Task-Driven Detection of Distribution Shifts With Statistical Guarantees for Robot Learning

Our goal is to perform out-of-distribution (OOD) detection , i.e., to detect when a robot is operating in environments drawn from a different distribution than the ones used to train the robot. We leverage probably approximately correct-Bayes theory to train a policy with a guaranteed bound on perfo...

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Vydané v:	IEEE transactions on robotics Ročník 41; s. 926 - 945
Hlavní autori:	Farid, Alec, Veer, Sushant, Pachisia, Divyanshu, Majumdar, Anirudha
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 2025
Predmet:	Collision avoidance Costs Deep learning in robotics and automation Detectors Drones failure detection and recovery formal methods in robotics and automation Grasping Hardware Navigation PAC-Bayes Robot sensing systems Robots Training
ISSN:	1552-3098, 1941-0468
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