Published: September 19, 2026
For decades, robot safety was evaluated through a relatively straightforward lens: Can a machine operate reliably, and what happens when a physical component fails? Engineers built safety loops, emergency stops, and redundant hardware to prevent hardware failures from causing physical harm. However, as robots transition into "Physical AI" systems—harnessing deep learning, multimodal sensors, and real-time decision-making models—this classical safety paradigm is no longer sufficient.
Today's autonomous systems do not just execute pre-programmed paths; they perceive their surroundings, interpret context via vision-language-action (VLA) models, and translate those digital thoughts into physical actions. This reliance on a complex data pipeline introduces a critical vulnerability. What happens when a robot's hardware operates perfectly, but its perception, reasoning, or communication channels are subtly manipulated by an external actor?
