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When deploying machine learning models in high-stakes robotics applications, the ability to detect unsafe situations is crucial.
IEEE Transactions on Robotics and Automation 11(4): 477–490
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Robotics and Autonomous Systems 68: 140–155
arXiv preprint arXiv:1604.03639
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IEEE Transactions on Automation Science and Engineering 15(1): 172–188
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Automatica 93: 476–488
Harirchi F and Ozay N (2018) Guaranteed model-based fault detection in cyber-physical systems: A model invalidation approach · 2018
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ACM Computing Surveys 51(1): 1–24
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Science Robotics 4(26)
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Crestani D, Godary-Dejean K and Lapierre L (2015) Enhancing fault tolerance of autonomous mobile robots · 2015
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Analysis and Design of Hybrid Systems 48(27): 260–266
Harirchi F and Ozay N (2015) Model invalidation for switched affine systems with applications to fault and anomaly detection · 2015
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Computers & Electrical Engineering 20(5): 421–435
Visinsky ML, Cavallaro JR and Walker ID (1994a) Expert system framework for fault detection and fault tolerance in robotics
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Reliability Engineering & System Safety 46(2): 139–158
Visinsky ML, Cavallaro JR and Walker ID (1994b) Robotic fault detection and fault tolerance: A survey
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Angelopoulos AN, Krauth K, Bates S, Wang Y and Jordan MI (2022) Recommendation systems with distribution-free reliability guarantees · 2022
Closest in time.
Annals of Statistics 51(2): 816–845
Barber RF, Candès EJ, Ramdas A and Tibshirani RJ (2023) Conformal prediction beyond exchangeability · 2023
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