# Can We Teach a Robot Hand To Keep Learning? ## Метаданные - **Канал:** Two Minute Papers - **YouTube:** https://www.youtube.com/watch?v=9gX24m3kcjA - **Дата:** 12.05.2020 - **Длительность:** 5:16 - **Просмотры:** 81,038 ## Описание ❤️ Check out Linode here and get $20 free credit on your account: https://www.linode.com/papers 📝 The paper "Efficient Adaptation for End-to-End Vision-Based Robotic Manipulation" is available here: https://sites.google.com/view/efficient-ft/home ❤️ 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, 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, Marcin Dukaczewski, Michael Albrecht, Nader S., Owen Campbell-Moore, Owen Skarpness, Rob Rowe, 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=9gX24m3kcjA) Dear Fellow Scholars, this is Two Minute Papers with Dr. Károly Zsolnai-Fehér. In 2019, researchers at OpenAI came up with an amazing learning algorithm that they deployed on a robot hand that was able to dexterously manipulate a Rubik’s cube…even when it was severely hamstrung. A good game plan to perform such a thing, is to first, solve the problem in a computer simulation where we can learn and iterate quickly, and then, transfer everything the agent learned there to the real world, and hope that it obtained general knowledge that indeed can be applied to real tasks. Papers like these are some of my favorites. If you are one of our core Fellow Scholars, you may also remember that we talked about walking robots about 200 episodes ago. In this amazing paper, we witnessed a robot not ### [0:45](https://www.youtube.com/watch?v=9gX24m3kcjA&t=45s) classic tripod gait, damaged robot only learning to walk, but, it could also adjust its behavior and keep walking, even if one or multiple legs lose power, or get damaged. In this previous work, the key idea was to allow the robot to learn tasks such as walking not only in one, optimal way, but to explore and build a map of many alternative motions relying on different body parts. Both of these papers teach us that working in the real world often shows us new, unexpected challenges to overcome. And, this new paper offers a technique to adapt a robot arm to these challenges after it has been deployed into the real world. It is supposed to be able to pick up objects, which sounds somewhat simple these days…until we realize that new, previously unseen objects may appear in the bin with different shapes, or material models. For example, reflective and refractive objects are particularly perilous because they often show us more about their surroundings than about themselves, lighting conditions may also change after deployment, the gripper’s length or shape may change, and many, many other issues are likely to arise. Let’s have a look at the lighting conditions part. Why would that be such an issue? The objects are the same, the scene looks nearly the same, so, why is this a challenge? Well, if the lighting changes, the reflections change significantly, and since the robot arm sees its reflection and thinks that it is a different object, it just keeps trying to grasp it. After some fine-tuning, this method was able to increase the otherwise not too pleasant ### [2:33](https://www.youtube.com/watch?v=9gX24m3kcjA&t=153s) Harsh Lighting 32% success rate to 63%. Much, much better. Also, extending the gripper used to be somewhat ### [2:44](https://www.youtube.com/watch?v=9gX24m3kcjA&t=164s) Extend Gripper of a problem, but as you see here, with this technique, it is barely an issue anymore. Also, if we have a somewhat intelligent system, and we move position of the gripper around, nothing really changes, so we would expect it to perform well. Does it? Well, let’s have a look! Unfortunately, it just seems to be rotating around without too many meaningful actions. And now, hold on to your papers, because after using this continual learning scheme, yes! It improved substantially and makes very few mistakes, and can even pick up these tiny objects that are very challenging to grasp with this clumsy hand. This fine-tuning step typically takes an additional hour, or at most a few hours of extra training, and can used to help these AIs learn continuously after they are deployed in the real world, thereby updating and improving themselves. It is hard to define what exactly intelligence ### [3:46](https://www.youtube.com/watch?v=9gX24m3kcjA&t=226s) Checkerboard is, but an important component of it is being able to reuse knowledge and adapt to new, unseen situations. This is exactly what this paper helps with. Absolute witchcraft. What a time to be alive! This episode has been supported by Linode. Linode is the world’s largest independent cloud computing provider. 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Thanks for watching and for your generous support, and I'll see you next time! --- *Источник: https://ekstraktznaniy.ru/video/14132*