Building Trustworthiness by Minimizing the Sim-to-Real Gap in Fault Detection for Robot Swarms

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

Abstract

As robot swarm applications move to the real-world, ensuring the safety of such systems will be critical for trust and adoption. Fault detection is an essential component in systems which require a level of safety. Previous work has identified metrics with high discriminatory power between faulty and normal states of a robot in the swarm. The method for identifying such metrics has been implemented in simulation. Here, we implement metric extraction in a real-world environment and evaluate whether the extracted metrics can overcome the “sim-to-real gap” - in other words how well it transfers from simulation to a real-world setting.
Original languageEnglish
Title of host publicationTAS '23: Proceedings of the First International Symposium on Trustworthy Autonomous Systems
PublisherAssociation for Computing Machinery (ACM)
Pages1-3
Number of pages3
ISBN (Electronic)979-840070734-6
DOIs
Publication statusPublished - 11 Jul 2023

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