Before revision:  By learning the subtask policies together in an end-to-end fashion, our formulation not only learns coordination between subtask behaviors, but also learns how to trade them off against default behaviors that may exist .
After revision:  By learning the subtask policies together in an end-to-end learning objective function that is suitable for the multi-task problem, emphasizing the deviation of motions on task spaces .