Safety Validation of Learning-Based Autonomous Systems: A Multi-Fidelity Approach
DOI:
https://doi.org/10.1609/aaai.v37i13.26799Keywords:
New Faculty HighlightsAbstract
In recent years, learning-based autonomous systems have emerged as a promising tool for automating many crucial tasks. The key question is how we can build trust in such systems for safety-critical applications. My research aims to focus on the creation and validation of safety frameworks that leverage multiple sources of information. The ultimate goal is to establish a solid foundation for a long-term research program aimed at understanding the role of fidelity in simulators for safety validation and robot learning.Downloads
Published
2024-07-15
How to Cite
Baheri, A. (2024). Safety Validation of Learning-Based Autonomous Systems: A Multi-Fidelity Approach. Proceedings of the AAAI Conference on Artificial Intelligence, 37(13), 15432-15432. https://doi.org/10.1609/aaai.v37i13.26799
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New Faculty Highlights