# This Robot Learned To Clean Up Clutter ## Метаданные - **Канал:** Two Minute Papers - **YouTube:** https://www.youtube.com/watch?v=txHQoYKaSUk - **Дата:** 07.10.2018 - **Длительность:** 2:31 - **Просмотры:** 26,134 ## Описание The paper "Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning" is available here: http://vpg.cs.princeton.edu/ Pick up cool perks on our Patreon page: › https://www.patreon.com/TwoMinutePapers We would like to thank our generous Patreon supporters who make Two Minute Papers possible: 313V, Andrew Melnychuk, Angelos Evripiotis, Anthony Vdovitchenko, Brian Gilman, Christian Ahlin, Christoph Jadanowski, Dennis Abts, Emmanuel, Eric Haddad, Eric Martel, Evan Breznyik, Geronimo Moralez, John De Witt, Kjartan Olason, Lorin Atzberger, Marten Rauschenberg, Michael Albrecht, Michael Jensen, Milan Lajtoš, Morten Punnerud Engelstad, Nader Shakerin, Owen Skarpness, Raul Araújo da Silva, Rob Rowe, Robin Graham, Ryan Monsurate, Shawn Azman, Steef, Steve Messina, Sunil Kim, Thomas Krcmar, Torsten Reil, Zach Boldyga. https://www.patreon.com/TwoMinutePapers Crypto and PayPal links are available below. Thank you very much for your generous support! › PayPal: https://www.paypal.me/TwoMinutePapers › Bitcoin: 13hhmJnLEzwXgmgJN7RB6bWVdT7WkrFAHh › Ethereum: 0x002BB163DfE89B7aD0712846F1a1E53ba6136b5A › LTC: LM8AUh5bGcNgzq6HaV1jeaJrFvmKxxgiXg Thumbnail background image credit: https://pixabay.com/photo-3198094/ Splash screen/thumbnail design: Felícia Fehér - http://felicia.hu Károly Zsolnai-Fehér's links: Facebook: https://www.facebook.com/TwoMinutePapers/ Twitter: https://twitter.com/karoly_zsolnai Web: https://cg.tuwien.ac.at/~zsolnai/ ## Содержание ### [0:00](https://www.youtube.com/watch?v=txHQoYKaSUk) Dear Fellow Scholars, this is Two Minute Papers with Károly Zsolnai-Fehér. This robot was tasked to clean up this table. ### [0:05](https://www.youtube.com/watch?v=txHQoYKaSUk&t=5s) What makes grasping hard? Normally, anyone who watches this series knows that would be no big deal for any modern learning algorithm. Just grab it, right? Well, not in this case, because reason number one: several objects are tightly packed together, and reason number two, they are too wide to hold with the fingers. What this means is that the robot needs to figure out a series additional actions to push the other pieces around and finally, grab the correct one. ### [0:33](https://www.youtube.com/watch?v=txHQoYKaSUk&t=33s) Learning Pushing and Grasping Look! It found out that sometimes, pushing helps grasping by making space for the fingers to grab these objects. This is a bit like the Roomba vacuum cleaner robot, but even better, for clutter. Really cool. This robot arm works the following way: it has an RGB-D camera, which endows it with the ability to see both color and depth. Now that we have this image, we have not one, but two neural networks looking at it: one is used to predict the utility of pushing at different possible locations, and one for grasping. Finally, a decision is made as to which motion would lead to the biggest improvement in the state of the table. So, what about the training process? As you see, the speed of this robot arm is limited, and we may have to wait for a long time for it to learn anything useful and not just flail around destroying other nearby objects. The solution includes my favorite part - training the robot within a simulated environment, where these commands can be executed within milliseconds, speeding up the training process significantly. Our hope is always that the principles learned within the simulation applies to reality. Checkmark. The simulation is also very useful to make comparisons with other state of the art algorithms ### [1:49](https://www.youtube.com/watch?v=txHQoYKaSUk&t=109s) Does pushing help grasping? easier. And, do you know what the bane of many-many learning algorithms is? Generalization. This means that if the technique was designed well, it can be trained on matte looking, wooden blocks, and it will do well when it encounters new objects that are vastly different ### [2:07](https://www.youtube.com/watch?v=txHQoYKaSUk&t=127s) Generalizing to Novel Objects in shape and appearance. And as you see on the right, remarkably, this is exactly the case. Checkmark. This takes us one step closer to learning algorithms that can see the world around us, interpret it, and make proper decisions to carry out a plan. ### [2:23](https://www.youtube.com/watch?v=txHQoYKaSUk&t=143s) Failure Modes Thanks for watching and for your generous support, and I'll see you next time! --- *Источник: https://ekstraktznaniy.ru/video/14406*