May 11, 2021 10:13 PM
https://arstechnica.com/science/2021/05/...w-to-move/
EXCERPTS: One of the most impressive developments in recent years has been the production of AI systems that can teach themselves to master the rules of a larger system. Notable successes have included experiments with chess and Starcraft. Given that self-teaching capability, it's tempting to think that computer-controlled systems should be able to teach themselves everything they need to know to operate. Obviously, for a complex system like a self-driving car, we're not there yet. But it should be much easier with a simpler system, right?
Maybe not. A group of researchers in Amsterdam attempted to take a very simple mobile robot and create a system that would learn to optimize its movement through a learn-by-doing process. While the system the researchers developed was flexible and could be effective, it ran into trouble due to some basic features of the real world, like friction.
[...] The robots in the study were incredibly simple and were formed from a varying number of identical units. Each had an on-board controller, battery, and motion sensor. ... While simple, the system provides some insights into how we might think about self-teaching systems. And the experiment reminds us that the real world will throw even the best self-teaching system a few curves... (MORE - details)
EXCERPTS: One of the most impressive developments in recent years has been the production of AI systems that can teach themselves to master the rules of a larger system. Notable successes have included experiments with chess and Starcraft. Given that self-teaching capability, it's tempting to think that computer-controlled systems should be able to teach themselves everything they need to know to operate. Obviously, for a complex system like a self-driving car, we're not there yet. But it should be much easier with a simpler system, right?
Maybe not. A group of researchers in Amsterdam attempted to take a very simple mobile robot and create a system that would learn to optimize its movement through a learn-by-doing process. While the system the researchers developed was flexible and could be effective, it ran into trouble due to some basic features of the real world, like friction.
[...] The robots in the study were incredibly simple and were formed from a varying number of identical units. Each had an on-board controller, battery, and motion sensor. ... While simple, the system provides some insights into how we might think about self-teaching systems. And the experiment reminds us that the real world will throw even the best self-teaching system a few curves... (MORE - details)