Conference Proceedings

Statistical Data Cleaning for Deep Learning of Automation Tasks from Demonstrations

Human demonstrators of deep learning tasks are prone to inconsistencies and errors that can delay or degrade learning. We improve task performance by characterizing supervisor inconsistency and correcting for it using data cleaning techniques. In human demonstrations of a planar part extraction task on a 2DOF robot, trained CNN models show an improvement of 11.2% in mean absolute success rate after data cleaning.

Caleb Chuck, Michael Laskey, Sanjay Krishnan, Ruta Joshi, Roy Fox, and Ken Goldberg, CASE, 2017