外观
论文arXiv 2610.1453018 天前
在多机器人系统中信任谁：通过神经符号交叉验证联合推断机器人与物体假设信任
原文链接：Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation（arXiv 2610.14530 · 发布日期：2026-10-10）
Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation
针对多机器人在部分可观测和潜在攻击下的信任推断，提出 NeST-Bayes，并在基准和 Webots 仓库中验证。
核心要点
- 多机器人协作需同时判断信息来源及其报告的物体假设可信度。
- NeST-Bayes 仅利用机器人间观测一致性，无需预设信任或侧信道身份信号。
- 方法在自中心关系图上推理证据分数，并用 Beta 贝叶斯更新累积信任。
- 评估显示其在所有基准设置取得最高平均物体精度和机器人召回。
验证范围与局限：摘要仅报告基准设置、优化攻击和遮挡丰富的 Webots 仓库评估，未述真机验证。
英文摘要 (Abstract)
Multi-robot systems (MRS) offer significant advantages over single-robot platforms by enabling information sharing and coordination for different tasks in complex environments. However, reliable cooperation critically depends on inferring trust over both information sources and their reported hypotheses, particularly under partial observability and potential adversarial interference. Existing approaches frequently depend on pre-established trust relationships or side-channel information, which may not be available or reliable in realistic MRS deployments. This work introduces NeST-Bayes, a neural-symbolic framework that jointly infers robot and hypothesis trust using only inter-robot observation consistency, without requiring preassigned robot trust, reputation information, or side-channel identity signals. NeST-Bayes infers instantaneous trust evidence scores for robots and object hypotheses by reasoning over ego-centric relational graphs constructed from cross-robot observation consistency. These evidence scores are accumulated over time through Bayesian updates of Beta-distributed trust beliefs to maintain temporally consistent trust estimates. Extensive evaluations show that NeST-Bayes achieves the highest mean object precision and robot recall across all benchmark settings, and the highest robot precision and object recall in most settings, compared to trust-based methods. Under aggressive optimized attacks, it achieves 98.8% robot precision with 93.8% recall and 98.3% object precision with 95.4% recall. In an occlusion-rich Webots warehouse, it maintains 92.5% robot precision with 92.0% recall and 97.3% object precision with 85.0% recall. These results demonstrate joint improvements in the reliability and coverage of retained information.
作者：Ruiyang Wang, Hao-Lun Hsu, Haocheng Meng, Songqiao Xie, Miroslav Pajić
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