Early failure prediction during robotic assembly using Transformers

Anonymous

Keywords: robotic assembly, failure classification, Transformer neural networks

TL;DR: Early failure detection during robotic assembly using transformer neural networks for makespan reduction

Abstract: Peg-in-hole assembly of tightly fitting parts often requires multiple attempts. Parts need to be put together by performing a wiggling motion of undetermined length and can get stuck, requiring a restart. Recognizing unsuccessful insertion attempts early can help in reducing the \emph{makespan} of the assembly. This can be achieved by analyzing time-series data from force and torque measurements. We describe a transformer neural network model that is three times faster, i.e. requiring much shorter time series, for predicting failure than a dilated fully convolutional neural network. Albeit the transformer provides predictions with higher confidence, it does so at reduced accuracy. Yet, being able to call unsuccessful attempts early, makespan can be reduced by almost 40% which we show using a dataset with force-torque data from 241 peg-in-hole assembly runs with known outcomes.