WEBVTT 00:00:00.433 --> 00:00:02.235 Tilman, thank you so much for joining us. 00:00:02.235 --> 00:00:04.187 What is physical intelligence? 00:00:04.788 --> 00:00:08.541 Well, physical intelligence refers back to the ability to 00:00:08.541 --> 00:00:12.345 observe, understand, and interact with the physical world. 00:00:12.595 --> 00:00:13.747 Let me give you an example. 00:00:14.080 --> 00:00:16.916 Let's assume you would ask to get something for drink. 00:00:17.400 --> 00:00:21.438 So as a human being, I would observe our surrounding. 00:00:21.521 --> 00:00:23.323 I would identify a bottle. 00:00:23.356 --> 00:00:27.961 I understand that a bottle is, for example, a drinkable liquid 00:00:27.961 --> 00:00:28.828 for a human. 00:00:29.162 --> 00:00:32.232 And I also have the ability to grab the bottle and to fill it 00:00:32.232 --> 00:00:32.882 into a glass. 00:00:32.882 --> 00:00:37.020 So it sounds quite easy, which is easy for a human is a quite 00:00:37.020 --> 00:00:39.355 complex task for a robotic system. 00:00:39.756 --> 00:00:42.242 And this is what physical intelligence means. 00:00:43.193 --> 00:00:45.361 And so how can embodied AI help? 00:00:45.428 --> 00:00:46.513 And why now? 00:00:47.547 --> 00:00:52.318 Well, embodied AI is at first when artificial intelligence 00:00:52.318 --> 00:00:57.006 meets the world of robotics and we talk about three basic 00:00:57.006 --> 00:00:59.042 embodied AI capabilities. 00:00:59.476 --> 00:01:03.012 First and foremost, it starts with a visual perception to 00:01:03.012 --> 00:01:04.481 understand the 3D world. 00:01:04.748 --> 00:01:09.152 We did great progress over the past month, not only to identify 00:01:09.152 --> 00:01:13.640 any arbitrary object in 3D space, we can also predict its 00:01:13.640 --> 00:01:18.628 exact location as well as orientation in space, which is 00:01:18.628 --> 00:01:21.564 of importance because if you ask a robotic system to grab 00:01:21.564 --> 00:01:25.268 something, the system needs to understand where to grab what. 00:01:25.835 --> 00:01:28.471 Secondly, it's about workflow planning. 00:01:29.239 --> 00:01:32.492 This brings us back to the example with a bottle because 00:01:32.492 --> 00:01:34.594 you just ask for something to drink. 00:01:35.161 --> 00:01:38.815 But to accomplish this task, you need to plan accordingly 00:01:38.815 --> 00:01:41.518 incremental steps to accomplish this goal. 00:01:42.152 --> 00:01:45.989 And also here we did great progress in so-called concept learning. 00:01:46.639 --> 00:01:50.410 And last but not least, it's about the manipulation of 00:01:50.410 --> 00:01:54.581 objects. And for doing this we need to invest into so-called 00:01:54.581 --> 00:01:59.035 causal models of the real world because the robotic system needs 00:01:59.035 --> 00:02:03.356 to understand the implication and impact of certain actions on 00:02:03.356 --> 00:02:04.991 the physical embodiment. 00:02:05.558 --> 00:02:10.446 So overall this is what embodied AI paves the the way towards 00:02:10.446 --> 00:02:11.698 physical intelligence. 00:02:12.549 --> 00:02:16.269 And how can companies capture value with embodied AI? 00:02:17.137 --> 00:02:20.490 Well, embodied AI has a potential to revolutionize the 00:02:20.490 --> 00:02:24.244 world of robotics because you can't start automating handling 00:02:24.244 --> 00:02:28.148 operations too versatile and complex for traditional automation. 00:02:28.148 --> 00:02:32.669 Now think of the millions of handling operations which we do 00:02:32.669 --> 00:02:37.907 everyday on the shop floor, in warehouses or even beyond the factory. 00:02:38.308 --> 00:02:42.362 And this is the great potential of embodied AI, which can really 00:02:42.362 --> 00:02:46.065 change manufacturing, but also the world we are living in. 00:02:46.783 --> 00:02:47.734 Thank you so much. 00:02:47.817 --> 00:02:48.768 Thank you for having me.