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Research paper

Bayesian model learning for rover-style safety control

Bayesian dynamics learning feeds stochastic Lyapunov and barrier constraints to adapt under model uncertainty. The abstract presents terrestrial mobility experiments aimed at possible high-speed rover applications.

Reviewed 2026-11-05 · Source and bibliography checked

Bibliographic details and the editorial overview were checked against the original source. Access conditions and editions are identified with the source.
Original title
Bayesian Learning-Based Adaptive Control for Safety Critical Systems
Publisher
IEEE
Authors
David D. Fan, Jennifer Nguyen, Rohan Thakker, Nikhilesh Alatur, Ali-akbar Agha-mohammadi, Evangelos A. Theodorou
DOI
10.1109/icra40945.2020.9196709
Conference date
2020-05
Source language
en
Edition
2020 IEEE International Conference on Robotics and Automation (ICRA)
Access
Publisher metadata and open author manuscript
Publication status
Published

Source overview

Bayesian dynamics learning feeds stochastic Lyapunov and barrier constraints to adapt under model uncertainty. The abstract presents terrestrial mobility experiments aimed at possible high-speed rover applications.

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

Bayesian Learning-Based Adaptive Control for Safety Critical Systems ↗

IEEE · 2020 IEEE International Conference on Robotics and Automation (ICRA)

Archivev3dated2021-07-04abstract/authors explicitly link ICRA2020publisherDOI. PossibleMarsroverapplication is an intendeduse, not a Marsmissiondeployment.