# This AI Controls Virtual Quadrupeds! 🐕 ## Метаданные - **Канал:** Two Minute Papers - **YouTube:** https://www.youtube.com/watch?v=qwAiLBPEt_k - **Дата:** 06.06.2020 - **Длительность:** 6:26 - **Просмотры:** 107,028 ## Описание ❤️ Check out Weights & Biases and sign up for a free demo here: https://www.wandb.com/papers Their instrumentation for this previous work is available here: https://app.wandb.ai/sweep/nerf/reports/NeRF-%E2%80%93-Representing-Scenes-as-Neural-Radiance-Fields-for-View-Synthesis--Vmlldzo3ODIzMA 📝 The paper "CARL: Controllable Agent with Reinforcement Learning for Quadruped Locomotion" is available here: https://inventec-ai-center.github.io/projects/CARL/index.html 🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Aleksandr Mashrabov, Alex Haro, Alex Paden, Andrew Melnychuk, Angelos Evripiotis, Benji Rabhan, Bruno Mikuš, Bryan Learn, Christian Ahlin, Daniel Hasegan, Eric Haddad, Eric Martel, Javier Bustamante, Lorin Atzberger, Lukas Biewald, Michael Albrecht, Nader S., Owen Campbell-Moore, Owen Skarpness, Robin Graham, Steef, Sunil Kim, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh More info if you would like to appear here: https://www.patreon.com/TwoMinutePapers Meet and discuss your ideas with other Fellow Scholars on the Two Minute Papers Discord: https://discordapp.com/invite/hbcTJu2 Károly Zsolnai-Fehér's links: Instagram: https://www.instagram.com/twominutepapers/ Twitter: https://twitter.com/twominutepapers Web: https://cg.tuwien.ac.at/~zsolnai/ ## Содержание ### [0:00](https://www.youtube.com/watch?v=qwAiLBPEt_k) Intro dear fellow scholars this is two minute papers with dr. Caro Jonah Aoife here if we have an animation movie or a computer game with quadrupeds and we are yearning for really high-quality lifelike animations motion capture is often the go-to tool for the job ### [0:15](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=15s) Motion Capture motion capture means that we put an actor in our case a dog in the studio and we ask it to perform sitting trotting pacing and jumping record its motion and transfer it onto our virtual character there are two key challenges with this approach one we have to try to weave together all of these motions because we cannot record all the possible transitions between sitting and pacing jumping and trotting and so on we need some filler animations to make these transitions work this was addressed by this neural network based technique here the other one is trying to reduce these unnatural foot sliding motions both of these have been addressed by learning based algorithms in the previous works that you see here later bipeds were also taught to ### [1:00](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=60s) Research maneuver through complex geometry and Sirian not one kind of chair but any chair regardless of geometry this already sounds like science fiction so are we done or can these amazing techniques be further improved well we are talking about research so the answer is of course yes here you see a technique that reacts to its environment in a believable manner it can accidentally step on the ball stagger a little bit and then flounder on the slippery surface and it doesn't fall and it can do much more the goal is that we would be able to do all this without explicitly programming all of these behaviors by hand but unfortunately there is a problem if we write an agent that ### [1:52](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=112s) The Problem behaves according to physics it will be difficult to control properly and this is why this new technique shines it gives us physically appealing motion and we can grab the controller and play with the character like in a video game the first step we need to perform is called imitation learning this means looking at real reference movement data and trying to reproduce it this is going to be motion that looks great is very natural however we are nowhere near done because we still don't have any control over this agent can we improve this somehow well let's try something and see if it ### [2:32](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=152s) The Architecture works this paper proposes that in step number two we try an architecture by the name generative adversarial Network here we have a neural network that generates motion and a discriminator that looks at these motions and tries to tell what is real and what is fake however to accomplish this we need lots of real and fake data that we then use to train the discriminator to be able to tell which one is which so how do we do that well let's try to label the movement that came from the user controller inputs as fake and the reference movement data from before as real remember that this makes sense as we concluded that the reference motion looked natural if we do this over time we will have a discriminator network that is able to look at a piece of animation data and ### [3:21](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=201s) The Performance tell whether it is real or fake so after doing all this work how does this perform does this work well sort of but it does not react well if we try to control the simulation if we let it run undisturbed it works beautifully and now when we try to stop it with the controller well this needs some more work doesn't it so how do we adapt this architecture to the animation problem that we have here and here comes one of the key ideas of the paper in step number three we can ### [3:57](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=237s) Reinforcement Learning rewire this whole thing to originate from the controller and introduced a deep reinforcement learning based fine-tuning stage this was the amazing technique that did mind used to defeat Atari breakout so what good does all this for us well hold on ### [4:16](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=256s) Motion Synthesis to your papers because it enables true user control was synthesizing motion that is very robust against tough previously unseen scenarios and if you have been watching this series for a while you know what is coming of course throwing blocks at it and see how well it can take the punishment as you see the AI is taking it like a champ ### [4:41](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=281s) Blocks so add pathfinding to the agent and of course being computer graphics researchers throw some blocks into the mix for good measure it performs beautifully this is so realistic ### [4:54](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=294s) Sensors we can also add sensors to the agent to allow them to navigate in this virtual world in a realistic manner just a note on how remarkable this is so this quadruped it behaves according to ### [5:05](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=305s) SIGGRAPH physics lets us control it with the controller which is already somewhat of a contradiction and it is robust against these perturbations at the same time this is absolute witchcraft and no doubt it has earned to be accepted to SIGGRAPH which is perhaps the most prestigious research venue in computer graphics congratulations what you see here is an ### [5:29](https://www.youtube.com/watch?v=qwAiLBPEt_k&t=329s) Outro instrumentation for a previous paper that we covered in this series which was made by weights and biases I think organizing these experiments really showcases the usability of their system weights and biases provides tools to track your experiments in your deep learning projects their system is designed to save you a ton of time and money and it is actively used in projects at prestigious labs such as open AI Toyota research github and more and the best part is that if you are an academic or have an open-source project you can use their tools for free it really is as good as it gets make sure to visit them through wnb comm slash papers or just click the link in the video description and you can get a free demo today our thanks to weights and biases 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 --- *Источник: https://ekstraktznaniy.ru/video/14119*