HiPPo: hierarchical POMDPs for planning information processing and sensing actions on a robot

Abstract

Flexible general purpose robots need to tailor their visual pro-cessing to their task, on the fly. We propose a new approach to this within a planning framework, where the goal is to plan a sequence of visual operators to apply to the regions of interest (ROIs) in a scene. We pose the visual processing problem as a Partially Observable Markov Decision Process (POMDP). This requires probabilistic models of operator effects to quan-titatively capture the unreliability of the processing actions, and thus reason precisely about trade-offs between plan ex-ecution time and plan reliability. Since planning in practical sized POMDPs is intractable we show how to ameliorate this intractability somewhat for our domain by defining a hier-archical POMDP. We compare the hierarchical POMDP ap-proach with a Continual Planning (CP) approach. On a real robot visual domain, we show empirically that all the plan-ning methods outperform naive application of all visual op-erators. The key result is that the POMDP methods produce more robust plans than either naive visual processing or the CP approach. In summary, we believe that visual processing problems represent a challenging and worthwhile domain for planning techniques, and that our hierarchical POMDP based approach to them opens up a promising new line of research.