arXiv:2606.11474v1 Announce Type: new Abstract: In this paper, we study Mahalanobis-guided latent out-of-distribution (OOD) detection for test-time RL controller switching in nonlinear time-varying systems. RL controllers can quickly control high-dimensional systems within the training distribution, but their performance can degrade when time-varying dynamics produce unseen observations. We consider a combined ES--DRL controller, where RL provides fast in-distribution actions and bounded extremum seeking (ES) provides robust model-independent control under OOD operation. The key challenge is d
The increasing complexity and dynamism of real-world AI applications, particularly in control systems, necessitates robust solutions for managing out-of-distribution scenarios to ensure reliable performance.
This development addresses a critical vulnerability in AI control systems, specifically their performance degradation in unseen conditions, which is vital for deploying AI in sensitive, time-varying environments.
The ability to reliably detect and adapt to out-of-distribution conditions at test-time means AI-controlled systems can operate more safely and effectively in dynamic, unpredictable environments previously considered too risky for full AI autonomy.
- · AI-driven control system developers
- · Robotics and autonomous vehicle manufacturers
- · Industrial automation sector
- · Aerospace and defense industries
- · Developers of brittle, non-adaptive AI control systems
- · Sectors reliant on purely in-distribution AI models
Improved reliability and safety metrics for AI-controlled systems in complex dynamic environments.
Accelerated adoption of AI in critical infrastructure and high-stakes autonomous applications due to enhanced robustness.
New regulatory frameworks and certification processes will emerge to validate OOD detection and control capabilities in autonomous systems.
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Read at arXiv cs.LG