  The adoption of cyber-physical systems (CPS) is on the rise in complex
physical environments, encompassing domains such as autonomous vehicles, the
Internet of Things (IoT), and smart cities. A critical attribute of CPS is
robustness, denoting its capacity to operate safely despite potential
disruptions and uncertainties in the operating environment. This paper proposes
a novel specification-based robustness, which characterizes the effectiveness
of a controller in meeting a specified system requirement, articulated through
Signal Temporal Logic (STL) while accounting for possible deviations in the
system. This paper also proposes the robustness falsification problem based on
the definition, which involves identifying minor deviations capable of
violating the specified requirement. We present an innovative two-layer
simulation-based analysis framework designed to identify subtle robustness
violations. To assess our methodology, we devise a series of benchmark problems
wherein system parameters can be adjusted to emulate various forms of
uncertainties and disturbances. Initial evaluations indicate that our
falsification approach proficiently identifies robustness violations, providing
valuable insights for comparing robustness between conventional and
reinforcement learning (RL)-based controllers
