发表机构
TU Eindhoven; Cajal Centre for Neuroscience, Spanish National Research Council（埃因霍温理工大学; 西班牙国家研究委员会卡哈尔神经科学中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出将主动推理的期望自由能作为统一准则，用于预算约束下的机器人信息路径规划，其规划方法在火星探测场景中可同时实现精准地图构建与高价值区域定位，性能优于信息论基准方法。
AI 中文摘要
自主机器人在未知环境（如寻找火星水源）中高效探测时，需同时满足两项需求：构建精准的信息地图，以及快速定位高价值区域，同时还要考虑每米行程和每次测量的成本。经典的信息寻求准则和奖励寻求准则仅能分别处理其中一项目标。本文提出主动推理中的原则性动作选择目标——期望自由能（Expected Free Energy, EFE），作为预算约束下机器人信息路径规划的统一准则。智能体基于信息场维持高斯过程信念，规划连续轨迹，在路径长度的硬约束下最小化期望自由能。多次实验结果表明，基于EFE的规划可同时生成精准的后验地图并定位最高价值区域，在相同设置下优于信息论基准方法。在机器人探测任务中，这类统一、易调参的原则性信息收集策略可促进自主部署，同时满足效率和资源约束。
英文摘要
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.
Commentsaccepted for IWAI 2026
