Estimating Neural Network Robustness via Lipschitz Constant and Architecture Sensitivity
This addresses the need for safer robot learning systems by providing a method to estimate robustness, though it appears incremental as it builds on existing Lipschitz constant theory.
This paper tackles the problem of neural network robustness in robotic perception systems by using the Lipschitz constant as a metric to quantify sensitivity to small perturbations, deriving an analytical expression to compute it based on architecture and showing experimental relationships between network design, Lipschitz constant, and robustness.
Ensuring neural network robustness is essential for the safe and reliable operation of robotic learning systems, especially in perception and decision-making tasks within real-world environments. This paper investigates the robustness of neural networks in perception systems, specifically examining their sensitivity to targeted, small-scale perturbations. We identify the Lipschitz constant as a key metric for quantifying and enhancing network robustness. We derive an analytical expression to compute the Lipschitz constant based on neural network architecture, providing a theoretical basis for estimating and improving robustness. Several experiments reveal the relationship between network design, the Lipschitz constant, and robustness, offering practical insights for developing safer, more robust robot learning systems.