高次元POMDPのためのスケーラブルなRao-Blackwell化オンラインプランニング
Scalable Rao-Blackwellized Online Planning for High-Dimensional POMDPs
高次元の部分観測環境でのオンラインプランニングを改善するため、Rao-Blackwell化POMDPフレームワークを拡張し、連続・離散ハイブリッド信念表現で不確実性を解析的に伝播させてサンプリング分散を低減する手法を提案。探索救助タスクでFastSLAM 2.0と統合し、少ない粒子とシミュレーションで高い累積報酬を達成した。
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著者: Jiho Lee, Nisar Ahmed, Kyle Hollins Wray, Zachary Sunberg
分類: cs.RO, cs.AI
原文アブストラクト
Online planning under uncertainty remains a fundamental challenge for robotic systems operating in partially observable environments with high-dimensional state spaces. While sampling-based POMDP solvers enable approximate decision-making in large or continuous domains, their performance degrades as belief dimensionality increases due to the high variance inherent in Monte Carlo-based estimation. In this work, we extend the Rao-Blackwellized online POMDP (RB-POMDP) framework to improve its generalizability in high-dimensional settings through hybrid continuous-discrete belief representations. By analytically propagating uncertainty associated with marginalized state components during tree-based planning, the proposed approach reduces sampling-induced variance in value estimation. We demonstrate the effectiveness of this framework in a robotic search-and-rescue task by integrating it with FastSLAM 2.0. Experimental results show that the proposed planner achieves higher cumulative rewards using significantly fewer particles and planning simulations than purely sampling-based methods under equivalent computational budgets. These results suggest that structured high-dimensional robotic problems admitting tractable sufficient statistics can be effectively leveraged within the RB-POMDP framework for computationally feasible online decision-making.