Risk Aware Adaptive Belief-dependent Probabilistically Constrained Continuous POMDP Planning

TL;DR

This paper introduces a novel risk-averse, belief-dependent probabilistically constrained POMDP framework for continuous, partially observable environments, offering faster, more flexible decision-making algorithms with comparable safety performance.

Contribution

It presents a new probabilistic formulation for risk-aware continuous POMDPs, along with algorithms that improve speed and flexibility over existing chance-constrained methods.

Findings

Algorithms achieve faster decision-making with similar safety levels.

Probabilistic approach allows flexible risk management via confidence parameters.

Simulation results demonstrate unprecedented celerity in planning.

Abstract

Although risk awareness is fundamental to an online operating agent, it has received less attention in the challenging continuous domain and under partial observability. This paper presents a novel formulation and solution for risk-averse belief-dependent probabilistically constrained continuous POMDP. We tackle a demanding setting of belief-dependent reward and constraint operators. The probabilistic confidence parameter makes our formulation genuinely risk-averse and much more flexible than the state-of-the-art chance constraint. Our rigorous analysis shows that in the stiffest probabilistic confidence case, our formulation is very close to chance constraint. However, our probabilistic formulation allows much faster and more accurate adaptive acceptance or pruning of actions fulfilling or violating the constraint. In addition, with an arbitrary confidence parameter, we did not find any analogs to our approach. We present algorithms for the solution of our formulation in continuous domains. We also uplift the chance-constrained approach to continuous environments using importance sampling. Moreover, all our presented algorithms can be used with parametric and nonparametric beliefs represented by particles. Last but not least, we contribute, rigorously analyze and simulate an approximation of chance-constrained continuous POMDP. The simulations demonstrate that our algorithms exhibit unprecedented celerity compared to the baseline, with the same performance in terms of collisions.