Eagles and Algos: a Non-Technical Primer on Robot Learning
This is part 1 of a six part series. Links to the other articles in this series can be found at the bottom of this page.
Machine learning has recently become somewhat of a buzz word. The appendage of “ML” to any capability lends it an immediate halo of sophistication. But why has it really captured the zeitgeist? The answer lays in what computers have historically been able to do. For most of their existence, computers have done just two things: store copious amounts of data and compute complex calculations, with the latter being a tightly programmed output of the former. While impressive, there was always a missing piece: learning.
The belief was that if computers could learn, they could help out with menial tasks around the home or office and free up human resources for higher level tasks. After several false starts, sprinkled with spasmodic progress, the last two decades have yielded some seminal research in making this dream a reality. This series is an attempt to take a closer look at some of these works, particularly three emergent learning models and one novel technique being used to help computers become better learners. We will also briefly explore how these models are applied to robots and finally, examine the implications of real world use cases.