这篇论文把主动推理的EFE用到火星车探路上，一套准则同时搞定画地图和找目标，比纯信息论方法更省路。
该论文提出用主动推理中的预期自由能（EFE）作为统一准则，解决机器人在未知环境（如火星找水）中同时构建信息地图和寻找高价值区域的问题。方法基于高斯过程维护信息场信念，并在硬路径长度约束下规划连续轨迹。多组实验显示，EFE规划在相同条件下优于信息论基线，能同时获得准确后验地图并定位高价值区域。该策略易于调参，适合自主部署并满足效率与资源约束。
Expected Free Energy-based Informative Path Planning for Robotic Mars Exploration
An autonomous robot efficiently exploring an unknown environment, such as looking for water sources on Mars, faces two simultaneous demands: building an accurate information map while quickly finding the regions of greatest value, and paying for every meter of travel and the cost of every measurement it takes. Classical information-seeking and reward-seeking criteria address only one of these objectives at a time. Here, we propose Expected Free Energy (EFE), the principled action-selection objective from active inference, as a unifying criterion for budgeted robotic informative path planning. Maintaining a Gaussian-process belief over the information field, our agent plans continuous trajectories that minimize expected free energy under hard path-length constraints. The results from multiple realizations show that EFE-based planning yields accurate posterior maps and locates the highest-value regions simultaneously, outperforming information-theoretic baselines under the same settings. In robotic exploration, these unified, easy-to-tune principled information-gathering strategies facilitate autonomous deployment while enforcing efficiency and resource constraints.