April 30, 2010
How Robots Learn to Reason
There is a very nice article from Ars Technica on how Robots learn to think. Largely it is a nice explanation of both Bayes rule and estimation in general. Still, it is a good reference for the lay person, and a decent look at how some of these things are implemented.
Today's robots are starting to be able to make these difficult determinations. The question we're here to answer is: how? What allowed robots to go from blind, dumb, immobile automatons to fully autonomous entities able to operate in unstructured environments like the streets of a city? The most obvious answer is Moore's Law, and it has certainly been a huge factor. But raw processing power is useless without the right algorithms. A revolution has taken place in the robotics world. By embracing uncertainty and using the tools of probability, robots are able to make sense of their surroundings like never before.
In this article, we'll explore how robots use their sensors to make sense of the world. This discussion applies mostly to robots that carry an internal representation of the world and act according to that representation. There are lots of successful robots that don't do such "thinking": the military's UAVs are mostly remotely piloted, linked by an electronic tether to human eyes and brains on the ground. The Roomba does its job without building a map of your house; it just has a series of simple behaviors that are triggered by timing or bumping into things. These robots are very good at what they do, but to autonomously carry out more complicated tasks like driving, a robot needs to have some understanding of the world around it. The robot needs to know where it is, where it can and can't go, and decide what to do and where to go. We'll be discussing how modern robots answer these questions.
Worth a look.