We know how to build smarter robots. Now, we need to learn smarter ways to test them

As robots become more autonomous and capable of real-time decision-making without human control, the testing methods used to validate their safety have not kept pace with their advancing capabilities. Current safety validation frameworks, such as failure mode and effects analysis, were designed for traditional software systems and struggle to account for the emergent and context-dependent failure modes that arise in AI-driven systems. The industry needs new testing philosophies that scale alongside autonomy levels, moving from simple test case enumeration toward formal safety guarantees and adversarial robustness evaluation. This matters because autonomous robots capable of physical force are already commercially available without standardized safety certification, creating a gap between what these systems can do and the frameworks available to validate their behavior responsibly.