用于自主科学探测的POMDP
POMDPs for Autonomous Science Exploration
浏览论文内容
中文总结 AI 辅助
该研究提出SHM-POMDP模型，解决高维观测下POMDP规划融入科学表征的难题，在RockSample域和Cuprite地质探测任务中，其性能优于传统POMDP与信息论基线方法。
中文摘要 AI 辅助
自主探测任务需要在传感器不确定性和计算约束下进行决策，但由于观测空间维度高，将科学表征融入POMDP规划一直难以实现。信息论规划器通过假设观测是确定性的来解决该问题，但牺牲了POMDP提供的原则性不确定性量化能力。我们提出科学假设图POMDP（SHM-POMDP），该方法通过基于推断的物理属性而非原始传感器数据进行分支，使科学驱动的信念空间规划更易处理。其通过学习到的观测模型保留了完整的传感器信息，同时使规划器能够在不确定性下联合推理导航和科学属性。在具有50维观测的扩展RockSample域中，SHM-POMDP的奖励比连续观测基线高18.6%，每步计算时间减少32.9%；在使用Cuprite高光谱数据的实际地质探测中，SHM-POMDP通过维护信念并自适应重规划，信息增益比最佳信息论基线高2.5倍，仅使用均匀先验就达到了80%的神谕性能。这些结果表明，将分层概率模型融入信念空间规划，可实现比传统POMDP方法和感知科学的信息论方法更优的易处理、原则性自主科学。
英文摘要
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.
