A WAM arm learns how to flip a pancake with a lot of trial and error. Emphasis on the error.

One day robots will sell like hotcakes...especially if they can make you hotcakes. Scientists from the Italian Institute of Technology have successfully taught a Barrett WAM arm the time honored culinary skill of flipping pancakes. Petar Kormushev and Sylvain Calinon first trained the robot by actively moving its arm in a demonstration. From that point on, however, the robot learned by evaluating it's own success and trying to improve upon it. A fake pancake was used so that it could be more easily tracked. Still, it took about 50 trials to get the basics down. The video of the project is hilarious. I just can't get enough of watching an artificial pancake whiz about a room haphazardly. Check it out for yourself below.

The real story behind this project is not the pancakes, but the programming. We've seen other robots perform much better in the kitchen than this WAM. What makes Kormushev and Calinon's attempt intriguing is that they relied on reinforcement learning - a process by which a program (like the one controlling the robot) tries to maximize its success. The robot didn't simply repeat the example is was first given, it actively asked itself what it had done well, and what it would probably benefit from changing. The researchers used variations on this premise (most of which are beyond my programming knowledge) to help the robot quickly decide what works. 50 trials may sound like a lot (and it probably is) but the fact that the robot's program was learning, and not simply repeating a defined set of tasks, is interesting. (This focus on learning is not a surprise when you consider that Kormushev is also working on the iCub child-like robot).  In the future, these techniques may help computers/robots learn tasks with a minimum of input and help them become proficient automatically over time. Flipping pancakes today, performing heart surgery tomorrow. Just don't be one of the first 50 patients.

[screen capture and video credit: Kormushev and Calinon]
[source: Kormushev website]