  Existing methods for predicting robotic snap joint assembly cannot predict
failures before their occurrence. To address this limitation, this paper
proposes a method for predicting error states before the occurence of error,
thereby enabling timely recovery. Robotic snap joint assembly requires precise
positioning; therefore, even a slight offset between parts can lead to assembly
failure. To correctly predict error states, we apply functional principal
component analysis (fPCA) to 6D force/torque profiles that are terminated
before the occurence of an error. The error state is identified by applying a
feature vector to a decision tree, wherein the support vector machine (SVM) is
employed at each node. If the estimation accuracy is low, we perform additional
probing to more correctly identify the error state. Finally, after identifying
the error state, a robot performs the error recovery motion based on the
identified error state. Through the experimental results of assembling plastic
parts with four snap joints, we show that the error states can be correctly
estimated and a robot can recover from the identified error state.
