Analysis and a home assistance application of online AEMS2 planning
Előd Páll, Levente Tamás, Lucian Buşoniu
Abstract
We consider an online planning algorithm for partially observable Markov decision processes (POMDPs), called Anytime Error Minimization Search 2 (AEMS2). Despite the considerable success it has enjoyed in robotics and other problems, no quantitative analysis exists of the relationship between its near-optimality and the computation invested. Exploiting ideas from fully-observable MDP planning, we provide here such an analysis, in which the relationship is modulated via a measure of problem complexity called near-optimality exponent. We illustrate the exponent for some interesting POMDP structures, and examine the role of the informative heuristics used by AEMS2 in the guarantees. In the second part of the paper, we introduce a domestic assistance problem in which a robot monitors partially observable switches and turns them off if needed. AEMS2 successfully solves this task in real experiments, and also works better than several state of the art planners in simulation comparisons.
BibTeX
@inproceedings{iros2016_analysisandahome,
title = {Analysis and a home assistance application of online AEMS2 planning},
author = {Előd Páll and Levente Tamás and Lucian Buşoniu},
booktitle = {IROS 2016},
year = {2016}
}