Before revision:  We show this in experiments with a  7DOF robot manipulator . Finally, we discuss our method's potential implications for deep reward learning more broadly: taking a divide-and-conquer approach that focuses on important features separately before learning from demonstrations can improve generalization in tasks where such features are easy for the human to teach .
After revision:  We show this in experiments with a 7DOF robot manipulator , as well as in a user study conducted in a simulated environment .