確率的保証と実践による連続POMDP計画における複雑な観測モデルの簡略化
Simplifying Complex Observation Models in Continuous POMDP Planning with Probabilistic Guarantees and Practice
高次元連続観測を持つPOMDP計画において、簡略化観測モデルを用いる際の解品質の確率的保証を与え、オンライン計画中に高コストモデルへアクセスせずに形式的保証を達成する手法を提案した。
著者: Idan Lev-Yehudi, Moran Barenboim, Vadim Indelman
分類: cs.AI, cs.RO
原文アブストラクト
Solving partially observable Markov decision processes (POMDPs) with high dimensional and continuous observations, such as camera images, is required for many real life robotics and planning problems. Recent researches suggested machine learned probabilistic models as observation models, but their use is currently too computationally expensive for online deployment. We deal with the question of what would be the implication of using simplified observation models for planning, while retaining formal guarantees on the quality of the solution. Our main contribution is a novel probabilistic bound based on a statistical total variation distance of the simplified model. We show that it bounds the theoretical POMDP value w.r.t. original model, from the empirical planned value with the simplified model, by generalizing recent results of particle-belief MDP concentration bounds. Our calculations can be separated into offline and online parts, and we arrive at formal guarantees without having to access the costly model at all during planning, which is also a novel result. Finally, we demonstrate in simulation how to integrate the bound into the routine of an existing continuous online POMDP solver.
