Data-Efficient Process Monitoring and Failure Detection for Robust Robotic Screwdriving

Abstract

Screwdriving is one of the most prevalent assembly methods, yet its full automation is still challenging, especially for small screws. A critical reason is that existing techniques perform poorly in process monitoring and failure prediction. In addition, most solutions are essentially data-driven, thereby requiring lots of training data and laborious labeling. Moreover, they are not robust against varying environment conditions and suffer from generalization issues. To this end, we propose a stage and result prediction framework that combines knowledge-based process models with a hidden Markov model. The novelty of this work is the incorporation of operation-invariant characteristics such as screwdriving mechanics and stage transition graph, enabling our system to generalize across different experimental settings and largely reduce the required data and labeling. In our experiments, a system trained on M1.4x4 screws adapted with very little non-labeled data to three other screws (M1.2x3, M2.5x5, and M1.4x4) with widely varying tightening current, motor velocity, insertion force, and tightening force.