arXiv:2408.01273v3 Announce Type: replace Abstract: We propose a framework for training neural network controllers with certified robust forward invariant polytopes. First, we parameterize a family of lifted control systems in a higher dimensional space, where the original neural controlled system evolves on an invariant subspace of each lifted system. We use interval analysis and neural network verifiers to further construct a family of lifted embedding systems, carefully capturing the knowledge of this invariant subspace. If the vector field of any lifted embedding system satisfies a sign co
The paper builds on recent advancements in neural ODEs and interval analysis, pushing the boundaries of certified robustness in AI control systems.
This research addresses a critical need for verifiable safety and reliability in autonomous AI systems, which is essential for their deployment in sensitive applications.
The ability to formally certify the robustness of neural network controllers for forward invariant polytopes enhances trust and enables broader adoption of AI in safety-critical domains.
- · AI safety researchers
- · Autonomous systems developers
- · Robotics industry
- · Cyber-physical systems
- · Developers of unverified AI control systems
Increased reliability and trustworthiness of AI-controlled systems in industrial or defense applications.
Accelerated adoption of AI in areas requiring high safety assurance, like aerospace or medical devices.
Potentially, a shift towards regulatory frameworks that mandate certified robustness for AI in critical infrastructure.
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Read at arXiv cs.LG