 Dear Fellow Scholars, this is two-minute papers with Karu Zonai-Fehir, about 350 episodes ago in this series, in episode number 8, we talked about an amazing paper in which researchers built virtual characters with a bunch of muscles and joints and through the power of machine learning taught them to actuate them just the right way so that they could learn to walk. Well, some of them anyway. Later, we've seen much more advanced variants where we could even teach them to lift weights, jump really high, or even observe how their movements would change after they undergo surgery. This paper is a huge step forward in this area and if you look at the title, it says that it proposes multiplicative composition policies to control these characters. What this means is that these complex actions are broken down into a sum of elementary movements. Intuitively, you can imagine something similar when you see a child use small, simple Lego pieces to build a huge, breathtaking spaceship. That sounds great, but what does this do for us? Well, the ability to properly combine these Lego pieces is where the learning part of the technique shines and you can see on the right that these individual Lego pieces are as amusing as useless if they are not combined with others. To assemble efficient combinations that are actually useful, the characters are first required to learn to perform reference motions using combinations of these Lego pieces. Here on the right, the blue bars show which of these Lego pieces are used and when in the current movement pattern. Now, with heard enough of these Legos, what is this whole compositional thing good for? Well, a key advantage of using these is that they are simple enough so that they can be transferred and reused for other types of movement. As you see here, this footage demonstrates how we can teach a biped or even a T-Rex to carry and stack boxes or how to dribble or how to score a goal. Amusingly, according to the paper, it seems that this T-Rex weighs only 55 kilograms or 121 pounds, an adorable baby T-Rex, if you will. As a result of this transferability property, when we assemble a new agent or wish to teach an already existing character some new moves, we don't have to train them from scratch as they already have access to these Lego pieces. I love seeing all these new papers in the intersection of computer graphics and machine learning. This is a similar topic to what I am working on as a full-time research scientist at the Technical University of Vienna and in these projects we train plenty of neural networks which requires a lot of computational resources. Sometimes when we have to spend time maintaining the machines running these networks, I wish we could use Linode. Linode is the world's largest independent cloud hosting and computing provider and they have GPU instances that are tailor-made for AI, scientific computing and computer graphics projects. If you feel inspired by these works and you wish to run your experiments or deploy your already existing works through a simple and reliable hosting service, make sure to join over 800,000 other happy customers and choose Linode. To reserve your GPU instance and receive a $20 free credit, visit Linode.com slash papers or click the link in the video description and use the promo code papers20 during sign up. Give it a try today. Our thanks to Linode for supporting the series and helping us make better videos for you. Thanks for watching and for your generous support and I'll see you next time.