  In the rapidly evolving field of autonomous systems, the safety and
reliability of the system components are fundamental requirements. These
components are often vulnerable to complex and unforeseen environments, making
natural edge-case generation essential for enhancing system resilience. This
paper presents GENESIS-RL, a novel framework that leverages system-level safety
considerations and reinforcement learning techniques to systematically generate
naturalistic edge cases. By simulating challenging conditions that mimic the
real-world situations, our framework aims to rigorously test entire system's
safety and reliability. Although demonstrated within the autonomous driving
application, our methodology is adaptable across diverse autonomous systems.
Our experimental validation, conducted on high-fidelity simulator underscores
the overall effectiveness of this framework.
