Reproduce “Who to Trust in Multi-Robot Systems: Joint Robot and Object Hypothesis Trust Inference via Neural-Symbolic Cross-Validation” on OpenResearch
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About this paper
Ruiyang Wang, Hao-Lun Hsu, Haocheng Meng, Songqiao Xie, Miroslav Pajic
NeST-Bayes evaluates robot reports and object hypotheses in multi-robot teams by learning how typed cross-robot agreement and contradiction relations combine, then accumulating evidence over time. In 100 scenarios under an aggressive optimized attack, it reports mean robot precision/recall of 98.8%/93.8% and object precision/recall of 98.3%/95.4%. Robot discrimination is limited under conservative attacks that minimize observable disagreement.
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