Conferences, Workshops, and Symposia

Statistical Data Cleaning for Deep Learning of Automation Tasks from Demonstrations

We explore how characterizing supervisor inconsistency and correcting for this noise can improve task performance with a limited budget of data. In a planar part extraction task where human operators provide demonstrations by teleoperating a 2DOF robot, CNN models perform better when trained after error corrections.

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