Cantilever snap assemblies failure detection using SVMs and the RCBHT

Failure detection plays an increasingly important role in industrial processes and robots that serve in unstructured environments. This work studies failure detection on cantilever snap assemblies, which are critical to industrial use and growing in importance for personal use. Our aim is to study whether an SVM can use a small set of features abstracted as behavior representations from the assembly force signature to accurately detect failure at different stages of the task. In this work, a linear SVM was embedded with abstracted behavioral features to classify failure detection in cantilever snap assembly problems. The approach was useful in detecting failure offline during early and late stages of the task. For early stages, low-abstraction behaviors sets performed better due to their granularity and local temporal nature. For late stage analysis, high-abstraction behaviors performed better due to their coarse and global representations.