What is representation learning?
If you look at older machine learning algorithms, they rely on the input being a feature and learn a classifier, regressor, etc on top of that. Most of these features were hand crafted, meaning, they were designed by humans. Classical examples of features in computer vision include Harris, SIFT, LBP, etc . 

The problem with these is that they were designed by humans based on heuristics. Images can be represented using these features and ML algorithms can be applied on top of that. However, they may not be the most optimal in terms of the objective function, i.e, it may be possible to design better features that can lead to lower objective function values. Instead of hand crafting these image representations, we can learn them. That is known as representation learning. We can have a neural network which takes the image as an input and outputs a vector, which is the feature representation of the image. This is the representation learner. This be followed by another neural network that acts as the classifier, regressor, etc . 

So, in the wheel example, you can try to manually describe how a wheel should look like and how it can be represented. Say, it should be circular, be black in colour, have treads, etc. But these are all hand crafted features and may not generalize to all situations. For example, if you look at the wheel from a different angle, it might be oval in shape. Or the lighting may cause it to have lighter and darker patches. These kinds of variations are hard to account for manually. Instead, we can let the representation learning neural network learn them from data by giving it several positive and negative examples of a wheel and training it end to end. 

Hope this makes sense. Let me know if I can clarify any point I made.