Research paper
How model learning changes a barrier safety bound
Projection-to-State Safety relates uncertainty in a barrier derivative to degradation of its guarantee. Episodic learning reduces that uncertainty; the source reports simulation and a physical Segway-platform experiment.
Reviewed 2026-11-05 · Source and bibliography checked
- Original title
- A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Authors
- Andrew J. Taylor, Andrew Singletary, Yisong Yue, Aaron D. Ames
- DOI
- 10.1109/lcsys.2020.3009082
- Journal issue date
- 2021-07
- Source language
- en
- Source country
- US
- Edition
- IEEE Control Systems Letters
- Access
- Publisher metadata and open author manuscript
- Publication status
- Published
Source overview
Projection-to-State Safety relates uncertainty in a barrier derivative to degradation of its guarantee. Episodic learning reduces that uncertainty; the source reports simulation and a physical Segway-platform experiment.
Use and review
Abstract and bibliographic review only. Guarantees depend on model, uncertainty and constraint assumptions; no ISO13849 safety function or complete installation was validated.
Sources & editions
A Control Barrier Perspective on Episodic Learning via Projection-to-State Safety ↗
Institute of Electrical and Electronics Engineers (IEEE) · IEEE Control Systems Letters
Archivev1abstract/authors explicitly link publicationDOI; primaryauthorPDF figure2caption/conclusion identifies physicalSegwayexperiment. IEEEControlSystemsLetters5(3),2021issue retained; firstonline date not inferred.