# This Robot Arm Learned To Assemble Objects It Hasn’t Seen Before ## Метаданные - **Канал:** Two Minute Papers - **YouTube:** https://www.youtube.com/watch?v=O8l4Kn-j-5M - **Дата:** 04.01.2020 - **Длительность:** 4:18 - **Просмотры:** 101,804 ## Описание ❤️ Check out Lambda here and sign up for their GPU Cloud: https://lambdalabs.com/papers 📝 The paper "Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly" is available here: https://form2fit.github.io/ ❤️ Watch these videos in early access on our Patreon page or join us here on YouTube: - https://www.patreon.com/TwoMinutePapers - https://www.youtube.com/channel/UCbfYPyITQ-7l4upoX8nvctg/join  🙏 We would like to thank our generous Patreon supporters who make Two Minute Papers possible: Alex Haro, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Benji Rabhan, Brian Gilman, Bryan Learn, Christian Ahlin, Claudio Fernandes, Daniel Hasegan, Dan Kennedy, Dennis Abts, Eric Haddad, Eric Martel, Evan Breznyik, Geronimo Moralez, James Watt, Javier Bustamante, John De Witt, Kaiesh Vohra, Kasia Hayden, Kjartan Olason, Levente Szabo, Lorin Atzberger, Lukas Biewald, Marcin Dukaczewski, Marten Rauschenberg, Maurits van Mastrigt, Michael Albrecht, Michael Jensen, Nader Shakerin, Owen Campbell-Moore, Owen Skarpness, Raul Araújo da Silva, Rob Rowe, Robin Graham, Ryan Monsurate, Shawn Azman, Steef, Steve Messina, Sunil Kim, Taras Bobrovytsky, Thomas Krcmar, Torsten Reil, Tybie Fitzhugh. https://www.patreon.com/TwoMinutePapers Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Instagram: https://www.instagram.com/twominutepapers/ Twitter: https://twitter.com/karoly_zsolnai Web: https://cg.tuwien.ac.at/~zsolnai/ ## Содержание ### [0:00](https://www.youtube.com/watch?v=O8l4Kn-j-5M) Generalizable Assembly Through Shape Matching Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér. Have a look and marvel at this learning-based assembler robot that is able to put together simple contraptions. Since this is a neural-network based learning method, it needs to be trained to be able to do this. So, how is it trained? Normally, to train such an algorithm, we would have to show it a lot of pairs of the same ### [0:22](https://www.youtube.com/watch?v=O8l4Kn-j-5M&t=22s) Varying Initial Conditions contraption, and tell is that this is what it looks like when it’s disassembled, and what you see here is the same thing, assembled. If we did this, this method would be called supervised learning. This would be very time consuming, and potentially expensive as it would require the presence of a human as well. A more convenient way would be to go for unsupervised learning, where we just chuck a lot of things ### [0:44](https://www.youtube.com/watch?v=O8l4Kn-j-5M&t=44s) Descriptor Visualization on the table and say, “well, robot, you figure it out”. However, this would be very inefficient, if at all possible because we would have to provide it many-many contraptions that wouldn’t fit on the table. But this paper went for none of these solutions, as they opted for a really smart self-supervised ### [1:01](https://www.youtube.com/watch?v=O8l4Kn-j-5M&t=61s) Key Ideas technique. So what does that mean? Well, first, we give the robot an assembled contraption, and ask it to disassemble it. And therein lies the really cool idea, because disassembling it is easier, and by rewinding the process, it also gets to know how to assemble it later. And, the training process takes place by assembling, disassembling, and doing it over and over again, several hundred times per object. Isn’t this amazing? Love it. However, what is the point of all this? Instead of all this, we could just add explicit instructions to a non-learning based robot to assemble the objects. Why not just do that? And the answer lies in one of the most important aspects within machine learning - generalization. ### [1:49](https://www.youtube.com/watch?v=O8l4Kn-j-5M&t=109s) Data Collection from Disassembly If we program a robot to be able to assemble one thing, it will be able to do exactly that - assemble one thing. And whenever we have a new contraption on our hands, we’ll need to reprogram it. However, with this technique, after the learning process took place, we will be able to give ### [2:10](https://www.youtube.com/watch?v=O8l4Kn-j-5M&t=130s) Generalization to Novel Objects/Kits it a new, previously unseen object and it will have a chance to assemble it. This requires intelligence to perform. So, how good is it at generalization? Well, get this, the paper reports that when showing it new objects, it was able to successfully assemble them 86% of the time. Incredible. So what about the limitations? This technique works on a 2D planar surface, for instance, this table, and while it is able to insert most of these parts vertically - it does not deal well with more complex assemblies that require inserting screws and pegs in a 45 degree angle. As we always say, two more papers down the line, and this will likely be improved significantly. I you have ever bought a bed or a cupboard and said, well, it just looks like a block ### [2:59](https://www.youtube.com/watch?v=O8l4Kn-j-5M&t=179s) Generalization to Novel Settings Individual how hard can it be to assemble? Wait, does this thing have more than a 100 screws and pegs? I wonder why? And then, 4. 5 hours later, you find out yourself. I hope techniques like these will help us save time by enabling us to buy many of these contraptions pre-assembled, and it can be used for much, much more. What a time to be alive! This episode has been supported by Lambda. If you're a researcher or a startup looking for cheap GPU compute to run these algorithms, check out Lambda GPU Cloud. I've talked about Lambda's GPU workstations in other videos and am happy to tell you that they're offering GPU cloud services as well. The Lambda GPU Cloud can train Imagenet to 93% accuracy for less than $19! Lambda's web-based IDE lets you easily access your instance right in your browser. And finally, hold on to your papers, because the Lambda GPU Cloud costs less than half of AWS and Azure. Make sure to go to lambdalabs. com/papers and sign up for one of their amazing GPU instances today. Our thanks to lambda 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/14201*