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

Suet Lee and Sabine Hauert. 2023. Building Trustworthiness by Minimizing the Sim-to-Real Gap in Fault Detection for Robot Swarms. In Proceedings of the First International Symposium on Trustworthy Autonomous Systems (TAS '23). Association for Computing Machinery, New York, NY, USA, Article 47, 1–3. doi: 10.1145/3597512.3597527

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.