric beliefs represented by particles. In thecontext of an information-theoretic constraint, our presented frameworkstochastically quantifies if a cumulative information gain along the planninghorizon is sufficiently significant (e.g. for, information gathering, activeSLAM). We apply our method to active SLAM, a highly challenging problem of highdimensional Belief Space Planning. Extensive realistic simulations corroboratethe superiority of our proposed ideas.

Simplified Continuous High Dimensional Belief Space Planning with Adaptive Probabilistic Belief-dependent Constraints