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English(EN) Exact Fusion and Coordinated Exploration in Multi-Robot Active Inference

多机器人主动推理方法改进协同探索

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的多机器人主动推理方法，使团队能够学习共同的环境模型并基于预期的信息增益来规划行动。该方法通过向共享的自然参数添加证据增量来纠正信念融合和规划中的错误。在RockSample和现场监测等合作任务上的实验表明，这种融合校正保持了探索冗余，而预期的证据则有效地减少了冗余，以可管理的成本恢复了集中规划的显著价值。 AI

影响 增强了多机器人系统的协同规划和探索能力，有望提高复杂任务的效率。

排序理由 关于多机器人主动推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.MA (Multiagent) 阅读 →

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多机器人主动推理方法改进协同探索

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关于多机器人主动推理新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Mahdi Imani ·

    多机器人主动推理中的精确融合与协同探索

    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…

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