Learning and Classification of Contact States in Robotic Assembly Tasks

Abstract

The application of connectionist learning techniques, namely unsupervised neural networks, to contact classification is investigated. This approach demonstrates the feasibility and appropriateness of using force sensing to solve this problem. Empirical results are provided for the chamferless two-dimensional peg-in-hole insertion model with friction. The advantages of learning approaches over geometric model-based techniques are discussed. Our neural network approach is simple but robust against unpredictable changes of task parameters, and it exhibits a gracefully degrading behavior and on-line adaptation to new task conditions.