多机器人主动推断中的精确融合与协调探索

中文摘要 AI 辅助

学习共同环境模型的机器人团队交换信念摘要，并根据其行动的预期信息增益进行规划。在共轭指数族信念下，共享信念在每个机器人处被计数两次：在融合时，局部后验的乘积将共同先验计数n次；在规划时，每个机器人都在同一信念下对其计划进行评分，团队收敛于同一未知量。通过向共享自然参数添加证据增量（融合时的已实现增量与规划时的预期增量），这两种误差均被消除。已承诺队友的预期增量给予下一个机器人其条件增益；修正后的增益之和等于联合增益，被移除的冗余等于计划观测流的总相关，顺序承诺保持1/2贪婪保证。对于具有固定采样路径的高斯信念，以及离散主动推断的新颖性近似下的狄利克雷信念，预期增量是精确的，其团队目标在精确互信息的显式界限内具有闭式凹形式；而对于有限假设类，该方法失效，此时用简短的精确枚举替代。在合作RockSample、觅食和野外监测上的实验表明，融合校正使探索冗余保持不变，预期证据将其移除，顺序承诺以与团队规模线性相关的成本恢复了集中式联合规划的大部分价值。

英文摘要

Robot teams that learn a common environment model exchange belief summaries and plan by the expected information gain of their actions. Under conjugate exponential-family beliefs the shared belief is counted once per robot at two points: at fusion, the product of local posteriors counts the common prior $n$ times, and at planning, every robot scores its plan under the same belief and the team converges on the same unknown. Both errors are removed by adding evidence increments to the shared natural parameter, realized increments at fusion and expected increments at planning. The expected increment of a committed teammate gives the next robot its conditional gain; corrected gains sum to the joint gain, the redundancy removed equals the total correlation of the planned observation streams, and sequential commitment keeps the $1/2$ greedy guarantee. The expected increment is exact for Gaussian beliefs with fixed sampling paths and for Dirichlet beliefs under the novelty approximation of discrete active inference, whose team objective has a closed concave form within an explicit bound of the exact mutual information, and fails for finite hypothesis classes, where a short exact enumeration replaces it. Experiments on cooperative RockSample, foraging, and field monitoring show that fusion correction leaves exploration redundancy unchanged, anticipated evidence removes it, and sequential commitment recovers most of the value of centralized joint planning at cost linear in the team size.

发表机构

• Northeastern University（东北大学）
• University of California, Irvine（加州大学尔湾分校）
• U.S. Department of Homeland Security（美国国土安全部）
• Oracle Corporation（甲骨文公司）

机构由 AI 辅助整理，请以论文原文为准。