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It's a useful debate to be having -- although it is somewhat endless and undecidable in absolute terms, in the way that fundamental philosophical debates are.

It's a useful debate to be having -- although it is somewhat endless and undecidable in absolute terms, in the way that fundamental philosophical debates are. I think about robotics as an example of a problem domain that seems resistant to pure-learning approaches. I'm thinking about the whole perception-planning-execution problem, in which a map of the world, and a notion of a goal, are key modeling objects. It's really hard to think about how a robotic system that doesn't maintain a map could work intelligently to accomplish navigation goals, for instance. A map seems to imply some kind of model. And if the world (the map) isn't static, the modeling problem seems to get even more urgent.

dbcurtis 7y ago
> It's really hard to think about how a robotic system that doesn't maintain a map could work intelligently to accomplish navigation goals, For navigation with less map input, see the work of Oussama Khatib. A single nav goal is not a problem. A sequence of nav goals is where the planner adds value. Pure Sense-Plan-Act has long since been abandoned. Brooks' paper "Elephants Don't Play Chess" is a fun and easy read, and even though old, is still on the must-read list if you want to think about these problems. These days the "three layer architecture" is common in some form: classic controllers close to the hardware, a reactive layer, and a deliberative layer. If you squint your eyes the typical ROS-based robot is more-or-less three-layer. The default nav stack has a reactive Khatib-style layer executing nav sub-goals dropped into it from a planner that does map searches. Pure world-modelling is too slow and error-prone in the short term. Pure reactive is unwieldy with complex sequences of sub-goals.