Michael Laskey talks DART in Robohub podcast

EECS graduate student Michael Laskey (advisor: Ken Goldberg) is interviewed by Audrow Nash for a Robohub podcast titled “DART: Noise injection for robust imitation learning.”  Laskey works in the AUTOLAB where he develops new algorithms for Deep Learning of robust robot control policies and examines how to reliably apply recent deep learning advances for scalable robotics learning in challenging unstructured environments.  In the podcast, he discusses how DART relates to previous imitation learning methods, how this approach has been used for folding bed sheets, and on the importance of robotics leveraging theory in other disciplines.