Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér.
Scientists at NVIDIA knocked it out of the park with this one. Look,
they are training an AI that moves this virtual robot arm to finish everyday tasks on this table.
Let’s see. It picks up the toy school bus. So far so good. But, wait a second! This is not good
at all! It should have grabbed the c-clamp here, not the toy school bus. Why is that?
Well, it has only seen one demonstration to learn from yet, so these are just the humble beginnings.
Now, let’s look at what it can do after seeing 10 demonstrations. And…yes! That is so cool! Learning
is happening - The c-clamp goes in the box successfully. Wonderful! And now, for the crayon
box…that is not a crayon box. And neither is that one. And it’s not that one either. Oh my goodness.
Now, hold on to your papers, and let’s see what it learned after looking at still as few as a hundred
demonstrations. C-clamp into the box, crayon box too, toy school bus, get in that box. And,
yes, finally! The rest seems to work fine too. I love it. Good job, little AI!
And if you were wondering, this is a porcelain plate.
And that is twist number one. Learning happening from very few image-action pairs.
How many exactly? Well, it learned to do this, and more, from only 179 image-action
pairs. That, in the era of learning-based techniques, is almost nothing at all. Wow.
And, it gets better. One of the most mind-blowing results
is that from these very few examples, it also learned to deal with previously unseen colors and
words. And not only that, but it was also able to learn a wide variety of tasks from these 179
examples. And its capabilities just keep going on and on and on. Absolutely amazing.
Okay, now, that is all well and good. But still, this is robot arm
can play a computer game. Is that it? Why make a video about this? Why is this so important?
Well, and now, I give you the two more twists that breathe new life into this project.
Twist number two. We can use natural language to give instructions to these robots.
Yes! And twist number three. If you have been holding on to your papers so far, now,
squeeze that paper, because the knowledge it learned in the simulation transfers to reality.
We come come out of the video game world into the real world, and the AI still works wonderfully.
Let’s have a look. It can move chess pieces. Great work little AI, I love it! But, this was a solid
chess piece, you know what, let’s try something that requires a little more delicacy. Let’s see.
Yes, folding and unfolding a piece of cloth is also not a problem. That requires
delicate movement. So good, I love it! But, let’s try something that requires
even more finesse. Yes. Putting cherries in a box. That is really cool!
Sweeping also works… kind of. As long as we are looking to sweep beans.
And, believe it or not, it can even read. Again, kind of. You see, we can label two
boxes that will contain the good and bad stuff. And then, instruct the AI as follows.
This worked well, and it will work, even if we try to be a bit more tricky, and swap the labels.
And, as you see, it is not falling for it. I will have to give partial credit for that drop,
but still, all this from less than 200 demonstrations? That is incredible. So cool!
And, what you see here is what researchers refer to as sim2real. Teaching an AI something in a
simulated world, and then, deploying it into the real world. Okay, so what is all this good for?
Here is an amazing example, look, OpenAI trained their robot hand in a simulation
to be able to rotate these Rubik cubes. And then, deployed this software onto a real robot hand,
and, look! It can use this simulation knowledge, and now it works in the real world too.
But sim2real has relevance to self-driving cars too. Look. Tesla is already working on creating
virtual worlds, and training their cars there. One of the advantages of that is that we can create
really unlikely and potentially unsafe scenarios, but, in these virtual worlds, the self-driving AI
can train itself safely. And when they deploy them into the real world, they will have all
this knowledge. It is fantastic to see that Waymo is also moving in this direction.
As you see, sim2real is real. And NVIDIA’s new technique also puts us
one step closer to a future where this kind of intelligence
can be democratized and used by everyone. What a time to be alive!
Thanks for watching and for your generous support, and I'll see you next time!