AI alert: robot helicopters teach themselves to fly by OBSERVING OTHER HELICOPTERS FLY!

[JohnWxy]

http://www.physorg.com/news139501522.html

Computer Science Professor Andrew Ng (center) and his graduate students Pieter Abbeel (left) and Adam Coates have developed an artificial intelligence system that enables these helicopters to perform difficult aerobatic stunts on the their own. The “autonomous” helicopters teach themselves to fly by watching the maneuvers of a radio control helicopter flown by a human pilot.
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The dazzling airshow is an important demonstration of "apprenticeship learning," in which robots learn by observing an expert, rather than by having software engineers peck away at their keyboards in an attempt to write instructions from scratch.

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Stanford's artificial intelligence system learned how to fly by "watching" the four-foot-long helicopters flown by expert radio control pilot Garett Oku. "Garett can pick up any helicopter, even ones he's never seen, and go fly amazing aerobatics. So the question for us is always, why can't computers do things like this?" Coates said.

Computers can, it turns out. On a recent morning in an empty field at the edge of campus, Abbeel and Coates sent up one of their helicopters to demonstrate autonomous flight. The aircraft, brightly painted Stanford red, is an off-the-shelf radio control helicopter, with instrumentation added by the researchers.

For five minutes, the chopper, on its own, ran through a dizzying series of stunts beyond the capabilities of a full-scale piloted helicopter and other autonomous remote control helicopters. The artificial-intelligence helicopter performed a smorgasbord of difficult maneuvers: traveling flips, rolls, loops with pirouettes, stall-turns with pirouettes, a knife-edge, an Immelmann, a slapper, an inverted tail slide and a hurricane, described as a "fast backward funnel."
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Writing software for robotic helicopters is a daunting task, in part because the craft itself, unlike an airplane, is inherently unstable. "The helicopter doesn't want to fly. It always wants to just tip over and crash," said Oku, the pilot.

To scientists, a helicopter in flight is an "unstable system" that comes unglued without constant input. Abbeel compares flying a helicopter to balancing a long pole in the palm of your hand: "If you don't provide feedback, it will crash."

Early on in their research, Abbeel and Coates attempted to write computer code that would specify the commands for the desired trajectory of a helicopter flying a specific maneuver. While this hand-coded approach succeeded with novice-level flips and rolls, it flopped with the complex tic-toc."

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THis is amazing stuff. THe ramifications for us humans doing things from piloting a plane to doing surgery just boggles the mind. In not that many years they will develop a synthetic human voice that could listen to you talk and duplicate your voice.

[sam sarrha]

1. must be the source of Palins Qualifications for pResident.. she watched W

:rofl:

[JohnWxy]

2. puppet , Robot: good analogy.

[JohnWxy]

3. some amazing videos. check out "chaos" -

http://heli.stanford.edu/

and some more : http://www.youtube.com/watch?v=kN6ifrqwIMY

[kurt_cagle]

4. Autonomous Systems

We needed to get to a point where the horsepower necessary to do the "learning" had reached a sufficient level (theory's been around since the early 1990s) but it looks like we're getting close. I'm seeing the same thing happen with autonomous automobile driving systems - the most successful ones to date have been the ones that use a combination of learning systems and genetic algorithms for determining which strategies are most effective within that learning context.

[JohnWxy]

6. these genetic algorithms (IOW emperically validated - keep only what works)

have really shot AI forward - as you said it's really the horsepower (computing power) that really has made this possible.

(it's kinda scary too, when you realize these things are going to actually outperform humans in a lot of areas - and in the not-too-distant future.)

[comrade snarky]

8. The power of evolutionary systems

And the potential speed of large neural networks both frightens me and fills me with giddy anticipation for the next 50 years. We are still in the iron age of computing.

There is some hyper cool stuff right around the corner.

[eppur_se_muova]

5. Next they'll be feeding them worms ... nt

[BadgerKid]

7. I'm sure military applications are an eventuality. n/t