E-HiPPo: Extensions to Hierarchical POMDP-based Visual Planning on a Robot

One major challenge to the widespread deployment of mobile robots is the ability to autonomously tailor the sensory processing to the task on hand. In our prior work ̧itemohan:icaps08, we proposed an approach for such general-purpose processing of visual input in an application domain where a robot and a human jointly converse about and manipulate objects on a tabletop by processing the regions of interest (ROIs) in input images. We posed the visual processing management problem as a partially observable Markov decision problem (POMDP), and introduced a hierarchical decomposition to make it tractable to plan with POMDPs. In this paper we analyze and eliminate some of the limitations of the existing approach. First, in addition to tackling visual actions that analyze the state of the world represented by the image, we show how to incorporate actions that can change the state. Secondly, we show how policy caching can be used to speed the planning performance and analyse the tradeoff between planning speed and plan quality.