TECH010: The Real Robotics Timeline w/ Ken Goldberg (Tech Podcast)

28:55So I believe you don't need complex hands. So I'm not saying that's the path to go. I believe you can do simple grippers. In fact, my company, Ambire Robotics, uses an even simpler gripper which is a suction cup and you can do incredible things with them so it's not necessarily the hardware but it's the software it's a control of this nuanced interaction that is very very challenging i think many of the other aspects are addressable i mean we have the ability to tell a robot go pick up the orange you know the orange jumper off the table we can solve that now. Computer vision systems are good enough to know that a jumper is a sweater and there's an orange one and it'll pull that up.

29:38No problem. But it's being able to actually pick it up and maybe put it on you and then button it up for you. That's where it's difficult. Yeah. Yeah. I mean, maybe what we see in the interim is robots that go to market, humanoid robots that go to market that have simplified the hands or have the range of activities or things that they can actually perform is very limited relative to a real human being in there and being able to do it. I don't know if that's how they go to market or not. I want to talk a little bit more about your company, Ambi. So this is really fascinating. So you guys have gone to market primarily focusing on logistics and warehouse type activities for robotics.

30:19Is that correct? Correct. So this started about seven years ago. we had a breakthrough in robot grasping and that was just simply ability to pick things out of a bin okay so it's not manipulating you know doing surgery but it's just picking things out of a bit that was a very old problem it's been known as the bin picking problem and people have been looking at that for decades but we had it we made an advance and this was especially the work of jeff mauler who was the phd student of mine who was the lead researcher on this and we can go into more details on the technical aspects of this, but the system was called DexNet, Dexterity Network.

30:57And it was based on collecting data, lots of examples. And it was somewhat, it was analogous in many ways to ImageNet, which was a breakthrough for computer images. So we did something similar. We synthesized this data set. We added noise in a very specific way, but the system started working remarkably well. And so it could pick up almost any object that you put into a bin, it would just pull it out. And you would throw in a whole pile of objects. We were digging around in our garages and closets and throwing everything we could into it. And it was consistently just being able to pull these things out.

31:29And so that was a very exciting moment for us. We got some publicity. It was in the New York Times and other places. And then we were approached by a number of companies and we decided to form our own company. That's awesome. I'm curious where you've seen just good old-fashioned engineering matter more than additional data or larger models? And then to the converse of that, when did you have data actually really surprise you? Okay, good. Well, that was a case. Great example. That was a case where data really did surprise us. We were able to generate 6 million example grasps because we had collected 10 ,000 object models, and then we could generate grasps on those models.

32:10And then we had all these, and we trained a network to be able to learn essentially where to grasp an object. So that was a data-driven approach. But I will tell you that when you take that and you have to move that into an experimental system or into a commercial system, then you need a huge amount of what I call good old-fashioned engineering. And this is where you have to really sweat the details. You have to make sure that the sensors are calibrated correctly, that your robot arms are calibrated and accurate. You have to be able to do the computation to move the arms very quickly. You have to control the surfaces of the grippers, the suction cups, myriad of details like that.

32:50The lighting, we had a little scale underneath the system that would recognize when an object was removed from the bin. It was like a digital scale. It was just another piece of engineering that had to go in there. So lots of all that. That was just our demonstration system in the lab, experimental system. But then when we moved into Ambirobotic, and by the way, I want to give credit also to the other students who are involved. Matt Mattel was another computer scientist working closely with Jeff Mahler. And then I also had two other PhD students from mechanical engineering. And one of them, Steve McKinley and David Geely.

33:25Brilliant. All four of these guys, extremely, extremely brilliant engineering students. And so they really knew how to work. They were very good friends. They remain good friends. They all worked very closely and spent a huge amount of time camping and hanging out together too. But they were perfectly complimentary because we had the computing skills and the mechanical skills. And mechanical guys knew how to design machines that could work reliably over a great period of time. And that's when we moved into building the Ambi sort system, which sorts packages for e-commerce. And this was a little bit, We didn't go in with this plan, but what we saw very quickly was that e-commerce was growing and there's a huge demand for sorting packages.

34:08It's very challenging to get packages out to the customer fast. So we started using that technique, DexNet, and we evolved it and commercialized it. And then we could make it work very fast. And then all kinds of other elements had to come in. We had another gantry system that would drop it into, pick it out of bins, pick an object out of bin, had to be scanned for a zip code, figure out which bin to go into then put it into the right bin avoid jamming the whole time make the system reliable safe and easy to use right all this is what i call good old-fashioned engineering and so i become a big advocate for this because after all this is a body of research and ideas and insights that have been developed over 400 500 years in engineering and still what we teach at Berkeley and all the major universities.

34:58We teach the engineering principles. And my point is, let's not forget about those. Those are still extremely valuable for engineering and for robots and getting them to actually work in practice. And anyone working in robotics, I think, will acknowledge that. Although the public perception is, oh, it's just now, you know, we're using AI and that's solving everything. It's not. It's solving certain little pieces of it. And as I said, there's certain pieces that are very, very difficult that still remain very difficult. So this comes back to what I was saying earlier, Preston, about my fear, which is that because there's so much expectation around humanoids right now, that if the companies can't deliver on that ability, then there might be a big backlash.

35:43And that's going to hurt companies like Amby, who are not trying to do that. Amby is trying to solve a real practical problem and do it efficiently and cost effectively and actually you know basically something that's very valuable for every everyone who shops at amazon or any online companies right we've sorted 100 million packages so far and i'm very proud of that because these machines as we're talking are out there sorting packages and they're very reliable they're not featured in the videos about there's no humanoids doing this yeah by the way although some have said that you know we'll have a humanoid doing that.

36:16But humanoid with hands, it's going to be a long time before that's even close to the efficiency of the systems that we have with destruction cups. Let's take a quick break and hear from today's sponsors. Every business is asking the same question. How do we make AI work for us? Sitting on the sidelines is, of course, not an option. Your competitors are already making their move. But with NetSuite by Oracle, you can put AI to work today. NetSuite is the number one AI cloud ERP, trusted by over 43 ,000 businesses. It unifies your financials, inventory, commerce, HR, and CRM into a single source of truth.

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39:28Trust Vanta to handle this stuff so they can focus on what actually matters. Get started at vanta.com slash T-I-P. That's V-A-N-T-A dot com slash T-I-P. All right, back to the show. After shipping robots that work every day in the warehouse, what's one belief that you held earlier in your academic career that you've had to revise based on that? Lots. I would tell you, one of the things that is very interesting is that you think, okay, I have this great new technology that's the breakthrough that really solves an important problem. Therefore, I can rush out into commercial world and build a company around it.

40:10Well, it turns out that technology is only a very small core part. It enables, but then there's all these things that have to come around it that are equally, if not more important. And actually, when you go to the customers and you say, hey, we have this new AI thing. They're like, wait a second. I don't care about that. How much money is it going to save me? That's all they care about. And that's business. That's business. Both my grandfathers of business were entrepreneurs, and so was my father. So I grew up in this kind of environment, and it's tough. It's tough out there. One grandfather was very successful in electronics, and my other grandfather was in the housing business, building homes.

40:45But my father struggled. He was a metallurgist, and he had a company doing chrome plating and it was very very difficult and you know he was buffeted by things way behind his control like you know recession of the 70s actually really hurt his business very badly so he struggled so there's a lot of factors and it has to do with competition and timing what i would also say in industry is that and this is going to come back to the data aspect which is that you can do things in a lab that you think are you've really explored the full range of a problem so let me give you this example we were addressing the bin picking problem remember and we were dropping all kinds of objects in there in fact when people would come to the lab they would visit and i'd say well where do you have your car keys drop them in here i said uh if the robot will pick it out we'll keep the car and then uh but it would always do that it was no problem picking out someone's car keys right and we tried to talk all kinds of things again toys we made 3d printed weirdly shaped objects all kinds of things we could think of we try to basically consider everything and we were just trying to push the envelope right well the envelope was the keyword because it turned out that one thing we didn't ever really experiment with was bags oh and bags are extremely common in shipping yeah you probably receive if you know this if you receive bags from your from e-commerce from amazon or others you get bags of all kinds of forms now bags often plastic or paper but the problem the issue with bags is they're loose and so they have objects in them but there's a lot of slack and they tend to fold in interesting ways so we didn't we weren't testing those really in the lab that wasn't something that we would have thought about too much but that's so much more common in real shipping so my point is we had to adapt all of our systems to the reality of the consumer market, which in this case is bags.

42:42And that was something we didn't have a lot of data on. So we had to adapt our systems to work on data on real bags. And real bags are very difficult to actually even simulate and model because they fold again. And by the way, the folding matters because if you go to pick up with a suction cup right on top of a fold, as you lift it, the fold will unfold and you'll lose the suction and drop the object. Right. So we started collecting data as we started putting these robots to work. So as our customers were putting these systems into production at Ambi, right, we also had an agreement that we would maintain these systems very at high performance levels because we were constantly monitoring them.

43:22So we have a dashboard at the central headquarters in Berkeley where the team keeps an eye on every machine that's in operation out there. And so what we do is we get data on every single pick operation, what happens, how long it takes, whether it dropped the object, whether it was classified correctly, all kinds of things like that. Right. And we use that so that we can immediately tell when the performance, let's say the picks per hour performance, that's how it's often measured, drops. We can spot that early and say and we call the company and we say, what's going on? Did something change? Did the camera get knocked?

43:54Is the suction cup getting worn? and so we're constantly on top of it part of it is that that's a source of big pride for us we really customer focused and we want to make sure our machines work completely reliably but nice amazing side effect of this is that we've been able to collect data from all this real systems in real environments over the last four years and we now have a lapse on 22 years of robot data so remember i talked about the hundred thousand years yeah yeah now we have 22 years though and started okay but it's real robot data it's extremely valuable it's a high quality it's the gold standard for data and so we're now using that to refine our systems and that make them better and more higher performing more reliable but also allowing us to now branch out into new related types of products so we now have we introduced a new products called AmbiStack that stacks boxes very efficiently, very densely.

44:55And that's a new product that we sold out our first batch of these systems this year. Preston Pysh, MD, Amazing. On this idea of robot data or covering this 100 ,000-year gap that you're talking about, for a company that would be trying to overcome this because the data just isn't there, are they just having to construct a bunch of physical, real-world, going back to the hand example, would they have to have a bunch of physical hands with just a bunch of physical objects to then just be doing this? Or is this something that you think we could simulate in a virtual environment to accelerate that speed or kind of a combo of both?

45:34Good. So for grasping, it turns out simulations, it works pretty well because there you just need to know the geometry of the environment fairly accurately, and then the geometry of the object and the gripper. And then you can actually model that fairly well. So now grasping is just lifting an object off a table, okay? Or out of a bin. That's very different than tying the shoe that we talked about earlier, right? There, it turns out that we can't simulate that so well. As I mentioned, we don't know how to model and simulate the deformations, the minute forces that are going on in the process of interacting with that object.

46:11So that's a challenge. This is a little nuanced, And I know that your audience might say, what is Goldberg talking about? He said, this couldn't be solved. Now he says it can be solved. Well, it depends. There's certain categories of problems that can be addressed. And I think that picking objects out of a bin is something we've made an enormous amount of progress in the last five years. So I'm very optimistic about that. I think we're getting faster, more reliable. And those systems are, you know, that's the cutting edge of robotics. And it's real. but then tying shoes and doing things around a home by the way or in a factory where you're actually trying to put together you know electronic parts or car bodies or car installing upholstery and wiring inside a car these are extremely difficult by the way and they're even in detroit or anywhere in the world they're still humans doing those jobs because they're very very hard so those are hard to simulate and i do think it's everything's pointing toward this deformation is a key obstacle to doing it.

47:11And I've talked with people who are physicists and experts in deformation, and they agree this is a very, very hard problem. We don't even understand the physics of friction and deformation very well. Interesting. You've said your views on AI creativity have changed. Walk us through some of the timeline and what's changed and just kind of your overall opinion today? Okay. Well, on a very different note, I have been working as an artist in parallel with my work as a researcher and engineer. I like to say my day job is teaching at Berkeley and running a lab there, but I have another passion, which is making art.

47:50And I've worked on this for almost the same amount of time. And I make installations and projects. We did a project called the Telegarden, where we had a robot that was controlled by people over the internet and the robot could tend to garden a living garden we put this online in 1995 which was the very early days of the internet and i'm very proud of that project because it stayed online for nine years 24 hours a day people could come in and explore this garden and plant seeds and water them so it was a very interesting it was an artistic project but it was also a engineering proof of concept and it had to work reliably.

48:30And so, you know, it really pushed us. I sometimes say people think doing engineering is hard. Try art. It's really hard because you have to deal with the public and they're going to interact and do all kinds of crazy things. So we had to really spend a lot of time designing that system, but I continue an interest in art. And I have a new show coming up. It's a joint project with my wife, Tiffany Schlain, who's an artist and she and I are collaborating on a on a exhibition that's going to open in San Francisco in January 22nd okay all right so so this is a big passion of mine and it's using technology like AI and robots to ask questions about technology and I'm very interested in this contrast between the digital and the natural world when they they seem very symmetric and similar, but there's very profound differences between them.

49:22So that's what I think about or I try to express in my artwork. And so your question about the creativity. So I always said, AI won't be creative in the sense that you can ask it questions, but it's not going to actually come up with original ideas. But I actually have shifted my view on that. And I give this example where I I asked ChatGPT in the early days, hey, give me 100 uses for a guitar pick. I just thought it would start repeating the same thing over and over again. And it started with a screwdriver to scrape ice off a windshield, things like that, which all made sense. But then it started listing these as fast as I could read them or faster.

50:02And then it came up with one that I was like, it was a miniature sail for a toy boat. And when I saw that, I was like, oh my god that is a genius idea and i would not have thought of it and i you know immediately when you see something that's original and creative like that you spot it and you say ah why didn't i think of that those are those rare ideas and ai is capable of that now and so it's very exciting yeah it is exciting it's super exciting so i'm not in i'm not negative about ai at all i love it i use it i advocate for it my daughters my wife everyone uses it and so i'm 100 percent for it.

50:41I do think it's going to help with robotics, but the question is, is it going to do everything that people are hoping? And that's where I hope that this conversation, Preston, will put things into context for your audience. So I don't know if you're going to like this question or not, but I'm going to throw it over because I'm curious what you would think of this. So Figure AI recently sold their humanoid robot to put into the home. And there was a lot of pushback from at least the comments that I saw online that this was just a giant marketing scheme or maybe they're trying to raise their next round.

51:15I don't know. But for the audience, I'll just kind of frame it. It's a humanoid robot. All the demos that I've seen to date are extremely suspect as to its ability to actually do anything in the home. When you dig into it more, they were using, you're putting this thing in your house. And then I guess that it has this ability to go back to a human that would actually be manipulating the robot inside the house, which I think has all sorts of security, privacy issues, and everything else. But the reason I bring this up is because I don't know if he's, I'm pretty sure he's the founder and CEO of the company.

51:50He was suggesting that in order for AI to really start to accelerate It's learning that it needs to start being embodied into the physical form and to put itself into a challenging learning environment. And what he means by some of this, and Ken, correct me if I'm wrong in the way that I'm describing it, but what he's getting at is there's all these ambiguous situations that happen in the household with respect to social dynamics, the way that the family would interact and what they would ask of the robot, like, hey, go get me a cup of water. and then the person who's asking for it always likes it half full, or they like it warm, or they like it cold, or whatever.

52:29And so that learning that the robot would be forced to kind of undergo from a social dynamic would assist in its ability to get smarter collectively, because I'm sure all this information is then going back up into the mothership and getting networked. But his argument is the point of my question, which is, do you also agree that for AI to kind of take this next step or this next quantum leap from where it is today, that it really needs to be able to immerse itself and basically partition itself into the physical form. Well, I think that is helpful. And certainly understanding the dynamics of human interaction, especially in a home, is the social dynamics are very, very subtle.

53:18And as you said, very very important just understanding tone of voice gestures like my daughter will say you know does this right which is don't bother me she's a teenager okay or just rolls her eyes right like that oh yeah the world's right oh yeah yeah the subtle body language is super complex when you think about complex yeah we pick up on it in myriad of ways we don't even recognize right like i can pick up if one thing i always notice is when i'm teaching i can pick up if students are starting to lose interest or get tired or bored right yeah i just feel it you know i look around but i'm always watching that's why it's too tricky to teach online but all these things are very nuanced um body language can tell you a lot of what's going on and just interpreting what's the dog doing and what's the dog you know how's the dog feeling right there's all a lot of nuance there so all that is you're gonna you have to be in real homes to be able to do that and i think that's actually that makes sense i'm not opposed to having let's say a humanoid in a home that might be helpful for doing certain things like maybe fetching water or being able to pick up things around the house remember grasping i said that is actually something i think robots can do so if you said hey you know pick up all the things that are on the floor right we would all like that we have a Roomba you know you mentioned earlier the vacuum cleaners but the next step is to be able to actually pick things up and put them away yeah and that I think we can get there I do I actually think that's going to come that can happen in the next decade and it's very valuable because by the way if you're a senior citizen you really want things off the floor and if you're a young parent you have a lot of kids you have kids or if you have a teenager there's like can you clean your room you know that'd be great to have a robot go in and just clean off the pick up all the clothes we call that the teenagers problem by the way we have a paper okay okay which is how to get a robot to efficiently pick up clothes and it's not the obvious thing because you know if you just program a robot and go in and pick up it'll pick up one sock take it to the bin to pick up the next stock take it to the bin you actually need to be able to pick up lots of socks together and so how do you do that that's called multi-object grasping it's a very complex and nuanced topic and so we're studying that in the lab so just coming back to your bigger point.

55:36I think that robots will do something useful in the house. I think that's possible. They could be useful for security. Also, maybe in some form of companionship somewhere down the road, or as you get older, and I can appreciate this more and more, that I might want to have a robot that might help me shower or get changed or help me get out of bed in the morning. I think that would be nice. I'd rather have that than a stranger in my house. Let me put it that way. right i just don't relate just not one that's networked back to some other person on the control yeah right i mean well that the privacy issues are huge you're right and that's something also a lot of engineers don't appreciate that i faced this at berkeley a few years ago where i was talking about privacy and i made an art project about privacy and we had cameras surveillance cameras we did a whole installation about this and some of my friends were like i don't care about privacy i have nothing to hide i said oh really i said okay can i see all the letters of recommendation you wrote for the last 10 years?

56:33Oh, no. I'm not going to share those. I said, okay, how about all the research proposals that you're working on? Oh, no. I can't share those. Right. It's all kinds of stuff that you don't want to share. It's not that you're hiding. It's not that you're doing something criminal or embarrassing, but there's a lot of stuff you don't care to share because it's important and it's confidential. So same as in your home. It's not that it's going to catch me naked, but in the morning, I have bad hair day and I don't necessarily want that to be transmitted widely. So it's all kinds of things like that. So I'm not opposed to humanoid robot.

57:07I think it's going to be interesting to see what happens in the next few years, that we will probably start to see these. It'll be very interesting to see that roll out from figure. And Berndt is very, you know, he's a very compelling businessman. Like Elon, you know, he has a lot of optimism, a lot of confidence, and he's definitely building something that's working to some degree. So it'll be interesting to see. And, you know, I'm not a naysayer. I'm not saying that all this is going to fail. I just say that be patient. You know, it's going to take longer. The real science fiction stuff is going to take longer than we think.

57:38Last question I got for you, Ken, what's the most exciting or surprising thing that you've seen in the lab or just in the space in general that you almost gasped when you saw it in the past call it year okay so actually i have a good answer for that you know i'm so proud of ambi for being able to sort packages around the clock at very high speeds right but i recently saw a company called dyna dyna robotics okay and i'm friends with the founders so i'm maybe slightly biased but i have to tell you they demonstrated folding napkins with a robot okay and they did it for 24 hours so they just had the camera set up and they had and now this is by the way just two grippers two grippers okay right no head but it has cameras but there are two grippers basically folding a stack of napkins over and over again and they did it for 24 hours and they showed you the whole process now that to me as a roboticist is a big deal because they were able to do it fairly fast reliably the napkins were you know often get tangled up and it would figure out how to untangle them and keep going and the folds were actually pretty nice so that's impressive And then I got to see a live demo of the new version of that, which can now fold shirts.

58:47And it worked really well. I saw this in a, they had a booth in a conference in Korea in September, and it was folding shirts as it just round the clock. And it was fantastic. Even you could bring your own t-shirt and it would fold it. So that to me, that's very exciting that that shows. And again, it's a specific task. I do think we're going to make progress there. And by the way, everyone wants to have something fold their clothes. That's for sure. That is for sure. I'm so bad. I know. I'm so bad I got one of those little folding. Oh, you do? Yes, I do. Because I'm so bad at folding it. But when I use that, I'll actually, you know, do it.

59:23Oh, you have a folding board. Okay. Yes, I do. And I'm sure that - So you're going to be a great customer for this. But you know, you have high standards, right? Because you want the things just right. And that's where it gets tricky, right? But the Dyna Robotics guys, it's Jason and Lyndon, who are the leaders of this company. They really are pulling something off that I think is very interesting to keep an eye on. And a lot of the other robotics companies are now trying to emulate that, which is to show one task, one special task, doing it very reliably, making coffee or folding boxes. That's really exciting.

59:53And I think that is actually going to be important. Rather than trying to do general robotic, do everything in a home, which is, I think, going to take a long, long time. But if you get it to do certain tasks, like folding laundry, or maybe making coffee, certain things like that, that's a way sort of bottom up from certain tasks. learn other tasks rather than top down. I think that's going to be a path to getting progress. But again, it's going to take longer than most people think. Preston Pyshko, I can't thank you enough for making time. Your expertise is just off the charts. And I know the audience is going to love this.

1:00:27If you have anything else you want to highlight or point people towards that we can put in the show notes, just let us know what that is. Preston Pyshko, Okay. I'll send you some links because I have a bunch of things online I can to with that follow up on this in various ways. And no, I think it's great. Thanks for doing this. I'm really glad you're also going to connect with my good friend, Rich Wallace. Yes. Yes. Because he is fascinating. You know, he's a very, very original thinker and very, I would say very much an unsung hero around chatbots. People don't know, but he was really a pioneer in doing this, you know, very early.

1:00:59And he still has a lot of great, really interesting insights and ideas. So you'll appreciate it. Amazing. All right. Well, Ken, thank you so much for making time and coming on the show. My pleasure, Preston.

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