Presents a method to dynamically adapt probabilistic models for autonomous systems outside their ODD with formal verification and guarantees on improved reliability.
Digital twin-based out-of-distribution detection in autonomous vessels, 2025
3 Pith papers cite this work. Polarity classification is still indexing.
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ODiSAR uses a Transformer digital twin with reconstruction error and Monte Carlo dropout to detect OOD events in self-adaptive robots, reporting up to 98% AUROC on office navigation and maritime ship tasks.
This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.
citing papers explorer
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Formally Guaranteed Control Adaptation for ODD-Resilient Autonomous Systems
Presents a method to dynamically adapt probabilistic models for autonomous systems outside their ODD with formal verification and guarantees on improved reliability.
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Out of Distribution Detection in Self-adaptive Robots with AI-powered Digital Twins
ODiSAR uses a Transformer digital twin with reconstruction error and Monte Carlo dropout to detect OOD events in self-adaptive robots, reporting up to 98% AUROC on office navigation and maritime ship tasks.
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Software Engineering for Self-Adaptive Robotics: A Research Agenda
This paper proposes a research agenda for software engineering of self-adaptive robotic systems along lifecycle stages and enabling technologies, identifying challenges and a roadmap to 2030.