We develop a fundamentally novel paradigm that seeks to find a simplification of a given POMDP problem, which is computationally easier, while at the same time providing performance guarantees, and ideally, similar levels of performance as the original decision making problem.
Based on this conceptually novel paradigm, we develop approaches that simplify the decision making problem, for example, by resorting to belief simplification or reward function simplification.
We develop approaches for autonomous semantic perception addressing key challenges such as: classification aliasing for certain relative viewpoints between object & camera, localization uncertainty, and epistemic uncertainty of the classifier. Specifically, approaches for computationally efficient probabilistic inference and decision making, are developed, in the context of semantic perception and SLAM. A key component here is a learned viewpoint-dependent classifier model.