自律科学探査のためのPOMDP
POMDPs for Autonomous Science Exploration
センサ不確実性下での科学探査計画を、生のセンサデータではなく推論された物理特性に基づいて分岐するPOMDP（SHM-POMDP）を導入し、計算効率と報酬を改善した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Daniel Guirguis, Nathan Wallace, Hanna Kurniawati, Salah Sukkarieh
分類: cs.RO
原文アブストラクト
Autonomous exploration missions require decision-making under sensor uncertainty and computational constraints, yet integrating scientific representations into POMDP planning has remained intractable due to high-dimensional observation spaces. Information-theoretic planners overcome this by assuming deterministic observations, sacrificing the principled uncertainty quantification that POMDPs provide. We introduce the Science Hypothesis Map POMDP (SHM-POMDP), which makes science-driven belief-space planning more tractable by branching on inferred physical properties rather than raw sensor data. This preserves full sensor information through learned observation models while enabling the planner to reason jointly about navigation and scientific properties under uncertainty. On an extended RockSample domain with 50-dimensional observations, SHM-POMDP achieves 18.6\% higher rewards and 32.9\% reduced computation time per step than continuous-observation baselines. On realistic geologic exploration using Cuprite hyperspectral data, SHM-POMDP achieves 2.5$\times$ higher information gain than the best information-theoretic baseline by maintaining beliefs and replanning adaptively---reaching 80\% of oracle performance using only uniform priors. These results demonstrate that integrating hierarchical probabilistic models into belief-space planning enables tractable, principled autonomous science that outperforms both traditional POMDP methods and science-aware information-theoretic approaches.
