Yann LeCun observes that there is no demonstration of hierarchical planning in AI where the various levels of representations necessary have been learned.
We can do like two level hierarchical planning when we design the two levels. So for example, you have like a dog legged robot, right? You want it to go from the living room to the kitchen. You can plan a path that avoids the obstacle. And then you can send this to a lower level planner that figures out how to move the legs to kind of follow that trajectories, right? So that works, but that two level planning is designed by hand, right? We specify what the proper levels of abstraction, the representation at each level of abstraction have to be. How do you learn this? How do you learn that hierarchical representation of action plans, right? With com nets and deep learning, we can train the system to learn hierarchical representations of percepts. What is the equivalent when what you're trying to represent are action plans?