A problem within the AI domain is that Vision has not been solved yet.
The existing and functioning algorithms are mainly specialised into
sub domains like face recognition etc. These are very nice but are not
general enough to use in an arbitrary environment.
Fear not, for general purpose vision systems are on the way. I'm working on general purpose vision at the moment, for a company in the US. The materials transport robots produced by this company have the first general purpose vision system, consisting of a few stereo cameras, and it works remarkably well (reliably enough to navigate over long distances, within a tollerance of about 3-5cm). Basically what the cameras see is reverse engineered into a 3D model, not unlike the type of thing which you might find in a computer game. Once this kind of information is available many tasks become significantly easier than would be the case using other sensors, such as lasers or ultrasonics. I expect this technology to get cheaper and within a few years be within the realm of consumer applications. I should say that these vision systems will work in *any* environment, within the limitations of the dynamic range of the cameras. The only situations where visual navigation doesn't work are those were the environment is changing constantly, so that there's no repeatability in what the robot sees, but otherwise they can cope with quite substantial environmental changes (people or other vehicles wandering around).
I've not read Hawkins book, but in the lectures I've seen by him the kinds of vision which he demonstrates are extremely simple "toy" problems using binary images. It's also a very 2D approach, and yet we live in a fundamentally 3D world. I would be very surprised if this kind of approach worked with real camera images and all their inherent complexity and uncertainty.
- Bob
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