Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Today we are going to see if a robot can learn to play table tennis.
Spoiler alert, the answer is yes, quite well in fact.
That is surprising, but what is even more surprising is how quickly it learned to do
that.
Recently, we have seen a growing number of techniques where robots learn in a computer
simulation, and then, get deployed into the real world.
Yes, that sounds like science fiction.
So, does this work in practice?
Well, I’ll give you two recent examples, and you can decide for yourself.
What you see here is example number one, where OpenAI’s robot hand that learned to dexterously
rotate this Rubik cube to a given target state.
How did it do it?
Yes, you guessed it right, it learned in a simulation.
However, no simulation is as detailed as the real world, so they used a technique called
automatic domain randomization in which they create a large number of random environments,
each of which are a little different, and the AI is meant to learn how to solve many
different variants of the same problem.
And the result?
Did it learn general knowledge from that?
Yes, what’s more, this became not only a dexterous robot hand that can execute these
rotations, but, we can make up creative ways to torment this little machine, and it still
stood its ground.
Okay, so this works, but is this concept good enough for commercial applications?
You bet.
Example number two, Tesla uses no less than a simulated game world to train their self-driving
cars.
For instance, when we are in this synthetic video game, it is suddenly much easier to
teach the algorithm safely.
You can also make any scenario easier, harder, replace a car with a dog, or a pack of dogs,
and make many similar examples so that the AI can learn from these “what if” situations
as much as possible.
Now, that’s all great, but today, we are going to see whether this concept can be generalized
to playing table tennis.
And I have to be honest…I am very enthused, but a little skeptical too.
This task requires finesse, rapid movement, and predicting what is about to happen in
the near future.
It really is the whole package, isn’t it.
Now, let’s enter the training simulation and see how this goes.
First, we hit the ball over to its side, specify a desired return position, and ask it to practice
returning the ball around this desired position.
Then, after a quick retraining step against the ball throwing machine, we observe the
first amazing thing.
You see, the first cool thing here is that it practices against side spin and top spin
balls.
What are those?
These are techniques where the players hit the ball in ways to make their trajectory
much more difficult to predict.
Okay, enough of this, now, hold on to your papers, and let’s see how the final version
of the AI fares against a player.
And…whoa.
It really made the transition into the real world.
Look at that, this seems like it could go on forever.
Let’s watch for a few seconds.
Yep, still going.
Still going.
But, we are not done yet.
Not even close!
We said at the start of the video that this training is quick.
How quick?
Well, if you have been holding on to your papers, now, squeeze that paper, because all
the robot took was 1.5 hours of training.
And wait, there are two more mind-blowing numbers here.
It can return 98% of the balls.
And most of them are within 25 centimeters, or about 10 inches of the desired spot.
And again, great news, this is one more technique that does not require Google or OpenAI-level
resources to make something really amazing.
Loving it.
And you know, this is the way to make an excellent excuse to play table tennis during work hours.
They really made it work.
Huge congratulations to the team.
Now, of course, not even this technique is perfect.
We noted that it can handle side spin and top spin balls, but can’t deal with backspin
balls yet, because, get this, quoting ”it causes too much acceleration in a robot joint”.
Yes - a robot with joint pain.
What a time to be alive!
Now, one more thing.
As of the making of this video, this was seen by a grand total of… 54 people.
Again, there is a real possibility that if we don’t talk about this amazing work, no
one will.
And this is why I started Two Minute Papers.
Thank you very much for coming on this journey with me.
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Thanks for watching and for your generous support, and I'll see you next time!