Learning Stack Stability through Physical Perturbation Tests

Stack stability concerns whether an arrangement of physical objects will remain intact. Physics-based simulations require detailed scene parameters, such as friction coefficients which are often unavailable, while visual classifiers predict stability labels without understanding how failure develops. We propose StackProbe, which learns a state representation from images and available records, by observing how stacks respond to various physical perturbations. Then we can assess stack stability based on the learned state representation. During training, we test the learned state representation under different physical perturbations and refine it using responses from high-fidelity simulations. At inference, we use the learned state representation to predict whether, when, and how a stack may fail, without running a simulator. We evaluate StackProbe on two simulation benchmarks and data from a deployed industrial palletizing system. Empirical results show that StackProbe reduces missed-failure rates by over 32% compared with the strongest learned baseline, while its representation improves average precision for industrial collapse prediction by at least 6.0 percentage points. Code is available at https://anonymous.4open.science/r/StackProbe/.