Robot Learning

Adversarial Robustness

Adversarial robustness is a model's ability to maintain correct behavior under inputs deliberately crafted or perturbed to cause failure, from imperceptible pixel perturbations to physical patches placed in a scene. Robotics extends the threat model beyond images to state estimates, LiDAR returns, and language instructions given to instruction-following policies. Defenses include adversarial training, input smoothing, randomization, and certified bounds, each trading nominal performance for worst-case guarantees.

Why it matters for physical AI

Robots act on their perception with physical consequences, so an adversarial sticker on a stop sign or workpiece is a safety incident rather than a benchmark curiosity, motivating robustness evaluation before deployment.