The biggest problem in robotics is vision. How do you translate pixels to a 3D scene graph with objects attributes and correlate with prior knowledge.

Do that in real time, on device without using a crazy amoubt of power because of batteries.

CNNs and faster GPUs are the biggest breakthrough in that regards but it's still a long way to go before we get to human level visual cortex.

Vision is part of the puzzle--a large part in the case of self-driving cars. But blind people are way better than computers at everyday tasks, so I don't think that it's the Big Problem.

Translating to 3D is low-level and relatively easy. That's not the reason why we don't have household robots/self-driving cars.

Framing vision as "object attributes" and "correlate to prior knowledge" might be a good approach for current research. But humans do more--we understand what we look at. We form concepts and models of the world that allow us to adapt to very novel situations.

The main reason why we haven't solved vision, language, playing chess like a human, etc is that NNs are a poor approximation of human concepts. I agree that we probably need more compute and better compute.