Newest papers

Safe Multi-Robot Collaboration With Uniform Exclusion Using Trust

Thomas Kaminsky, Áron Vèkássy, Hammad Izhar, Angelia Nedić, Stephanie Gil · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28251 · cited by 0 works (OpenAlex) · open-access PDF

Portuguese

Envolvedores de robôs coordenam através de links de comunicação locais direcionados, variando o tempo, alguns dos quais podem ser comprometidos para permitir a transmissão de dados maliciosos arbitrários. Nós fornecemos um algoritmo que atua como um envelope em torno da comunicação robótica---quando um robô procura atualizar seu estado usando dados de alguns links de entrada, nosso envelope fornece um subconjunto de links confiáveis cujos dados podem ser seguramente incorporados. Ele seleciona esses links testando um fluxo de valores de confiança válidos---dados coletados de um detector de anomalias em cada robô---us usando Safe Anytime-Valid Inference. Nós exigimos apenas que o detector seja melhor do que a chance. Este envelope garante autonomia segura e eficaz fornecendo duas poderosas garantias estatísticas

Machine translation (Meta M2M100); the English original follows.

Abstract

Robot swarms coordinate through directed, time-varying local communication links, some of which may be compromised to allow the transmission of arbitrary malicious data. We provide an algorithm that acts as a wrapper around robot communication---when a robot seeks to update its state using data from some incoming links, our wrapper provides a subset of trusted links whose data can be safely incorporated. It selects these links by testing a stream of trust values---data collected from an anomaly detector on each robot---using Safe Anytime-Valid Inference. We require only that the detector be better than chance. This wrapper ensures safe and performant autonomy by providing two powerful statistical guarantees: it uniformly excludes all compromised links for all time with high user-chosen probability, but nevertheless quickly learns to trust legitimate links. Both guarantees are vital to secure generic tasks, but no previous work provides them simultaneously without restrictive assumptions on connectivity structure or the number of compromised links that are not guaranteed in practice. We study our Safe Wrapper's performance in hardware for two contrasting tasks---neural implicit mapping and heading consensus---deploying on 3 LoCoBots and 8 GoPiGos, respectively. We provide simulations using trust values sampled from hardware to validate that our approach scales well to swarms of hundreds of robots.

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Topics: Computer science, Anomaly detection, Robot, State (computer science), Computer security, Heading (navigation)

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