The algorithm has a major limitation: it's supervised learning, which means it needs big data sets for learning. And to create big learning sets, you need first

The algorithm has a major limitation: it's supervised learning, which means it needs big data sets for learning. And to create big learning sets, you need first to solve the problem that you are teaching the network how to solve. In this instance, the authors needed first to solve the problem of block stacking so that they could generate a learning set, which they used to learn how to stack the blocks. This issue is general to this method: Let's say you want to create a maid robot, you would have first to solve all the problems relating its duties (opening a door, cleaning the table, grasping a bottle ...), before you could train this network to solve these problems. To resolve this limitation we need either to create deep networks that do not need big data sets, which does not seem possible right now. Or we try to factorize the problem differently: for example, we separate learning how the control of the robot (e.g. how to move something for point a to b) and learning how to abstractly solve the problem (in this case, learning that you need to stack the blocks in a certain order).