Long Horizon Resilience Optimization as an AI Challenge: A Soft Actuator Case Study

Most self-driving laboratories assume that an experiment soon returns a scalar objective. Resilience experiments break this assumption: each physical replicate occupies equipment for an unknown duration and yields a noisy trajectory, a delayed failure label, or a right-censored record. We frame long-horizon resilience optimization as an AI-for-materials challenge and use dielectric elastomer actuators as a concrete case. The problem couples design selection, operating protocol discovery, and early prediction. It also creates four AI challenges: learning from censored trajectories, separating design effects from replicate and batch variation, scheduling asynchronous tests under a laboratory-time budget, and optimizing lower-tail reliability rather than mean performance alone. Previously reported fabrication and testing platforms provide independent action and observation layers: repeatable multilayer production and programmable loading with coupled electrical and mechanical measurements. They are not an integrated self-driving laboratory. Our contribution is an experimentally grounded problem specification, data interface, and falsifiable roadmap for closing this loop.