Not all of these are compatible with “and so the robot does the thing that the human does 5% of the time”. But it seems like there can and probably will be factors that are different between the human and the robot (even if the human uses teleoperation), and in that setting imitating factored cognition provides the wrong incentives, while optimizing factored evaluation provides the right incentives.
There are lots of reasons that a robot might be unable to learn the correct policy despite the action space permitting it:
Not enough model capacity
Not enough training data
Training got stuck in a local optimum
You’ve learned from robot play data, but you’ve never seen anything like the human policy before
etc, etc.
Not all of these are compatible with “and so the robot does the thing that the human does 5% of the time”. But it seems like there can and probably will be factors that are different between the human and the robot (even if the human uses teleoperation), and in that setting imitating factored cognition provides the wrong incentives, while optimizing factored evaluation provides the right incentives.