 Dear Fellow Scholars, this is Two Minute Papers with Dr. Karojejona Ifeher. 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 open AI's robot hand learned to dexterously rotate this ruby 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, all that's 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 it 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 one and a half 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 also one of those techniques that does not require Google or OpenAI-level resources to make something really amazing. 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 it can 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. Please subscribe if you wish to see more of these. This episode has been supported by Lambda GPU Cloud. If you're looking for inexpensive Cloud GPUs for AI, check out Lambda GPU Cloud. They recently launched Quadro RTX 6000, RTX 8000, and V100 instances. And hold onto your papers because Lambda GPU Cloud can cost less than half of AWS and Azure. Plus, they are the only Cloud service with 48GB RTX 8000. Join researchers at organizations like Apple, MIT and Caltech in using Lambda Cloud instances, workstations or servers. Make sure to go to lambdalabs.com slash papers to sign up for one of their amazing GPU instances today. Thanks to Lambda for their long-standing support and for helping us make better videos for you. Thanks for watching and for your generous support, and I'll see you next time.