 Dear Fellow Scholars, this is two-minute papers with Kato Ejona Ifehir. This robot was tasked to clean up this table. 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 1, several objects are tightly packed together and Reason Number 2, they are too wide to hold with the fingers. But this means that the robot needs to figure out a series of additional actions to push the other pieces around and finally grab the correct one. 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 clatter. Really cool. This robot arm works the following way. It has an RGBD 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 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 map looking, wooden blocks, and it will do well when it encounters new objects that are vastly different in shape and appearance. And as you see on the right, remarkably, this is exactly the case. Checkmark This makes us one step closer to learning algorithms that can see the world around us, interpret it, and make proper decisions to carry out the plan. Thanks for watching and for your generous support, and I'll see you next time.