Lex Fridman Podcast #374 - #374 – Robert Playter: Boston Dynamics CEO on Humanoid and Legged Robotics Machine transcription by Qwen3-ASR 0.6B (MLX, local, Apache-2.0). May contain recognition errors. The following is a conversation with Robert Plater, CEO of Boston Dynamics, a legendary robotics company that over thirty years has created some of the most elegant, dexterous and simply amazing robots ever built, including the humanoid robot Atlas and the robot dog Spot, one or both of whom you've probably seen on the internet either dancing, doing backflips, opening doors or throwing around heavy objects. Robert has led both the development of Boston Dynamics humanoid robots and their physics based simulation software. He has been with the company from the very beginning, including its roots at MIT, where he received his PhD in aeronautical engineering. This was in nineteen ninety four at the legendary MIT Leg Lab. He wrote his PhD thesis on robot gymnastics, as part of which he programmed a bipedal robot to do the world's first three D robotic somersault. Robert is a great engineer, roboticist, and leader, and Boston Dynamics to me as a roboticist is a truly inspiring company. This conversation was a big honor and pleasure, and I hope to do a lot of great work with these robots in the years to come. And now a quick few second mention of each sponsor. Check them out in the description. It's the best way to support this podcast. We got NetSuite for business management software, Linux for Linux systems, and Element for zero sugar electrolytes. Choose wisely, my friends. Also, if you want to work with our team, we're always hiring. Go to lexfreeman dot com slash hiring. And now onto the full ad reads. As always, no ads in the middle. I try to make this interesting, but if you must commit the horrible terrible crime of skipping them, please do check out our sponsors. I do enjoy their stuff, I really do, and maybe you will as well. This show is brought to you by NetSuite, an all in one cloud business management system. Running a business, as this podcast reveals, from Robert Plater and Boston Dynamics is really hard. It's not just about the design of the systems, it's not just about the engineering, the software, the hardware, all the complicated research that goes into it, all the different prototypes, all the failure upon failure upon failure in the early stages in the middle stages of getting these incredible robots to work. It's all the it's all the glue that ties a company together, and for that you have to use the best tools for the job. I hope to run a business, a large business that actually builds stuff one day, and boys and much more than just the innovation and the engineering. I understand that deeply, and you should be hiring the best team for that job and use the best tools for that job and that's where NetSuite can help out. 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I've eaten ice cream with Dairy Queen so many times in my life, especially through my high school years, and to contradict what I just said, I regret nothing. I think Snickers and if memory serves me correctly, there is something called the Dairy Queen Blizzard, where you could basically shove in whatever you want into the ice cream and blend it and it tastes delicious. Like I think my favorite would be like the Snickers bar, any kind of bar Mars bar, and anything with kind of chocolate caramel, maybe a little bit coconut. That that kind of stuff, you know I don't regret it, but we've experimented. All of us have experimented with different flavors, with different things in life, and I regret nothing. You should not regret any of it either because that path is what created the beautiful person that you are today. And that path is also the reason I mostly drink the watermelon flavor of I guess it's called watermelon salt. I don't know what it's called, but watermelon is in the word of Element. I highly recommend it. You can try other flavors, chocolate is pretty good too, like chocolate mint. I think it's called. Um, totally different thing. All the flavors are very different, and that's why I love it. So you should explore anyway. It's a good way to get all the electrolytes in your system, the salt, the magnesium, the potassium not salt sodium is what I meant to say. It doesn't matter what I meant to say. What matters is it's delicious and I am consuming it and I am singing it praises and I will toast you when we see each other in person one day friend. And we should drink Element drink to our deepest fulfillment together as brothers and sisters in arms. Get a simple pack for free with any purchase. Try it at drinkelement dot com slash lex. This is the Lex Friedman podcast to support it. Please check out our sponsors in the description. And now dear friends, here is Robert Plater. When did you first fall in love with robotics? Let's start with love and robots. Well, love is is relevant because I think the the fascination the deep fascination is really about movement. And I was visiting MIT looking for a place to get a PhD, and I wanted to do some laboratory work, and one of my professors in the Aero Department said, "Go see this guy Mark Rebert down in the basement of the AI lab." And so I walked down there and saw him. He showed me his robots, and he showed me this robot doing a somersault, and I just immediately went, "Whoa, you know robots can do that and because of my own interest in in gymnastics there was like this immediate connection." And you know I was interested in I was in an Aero Astro degree because you know flight and movement was all so fascinating to me and then it turned out that you know robotics had this big challenge how do you how do you balance how do you how do you build a legged robot that can really get around. And that just that was a fascination and it still exists today. You are still working on perfecting motion in robots. What about the elegance and the beauty of the movement itself? Is is there something maybe grounded in your appreciation of movement from your gymnastics days? Did you was there something you just fundamentally appreciated about the elegance and beauty of movement? You know we had this concept in in gymnastics of letting your body do what it wanted to do when you get really good at gymnastics. Part of what you are doing is putting your your body into a position where the physics and the body's inertia and momentum will kind of push you in the right direction in a very natural and organic way. And the thing that Mark was doing, you know, in the basement of that laboratory, was trying to figure out how to build machines to take advantage of those ideas. How do you build something so that the physics of the machine just kind of inherently wants to do what it wants to do and he was building these springy pogo stick type. You know his first cut at legged locomotion was a pogo stick where it's bouncing and there is a spring mass system that's oscillating has its own sort of natural frequency there and sort of figuring out how to augment those natural physics. With also intent, how do you then control that but not overpower it? It's that coordination that I think creates real potential. We could call it beauty, you could call it I don't know synergy. People have different words for it, but I think that that was inherent from the beginning that was clear to me that that that's part of what Mark was trying to do. He asked me to do that in my research work so you know that's where it got going. So part of the thing that I think I am calling elegance and beauty in this case, which was there even with the pogo stick is maybe the the efficiency so letting the body do what it wants to do. Trying to discover the efficient movement. It's definitely more efficient. It also becomes easier to control in its own way because the the physics are solving some of the problem itself. It's not like you have to do all this calculation and overpower the physics. The physics naturally inherently want to do the right thing. There can even be you know feedback mechanisms stabilizing mechanisms that occur simply by virtue of the physics of the body and it's you know not all not all on the computer or not even all in your mind as a person. And I there is something interesting in that that melding. You were with Mark for many many many years, but you were there in this kind of legendary space of Leg Lab and MIT in the basement. All great things happen in the basement. Is there some memories? Is there some memories from that time that you have because it's so it's such cutting edge work in in in robotics and artificial intelligence. The memories, the distinctive lessons I would say I learned in that in that time period, and and that I think Mark was a great teacher of. Was it's okay to pursue your interest, your curiosity, do something because you love it. You'll do it a lot better if you love it. Um, that that is a lasting lesson that I think we apply at the company still, um and really is a core value. So the interesting thing is I got to. Oh, with people like Rostedrek and others like the students that work at those robotics labs are like some of the happiest people I've ever met. I don't know what that is. I meet a lot of PhD students, a lot of them are kind of broken by the wear and tear of the process, but roboticists are while they work extremely hard and work a long hours. There is a there is a happiness there. The only other group of people I met like that are people that skydive a lot. Like for for some reason there is a deep fulfilling happiness maybe from like a long period of struggle to get a thing to work and it works and there is a magic to it I don't know exactly. Because it's so fundamentally hands on and you are bringing a thing to life I don't know what it is but they're happy. We see you know our our attrition at the company is really low. People come and they love the pursuit, and I think part of that is that there is perhaps an external connection to it. It's a little bit easier to connect when you have a robot that's moving around in the world and part of your goal is to make it move around in the world. You can identify with that and and this is on this is one of the unique things about the kinds of robots we're building is this physical interaction lets you perhaps identify with it. So I think that is a source of happiness. I don't think it's unique to robotics. I think anybody also who is just pursuing something they love. It's easier to work hard at it and be good at it and um not everybody gets to find that I I do feel lucky in that way and I think we're lucky as an organization that we've been able to build a business around this and that keeps people engaged. So if it's all right, let's linger on Mark for a little bit longer. Mark Rebert, so he's a legend. He's a legendary engineer and roboticist. What what have you learned about life about robotics from Mark through all the many years you worked with him? I think the most important lesson, which was you know, have the courage of your convictions and and do what you think is interesting. Um, be willing to try to find big big problems to go after, and at the time you know legged locomotion, especially in a dynamic machine, nobody had solved it and that felt like a multi decade problem to go after. And so you know have the courage to go after that because you are interested. Don't worry if it's going to make money. You know that that's been a theme so that that's really probably the most important lesson I think that I got from Mark. How crazy is the effort of doing legged robotics at that time, especially? You know Mark got some stuff to work starting from the simple ideas. So maybe the other another important idea that has really become a value of the company is try to simplify a thing to the core essence. And and while you know Mark was showing videos of animals running across the Savannah or climbing mountains, what he started with was a pogo stick because he was trying to reduce the problem to something that was manageable and and getting the pogo stick to balance had in it. The fundamental problems that if we solve those, you could eventually extrapolate to something that galloped like a horse, and so look for those simplifying principles. How how tough is the job of simplifying a robot? So I'd say in the early days, the the thing that made Boston, the researchers at Boston Dynamics special, is that we we worked on figuring out what that that central principle was. And then building software or machines around that principle and that was not easy in the early days and and it it took real expertise in understanding the dynamics of motion and feedback control. Principles, how to build and with computers at the time, how to build a feedback control algorithm that was simple enough that it could run in real time at a thousand Hertz and actually get that machine to work. Um, and that was not something everybody was doing you know at that time. Now the world's changing now and I I think the approaches to controlling robots are going to change, um but it and they're going to become more broadly available. Um, but at the time there weren't many groups who could really sort of work at that principled level with both the software and and make the hardware work. And I'll say one other thing about you are sort of talking about what are the special things. The other thing was it's it's it's good to break stuff you know, um you know, use the robots, break them, repair them, um you know, fix and repeat, test fix and repeat and that and that's also a core principle that has become part of the company. And it lets you be fearless in your work. Too often if you are working with a very expensive robot, maybe one that you bought from somebody else or that you don't know how to fix, then you treat it with kit gloves and you can't actually make progress. You have to be able to break something and so I think that's been a a principle as well. So just a linger on that psychologically. How do you deal with that? Because I remember I had I built a RC car with some had some custom stuff like compute on it all that kind of stuff cameras. And because I didn't sleep much, the code I wrote had an issue where it didn't stop the car and had the car got confused and at full speed at like twenty twenty five miles an hour slammed into a wall. And I just remember sitting there alone in a deep sadness, sort of full of regret. I think almost anger, but also like sadness because you think about well these robots especially for autonomous vehicles like like you should be taking safety very seriously even in these kinds of things. But just no good feelings, and made me more afraid probably to do this kind of experiments in the future. Perhaps the right way to have seen that is positively. Like it's it's too. It depends if you could have built that car or or just gotten another one right that would have been the approach. I remember when I got to grad school, you know I got some training about operating a lathe and a mill up in the machine shop, and I could start to make my own parts. And I remember breaking some piece of equipment in the lab and then realizing because I maybe this was a unique part and I couldn't go buy it and I realized oh I can just go make it. That was an enabling feeling. Then you are not afraid. You might take time. It might take more work than you thought it was going to be required to get this thing done, but you can just go make it and that's freeing in a way that nothing else is. You mentioned the the feedback control the dynamics. Sorry for the romantic question, but is in the early days and even now is the dynamics probably more appropriate for the early days? Is it more art or science? There is a lot of science around it and. And trying to develop you know scientific principles, that let you extrapolate from like one legged machine to another, you know, develop a core set of principles like, like a spring mass bouncing system, and then figure out how to apply that from a one legged machine to a two or a four legged machine, those principles are really important and and and we're definitely a core part of our work. Um, there's also, you know, when we started to pursue humanoid robots, um, there was so much complexity in that machine, that, you know, one of the benefits of the humanoid form is you have some intuition about how it should look while it's moving, and that's a little bit of an art I think. Now it's or maybe it's just tapping into a knowledge that you have deep in your body, and then trying to express that in the machine, but that's an intuition that's a little bit more on the art side, maybe it it predates your knowledge, you know, before you have the knowledge of how to control it, you try to work through the art channel, and humanoid sort of make that available to you, if it had been a different shape, maybe you wouldn't have had the same intuition about it. Yeah, so your knowledge about moving through the world is not made explicit to you, so you just that's why it's art, you know, might yeah might be hard to actually articulate exactly, you know, there's something about and being a competitive athlete, there's something about seeing movement, you know, a coach, one of the greatest strengths a coach has is being able to see, you know, some little change in what the athlete is doing, and then being able to articulate that to the athlete, you know, and then maybe even trying to say, and you should try to feel this. Um, so there's something just in seeing, and again, you know sometimes it's hard to articulate what it is you're seeing, but there's a, just perceiving the motion at a rate that is, again sometimes hard to put into words. Yeah, I wonder, how it is possible to achieve sort of truly elegant movement. You have a movie like X Machina, I'm not sure if you've seen it, but the main actress in that who plays the AI robot, I think is a ballerina. I mean just the natural, elegance and the, I don't know, eloquence of movement. It's it's, it looks efficient and easy and, just it looks right. It looks right is sort of the key yeah. And then you you look at especially early robots, I mean they they are so cautious in in the way they move, that, it's not it's not the caution that looks wrong, it's it's something about the movement that looks wrong that feels like, it's very inefficient, unnecessarily so, and it's hard to, put that into words exactly. We think, and part of the reason why people are attracted to the machines we build, is because the inherent dynamics of movement are, are closer to right, um because we we try to use, you know, walking gates or we build a machine around this gate where you're trying to work with the dynamics of the machine instead of, to stop them, you know some of the early walking machines, you know you're essentially, you're really trying hard to not let them fall over and so you're always stopping the tipping motion, you know, and sort of the insight, of dynamic stability in a legged machine is to go with it, you know, let the tipping happen, you know, let yourself fall, but then catch yourself with that next foot, and there's something about getting those physics to be expressed in the machine, that people interpret as, lifelike, or or elegant, or just natural looking, and so I think if you get the physics right, it also ends up being more efficient likely, there's a benefit that it probably ends up being, more stable in the long run, you know it could it could walk stably over a wider a range of conditions, um and it's and it's more beautiful and attractive at the same time. So how hard is it, to get the humanoid robot Atlas, to do some of the things it's recently been doing? Let's forget the flips and all of that, let's just look at the running. Maybe you can correct me, but there's something about running. I mean that's not careful at all. That you're falling forward, you're jumping forward and are falling. So how hard is it to get that right? Our first humanoid, we needed to deliver natural looking walking. You know we took a contract from the army. They wanted a robot that could, walk naturally. They wanted to put, a suit on the robot and be able to test it in a gas environment, and so they wanted the the motion to be natural. Um and so our goal was a natural looking gate. It was it was surprisingly hard to get that to work, um and we but we did build an early machine, we called it Petman prototype, it was the prototype before the Petman robot, and it had a really nice looking gate where, you know it would stick the leg out, it would do heel strike first before it rolled onto the toe, so you didn't land with a flat foot, you extended your leg a little bit, um but even then it was hard to get the robot to walk where when when you're walking that it fully extended its leg and essentially landed on an extended leg and if you watch closely how you walk you probably land on an extended leg but then you immediately flex your knee as you start to make that contact, and getting that all to work well, took such a long time in fact, I probably didn't really see the nice natural walking that I expected out of our humanoid until maybe last year, and the team was developing on our newer generation of Atlas, you know some new techniques, um for developing a walking control algorithm and they got that natural looking motion as sort of a byproduct of a of a just a different process they were applying to developing the control, so that probably took 15 years 10 to 15 years to sort of get that from from you know, the Petman prototype was probably in 2008 and what was it 2022 last year that I think I saw good walking on Atlas. If you could just like linger on it, what are some challenges of getting good walking? So is it, is this is this partially like a hardware like actuator problem? Is it the control? Is it the artistic element of just observing the whole system operating in different conditions together? I mean is there some kind of interesting, quirks or challenges you can speak to like the heel strike or yeah so one of the things that makes the like this straight leg a challenge, is you're sort of up against a a singularity a mathematical singularity where you know when your leg is fully extended it can't go further the other direction right there's only you can only move in one direction and that makes all of the calculations around how to produce, torques at that joint or positions makes it more complicated and so having all of the mathematics so it can deal with, these singular configurations is one of many challenges, that we face, and I'd say in in the, you know in those earlier days again we were working with these really simplified models, so we're trying to boil all the physics of the complex human body into a simpler subsystem that we can more easily describe in mathematics and sometimes those simpler subsystems don't have all of that complexity of the straight leg built into them, and so what what's happened more recently is we're able to apply techniques that let us take the full physics of the, robot into account and and deal with some of those strange situations like the like the straight leg. So is there a fundamental challenge here that it's, maybe you can correct me but is it underactuated? Are you falling? Underactuated is is the right word right you can't you can't push the robot in any direction you want to right and so that that is one of the hard problems of of legged locomotion and you have to do that for natural movement. It's not necessarily required for natural movement. It's just required, you know we we don't have you know a gravity force that you can hook yourself onto to apply an external force in the direction you want at all times right the only the only external forces are being mediated through your feet and how they get mediated depend on how you place your feet, and you know you can't just, you know God's hand can't reach down and give and push in any direction you want, you know so is there is there some extra challenge to the fact that Atlas is such a big robot. There is the humanoid form is, um attractive in many ways but it's also a challenge in many ways, um you have this big upper body that has a lot of mass and inertia, um and throwing that inertia around increases the complexity of maintaining balance, and as soon as you pick up something heavy in your arms you've made that problem even harder, and so in the early work in the leg lab and in the early days at the company, you know we were pursuing these quadruped robots which had a, a kind of built in simplification you had this big rigid body and then really light legs, so when you swing the legs, the leg motion didn't impact the body motion very much, all the mass and inertia was in the body but when you have the humanoid that doesn't work you have big heavy legs you swing the legs it affects everything else, and so dealing with all of that interaction, does make the humanoid a much more complicated platform, and I also saw that at least recently you've been doing more explicit modeling of the stuff you pick up, yeah, just very, um really interesting so you have to what model the shape, the weight distribution, I don't know what like you have to under like include that as part of the modeling as part of the planning because okay so for people who don't know, uh so Atlas at least in like a recent video like throws a heavy bag throws a bunch of stuff, so what what's involved in picking up a thing a heavy thing, uh and when that thing is a bunch of different non standard things I think they also picked up like a barbell, and to be able to throw in some cases what are some interesting challenges there, so we were definitely trying to show that the robot and the techniques we're applying to the robot to Atlas, let us deal with heavy things in the world because if the robot's going to be useful it's actually got to move stuff around and that and that needs to be significant stuff, that's an appreciable portion of the the body weight of the robot, and we also think this differentiates us from the other humanoid robot activities that you're seeing out there mostly they're not picking stuff up yet, and not heavy stuff anyway, um but just like you or me you know you need to anticipate that moment you know you're reaching out to pick something up and as soon as you pick it up your center of mass is going to shift, and if you're going to you know turn in a circle you have to take that inertia into account and if you're going to throw a thing you know you've got all of that has to be sort of included in in the model of what you're trying to do, so the robot needs to have some idea or expectation of what that weight is and then and sort of predict, you know think a couple of seconds ahead how do I manage my now my my body plus this big heavy thing together to get and and still maintain balance, right and so, I, I, that's a big change for us and I think the tools we've built are really allowing that to happen quickly now some of those motions that you saw in that most recent video we were able to create in a matter of days it used to be it took six months to do anything new you know on the robot and and now we're starting to develop the tools that let us do that in a matter of days and so, we think that's really exciting that means that the ability to create new behaviors for the robot is going to be, a quicker process. So being able to explicitly model, new things that it might need to pick up new types of things and you know to some degree you don't you don't want to have to pay too much attention to each specific thing right there's sort of a generalization here, obviously when you grab a thing you have to conform your your hand your end effector to the surface of that shape but once it's in your hands, it's probably just the mass and inertia that matter and the the shape may may not be as important, and so you know for some in some ways you want to pay attention to that detailed shape, in in others you want to generalize it and say well all I really care about is the center of mass of this thing especially if I'm going to throw it up on that scaffolding, and it's easier if the body is rigid what if it's there's some, doesn't it throw like a sandbag type thing that tool bag you know had loose had loose stuff in it so it it managed that there are harder things that we haven't done yet you know we could have had a big jointed thing or I don't know a bunch of loose wire or rope what about carrying another robot how about that, yeah we haven't we haven't done that yet carry spot I guess we did a little bit of a we did a a little skit around Christmas where we had, two spots holding up another spot that was trying to put you know a bow on a tree so I guess we're doing that in a small way, okay that's pretty good let me ask the all important question, do you know how much Atlas can curl? Have you, I mean you know this for us humans that's really one of the most fundamental questions you can ask another human being. Bench, it probably can't curl as much as we can yet but a metric that I think is interesting is you know another way of of looking at that strength, is you know the box jump so if how high of a box can you jump onto? Question, and Atlas I don't know the exact height it was probably a meter high or something like that it was a pretty pretty tall jump that Atlas was able to manage when we last tried to do this and and I have video of my chief technical officer, doing the same jump and he really struggled you know oh the human but the human getting all the way on top of this box but then you know Atlas was able to do it, um, we're now thinking about the next generation of Atlas and we're probably going to be in the realm of a person can't do it you know with this with the next generation the the robots the actuators are going to get stronger where, it really is the case that at least some of these joints some of these motions will be stronger, and to understand how high I can jump you probably had to do quite a bit of testing. Oh yeah and there's lots of videos of it trying and failing and that's you know that's all you know we don't always release those those videos but they're a lot of fun to look at, so we'll talk a little bit about that, but if can you talk to the jumping because you talked about the walking it took a long time many many years to get the walking to be natural, but there's also really natural looking, robust resilient jumping how hard is it to do the jumping? Well again this stuff has really evolved rapidly in the last few years you know the first time we did a somersault, um you know there was a lot of kind of manual iteration, what is the trajectory you know how hard do you throw you in fact in these early days, I actually would when I'd see early experiments that the team was doing I might make suggestions about how to change the technique again kind of borrowing from my own intuition about how backflips work, um, but frankly they don't need that anymore so in in the early days you had to iterate, kind of in almost a manual way trying to change these trajectories of the arms or the legs, to try to get you know a successful backflip to happen, but more recently we're running, these model predictive, control techniques, where we're able to the robot essentially can think in advance for the next second or two about how its motion is going to transpire and you can you know solve for optimal trajectories to get from A to B, so this is happening in a much more natural way and and we're really seeing an acceleration happen in the development of these behaviors, again partly due to these optimization techniques sometimes learning techniques, um so it's there's it's hard in that there's a lot of mathematics and, behind it, but we're figuring that out so you can do model predictive control for, uh I mean I don't even understand what that looks like when the entire robot is in the air, flying and doing a back, yeah I mean, but but but that's the cool part right so you know you know the physics we we can calculate physics pretty well using you know Newton's laws about how it's going to evolve over time and the road you know this this the sick trick which was a front somersault with a half twist is a good example right, you saw the robot on various versions of that trick I've seen it land in different configurations and it still manages to stabilize itself and so, you know what this model predictive control means is again the in real time the robot is, projecting ahead you know a second into the future and sort of exploring options and if I if I move my arm a little bit more this way how is that going to affect the outcome and so it can do these calculations many of them, you know and and basically solve where given where I am now maybe I took off a little bit screwy from how I had planned I can adjust, so you're adjusting in the air just on the fly so the the model predictive control lets you adjust on the fly, and of course I think this is what you know people adapt as well we we when when we do it even a gymnastic trick we try to set it up so it's it's close to the same every time but we figured out how to do some adjustment on the fly and now we're starting to figure out that the robots can do this adjustment on the fly as well using these techniques in the air, that's so I mean it just feels from a robotics perspective just surreal. Well that's sort of you talked about underactuated right so when you're when when you're in the air there's something there's some things you can't change right you can't change the momentum while it's in the air because you can't apply an external force or torque and so the momentum isn't going to change so how do you work within the constraint of that fixed momentum to still get from A to B, where you want to be that's really underactuated, you're in the air I mean you you become a drone for a brief moment in time no you're not even a drone because you can't can't hover you can't hover you can't you're gonna you're gonna impact soon be ready yeah have you considered like a hover type thing or no no it's too much weight, I mean it's just it's just incredible, and just even to have the guts to try backflip. It was such a large body. That's wild. But like, we definitely broke a few robots trying. But that, but that's where the build it break it fix it you know strategy comes in. You got to be willing to break. And what ends up happening is you end up by breaking the robot repeatedly you find the weak points and then you end up redesigning it so it doesn't break so easily next time you know. Through the breaking process you learn a lot like a lot of lessons and you keep improving not just how to make the backflip work but everything and how to build a machine better yeah yeah. I mean is there something about just the guts to come up with an idea of saying you know what let's try to make it do a backflip. Well I think the courage to do a backflip in the first place and and to not worry too much about the ridicule of somebody saying why the heck are you doing backflips with robots because a lot of people have asked that you know why why why are you doing this why go to the moon in this decade and do the other things JFK. Not because it's easy because it's hard yeah exactly. Don't ask questions okay so the the jumping I mean it's just there's a lot of incredible stuff if we can just rewind a little bit to the DARPA robotics challenge in 2015 I think which was for people who are familiar with the DARPA challenges. It was first with autonomous vehicles and there's a lot of interesting challenges around that and the DARPA robotics challenge was when humanoid robots were tasked to do all kinds of. You know manipulation, walking, driving a car, all these kinds of challenges. With if I remember correctly, sort of some slight capability to communicate with humans, but the communication was very poor. So basically has to be almost entirely autonomous. It could have periods where the communication was entirely interrupted and the robot had to be able to proceed.