Two interesting things to notice from the video:<p>1. In the failure trials the robot does not move its arm toward the object as it falls. This is distinctly inhuman! When we initiate a motor program we are constantly checking our prediction for the behavior of both our body and external objects against reality and then updating that program in real time. This bot apparently updates its program only after each trial.<p>2. The robot moves back to its standard starting position after each successful trial. This demonstrates the specificity of the motor pattern it's developed. Due to time pressure and the complexity of variations we deal with it is usually advantageous to learn a generalized pattern rather than a single pattern that works for a constrained set of starting conditions.<p>As a side note on the difficulty of this task, I agree with sukuriant that the paucity of the information the robot has, especially lack of fine grained touch, is a huge impediment. Secondly recall that in the human brain about 50% of the neurons live in the cerebellum which is strongly implicated in storing and updating fine grained motor patterns. (Gross patterns and intentions being initiated in the motor cortex).
Interesting how the failures make it look eerily human. And it seems to me while watching that the robot becomes frustrated when failing. I know it's me projecting that, seems interesting to apply that to human interaction though. How much is projection and how much is real empathy.
What I find particularly interesting is the sort of "superstition" many of these kinds of robots show. What I'm referring to in this case is how, after 50 tries, the robot arm here moves to its right before every flip attempt. I believe the same thing shows up with solutions from genetic algorithms and neural networks.<p>Seems like there is always some non-negligible probability that within the factors any learning robot takes into account as part of its success is a factor which is actually totally irrelevant. That's probably somewhat how our own brains operate as well.
What's interesting to me is that the robot has a little pan wobble at the end of the flip. I wonder if that has some sort of advantageous effect on the outcome of a flip? Or maybe the robot is just 'superstitious' because it had a more successful run once when it added the wobble.
The associated article seems to think that 50 steps is a long amount of time for learning this article. I would argue that 50 steps is nearly no time at all. Even though a human being may take less attempts to learn,
1) we are likely taking input from more senses than the robot is about what's occurring (we sense by stereoscopic sight, feel, etc. The robot may not sense by all of these.)
2) can apply knowledge from other domains in solving this problem (if memory serves me, this is part of the Holy Grail for artificial intelligence)
3) may make multiple attempts in our own minds before attempting to perform the activity physically again.<p>While I'm primarily experienced in Genetic Algorithms and NNs (so not re-enforcement learning, so much), 50 steps (or generations in a non-steady-state GA) is a very short amount of time, and so learning to properly coordinate multiple degrees of freedom into a successful activity in only 50 steps is, to me, pretty impressive.
I realize this isn't exactly the point of the whole exercise, but a stiff pancake that easily slides onto the pan is kind of cheating. :-)<p>A deformable pancake would make the experiment batter.
Even though it doesn't use any learning process, I think that you might find this video interesting:<p><a href="http://video.google.com/videoplay?docid=3757897210640719617#" rel="nofollow">http://video.google.com/videoplay?docid=3757897210640719617#</a><p>The control uses the dynamics of the robot to optimize a trajectory to increase the weight that the robot can lift.
I'm curious if anyone knows why it sounded like there was a jet engine in the background throughout the video? Was it just a bad recording or was there some reason why the room had to have a crazy amount of ventilation?