Speaker 1: Brought to you by Toyota. Let's go places. Welcome to
Speaker 1: Forward Thinking. Hey everyone, and welcome to Forward Thinking, the
Speaker 1: podcast that looks at the future. It says, mama's don't
Speaker 1: let your babies grow up to be robots. I'm Jovin
Speaker 1: Strickland and I'm Joe McCormick. And have you two ever
Speaker 1: noticed how much better robots are than organic life forms
Speaker 1: all the time. That's why I only work with various
Speaker 1: things that allow me to interact with machines as opposed
Speaker 1: to actual human beings. Like if it's a prerecorded type
Speaker 1: message and I navigate through a menu using buttons, and
Speaker 1: I don't have to talk to a person, that's awesome.
Speaker 1: They're certainly less offensively smelly. Well that's just because we
Speaker 1: haven't mastered the artificial intelligence sector of creating really lifelike
Speaker 1: bo Yeah, well we have, we have scientists working on that. Yeah, yeah, no,
Speaker 1: I was kidding. But there are, of course situations where
Speaker 1: it's much better to be a robot than to be
Speaker 1: an organic life form, and we've talked about plenty of
Speaker 1: them on the podcast before. Maybe in space exploration where
Speaker 1: you're at risk, or in or in search and rescue
Speaker 1: scenarios where again a human would be at risk or
Speaker 1: you know, any number of hundreds of others. Really, Yeah,
Speaker 1: just just performing the same physical job. Anything that would
Speaker 1: make a human board or or repeated stress injury, that
Speaker 1: kind of stuff. I mean things that where you do
Speaker 1: it once, it doesn't hurt, but you do it, you know,
Speaker 1: once every minute for eight hours a day, and it's
Speaker 1: and eventually, yeah, the button pushing muscles start to wear
Speaker 1: down in the bones and etcetera. Yeah, robots don't get
Speaker 1: don't get physically injured, they don't get emotionally injured. It's
Speaker 1: real hard to traumatize a robot. Yeah, I know. Now,
Speaker 1: we we've talked about the advantages. Of course, the life
Speaker 1: forms have over robots and limited in each individual case.
Speaker 1: So a person like a worker is a lot easier
Speaker 1: to train for a new task than a robot is. Well,
Speaker 1: depending on if if the robot is mechanically suited to
Speaker 1: the new task, you can reprogram it s well. But
Speaker 1: but a worker can learn many new tasks. Yeah, hypothetically,
Speaker 1: most robots are pretty good at doing one thing right, right, Um, Yeah, robots,
Speaker 1: as it turns out, they pretty much can do what
Speaker 1: you built them to do, and they're pretty bad at
Speaker 1: doing anything outside of that. In general, right, Robots don't
Speaker 1: really evolve. Yeah, so yeah, so humans are more adaptable
Speaker 1: on the individual scale, but they're also more adaptable as
Speaker 1: a type of thing. Sure, over time, if the air
Speaker 1: pressures from the environment on a living organism, it will evolve,
Speaker 1: it will adapt, or it might go extinct. But assuming
Speaker 1: it doesn't go extinct, it will adapt and evolve to
Speaker 1: to fit the environment that it needs to make its
Speaker 1: living in. Robots don't do that, no, they remain. Yeah,
Speaker 1: so that's what we're going to talk about today exactly.
Speaker 1: So let's talk about like, like general robots right now.
Speaker 1: They they are too pretty distinct pathways for robot design
Speaker 1: in general, and of course there are a lot of
Speaker 1: branches within these, but we can go one path where
Speaker 1: we design a robot for a specific task and that's
Speaker 1: all it is supposed to do. So rumba is a
Speaker 1: great example. What a rumba is supposed to do is
Speaker 1: clean the floor. It's not supposed to do anything else
Speaker 1: other than it doesn't clean the cat box. In my opinion,
Speaker 1: this is this is probably like of robots and all
Speaker 1: of the good ones. Yeah, the most efficient and the
Speaker 1: most effective robots, I would argue, are of this type
Speaker 1: because we only have to concern ourselves with the design
Speaker 1: elements that will enable the robot to complete its task,
Speaker 1: and we eliminate everything else. Right, you don't need to
Speaker 1: include anything that doesn't involve cleaning the floor, or navigation
Speaker 1: or returning to like a docking station to recharge. When
Speaker 1: you're designing a robot like the Rumba, you just need
Speaker 1: those basic elements in it. Anything else is superfluous. Uh.
Speaker 1: Doesn't stop us from doing things like having crazy Rumba
Speaker 1: fights where we put a balloon and something sharp on
Speaker 1: on a couple of them and have them joused each other.
Speaker 1: But it doesn't mean we don't put our cats on
Speaker 1: top of them and just watch what happen. Maybe put
Speaker 1: our cat in a shark costume first and then put
Speaker 1: the cat on top of a roomba. Yeah, that sort
Speaker 1: of thing. But in general, they just do what they
Speaker 1: were supposed to do, and if you wanted them to
Speaker 1: do anything else, you're kind of out of luck. Or
Speaker 1: we tried to build general purpose robots that are capable
Speaker 1: of doing lots of different tasks hopefully yeah, to varying
Speaker 1: degrees of mediocrity because it's hard. It's not because you know, No, yeah,
Speaker 1: it's not because the people working on these aren't smart. Yes,
Speaker 1: because the job is a thousand times more difficult, right,
Speaker 1: because you have to anticipate lots of different things, a
Speaker 1: lot of different changing conditions. This would be the example
Speaker 1: of the darker robots we talked about previously, where UH
Speaker 1: challenge robots exactly. Yes, the DARPA Challenge robots, the ones
Speaker 1: that had to replicate a uh kind of a first
Speaker 1: responder situation, search and rescue kind of. Yeah. They to
Speaker 1: do like drive a car to a building, open a door,
Speaker 1: go inside, twist to lever, and cut a hole in
Speaker 1: the wall. They had to plug in a cable in
Speaker 1: one case. Yeah, they had to walk across rubble. They
Speaker 1: had to go up some stairs and then fall over.
Speaker 1: Uh didn't have to fall over, but a lot of
Speaker 1: them sure did. At any rate, we saw how difficult
Speaker 1: it is to design a robot that can do these things.
Speaker 1: And keep in mind, these were all tasks that the
Speaker 1: various teams knew about beforehand they were designing these. Yeah, yeah,
Speaker 1: I mean it was this was all stuff that they
Speaker 1: knew they were going to have, the robot was going
Speaker 1: to have to be able to do. Uh. So, building
Speaker 1: a robot that could end up anticipating all sorts of stuff,
Speaker 1: whether it's a single robot or a you know, a
Speaker 1: robot that can then design the next generation of robots.
Speaker 1: That's something that we really haven't perfected yet, but there
Speaker 1: are some really good reasons why we would want to. Clearly,
Speaker 1: being able to have a robot that could either adapt
Speaker 1: itself or adapt the next generation of robots would lead
Speaker 1: to much more efficient machines over time, and so this
Speaker 1: is something that we would really like to see in technology.
Speaker 1: It's not it's not just in robotics. We're also seeing
Speaker 1: it in computers. So there's they call it, you know,
Speaker 1: evolutionary computation or evolutionary computers or evolutionary robotics because that's
Speaker 1: essentially what we what we mean. We're talking about a
Speaker 1: machine that looks at the ability of another, like a
Speaker 1: next generation of machines to do a particular task, evaluates them,
Speaker 1: and then makes decisions on how to alter that generation
Speaker 1: to produce an even more effective generation after it. Uh
Speaker 1: the goal always being to come up with the most
Speaker 1: ideal design for whatever purpose you have in mind. And
Speaker 1: this isn't a super super new idea, no. Now, there
Speaker 1: have been people who have been working on this for
Speaker 1: a while. Back in two thousand eleven, researchers with NASA
Speaker 1: published a paper on evolutionary computation that was used to
Speaker 1: design new efficient antenna's way back in two thousand six.
Speaker 1: So the paper was published in two thousand and eleven,
Speaker 1: but the actual project was yeah in two thousand and six.
Speaker 1: And what they what they were pointing out was that
Speaker 1: designing an antenna is really challenging. It requires that the
Speaker 1: builder to have a very detailed knowledge of how antenna's work,
Speaker 1: which already limits your pool of various builders, and even
Speaker 1: then it's just painstaking to create an actual efficient, working antenna.
Speaker 1: So what they did was they created evolutionary algorithms for
Speaker 1: a computer to design an antenna for spacecraft auto matically
Speaker 1: that the computer system went through various designs of antenna
Speaker 1: and essentially simulated tests of them to see which one
Speaker 1: would be the most effective. They then took the designs
Speaker 1: that were predicted to be the highest performing antennas by
Speaker 1: this computer program, and they took it to the same
Speaker 1: manufacturer who was already building the antenna intended for the spacecraft.
Speaker 1: Then they tested all three of the antenna, the two
Speaker 1: that were the two best performer ones in the computer program,
Speaker 1: and the one that people had designed for the spacecraft,
Speaker 1: and found that the two that the computer had designed
Speaker 1: were more effective. Yeah, so it was one of those
Speaker 1: examples of this approach actually working better than what we
Speaker 1: humans could do. Now, maybe you don't know the answer
Speaker 1: to this, but if you do, I'd be interested. In
Speaker 1: this case, we're the what what we might call the
Speaker 1: mutations introduced into the models in in the algorithm that
Speaker 1: that tested out antenna's where the mutations direct did where
Speaker 1: they programmed in? Or were they random mutations? Like? Was
Speaker 1: it really more like evolution where a random things thing
Speaker 1: happens and then the system tests is this any better? Well,
Speaker 1: as we've talked about in previous episodes, random with machines
Speaker 1: is really hard to do. It was more like they
Speaker 1: were the computer was given an enormous number of variables
Speaker 1: and started to test them in various configurations without going
Speaker 1: through every possible one. Because the goal of evolutionary computer
Speaker 1: computation or evolutionary robotics is to make sure that you
Speaker 1: come to the most ideal form of whatever it is
Speaker 1: you're going for without having to test every possible variation. Because,
Speaker 1: of course, thus things get more complex, those variations increase
Speaker 1: in number until it would take you till the end
Speaker 1: of time to test all the different ones to come
Speaker 1: up version one one seven eight nine seven, seven seven
Speaker 1: four three is the best. You would take forever to
Speaker 1: go through all of those. So what these are designed
Speaker 1: to do is to test ones that are a best
Speaker 1: guests already of being effective, measuring those against other best guesses,
Speaker 1: then combining ones. Like if you were to find a
Speaker 1: generation that works particularly well and another generation that works
Speaker 1: particularly well, you might say, well, what happens if I
Speaker 1: combine the best design elements of both of these into
Speaker 1: a new design. Does that increase the effect of the
Speaker 1: efficiency and effectiveness or does it decrease it? Because it doesn't,
Speaker 1: you know, adding to awesome things together does not guarantee
Speaker 1: you to get a third, even more awesome thing. Oh yeah, yeah.
Speaker 1: I was wondering because I'd read about the use of
Speaker 1: evolutionary algorithms before in the design of new planetary rovers
Speaker 1: like unmanned planetary rovers, and testing different variations on the
Speaker 1: models in a in a computer simulation that would naturally
Speaker 1: select the highest performing ones. Yeah. Yeah, it's very similar
Speaker 1: to that. And uh, the thing about the difference, you know,
Speaker 1: we're going to talk about a robot that works along
Speaker 1: these principles. The biggest difference between a robot and a computer,
Speaker 1: obviously is that a robot is working with actual physical matter,
Speaker 1: not just simulations. And so there's some practical limitations that
Speaker 1: you encounter in that case. Right, you have to work
Speaker 1: with real physical matter that has weight, it has mass,
Speaker 1: it occupies space, there's a limit to how heavy it
Speaker 1: can be. Uh, you know, you can't just magically increase
Speaker 1: the size of whatever it is in a simulation and
Speaker 1: then just see how it works. You have to physically
Speaker 1: put this thing together. And so it's a really interesting approach.
Speaker 1: And we have seen a couple of examples of people
Speaker 1: working with true physical matter, but uh, it's mostly been
Speaker 1: in things like working with a program that builds stuff
Speaker 1: out of lego. So there is an actual example of this.
Speaker 1: There were some evolutionary computational experts that design systems that
Speaker 1: would allow a machine to build other objects out of legos,
Speaker 1: but you had to build limitations into the computer program
Speaker 1: so that the machine would follow the rules of physics.
Speaker 1: In other words, you couldn't have to lego pieces occupy
Speaker 1: the same physical space at the same time, right, Sure,
Speaker 1: And some of these computations aren't going to I mean,
Speaker 1: it's hard as a programmer to build all of those
Speaker 1: limitations into something that has to work in reality, because
Speaker 1: the computer isn't going to understand I mean basic stuff,
Speaker 1: and so so it might when you take it out
Speaker 1: of the lab, out of the out of the computer
Speaker 1: simulation stage, it might operate very differently than you were
Speaker 1: expecting it. Then the computer was expecting it to. An
Speaker 1: analogy I would make and has nothing to do with
Speaker 1: computers or robotics, but an analogy I would make is
Speaker 1: if you were running a role playing game. You're you're
Speaker 1: the game master of a role play game, and you
Speaker 1: think you have anticipated everything your players are going to do,
Speaker 1: and you have made up a masterpiece of a module,
Speaker 1: and your players are going to have an amazing time,
Speaker 1: and three minutes in the players decided to do something
Speaker 1: you could not have possibly anticipated. It makes perfect sense
Speaker 1: within the context of the game. And then you and you, yeah,
Speaker 1: you gotta throw away all the stuff you worked on
Speaker 1: and say, well, well, I guess like it's sort of
Speaker 1: like if you imagine a horror movie where the characters
Speaker 1: all walk up to the spooky house, take one look
Speaker 1: and say no, and then walk away like, well, there
Speaker 1: goes the horror movie. Same sort of thing. Well, we
Speaker 1: wanted to specifically talk about some researchers from the University
Speaker 1: of Cambridge who were working with a robotic system that
Speaker 1: used evolutionary robotics. So imagine, essentially, you've got a robot
Speaker 1: that builds other robots and tests them to see which
Speaker 1: designs work the best, and then either UH either has
Speaker 1: a design go forward or eliminates designs and starts tweaking
Speaker 1: things to try and find the best physical design of
Speaker 1: a robot to complete a specific task. Now that sounds
Speaker 1: super cool. I need to explain some stuff first so
Speaker 1: we can manage our expectations. Yeah, yeah, okay, so so
Speaker 1: so performance of these of these baby bots, as we
Speaker 1: might call them UH in this case was how fast
Speaker 1: and far it could move, like kind of scuttle across
Speaker 1: the surface exactly. So it's not like it was performing
Speaker 1: open heart surgery. No, No, they decided to hold that
Speaker 1: for the next experiment. Perhaps, Now these robots all we
Speaker 1: were supposed to do. We're create a locomotion power that
Speaker 1: could move it from one point to another point, and
Speaker 1: uh they would. The experiment measured how long it took
Speaker 1: these little devices to move across this particular expanse they
Speaker 1: changed up. That didn't make the cut. They didn't they
Speaker 1: didn't continue on. Uh that they were taken apart. They
Speaker 1: were taking up bodily by the mamabot. Actually, to be fair,
Speaker 1: I think all of them were taken apart, but yes,
Speaker 1: uh uh yeah they were. They'd be scuttled, they'd be
Speaker 1: scuttled and then they'd be scuttled and and and harvested
Speaker 1: for their organs essentially. So that was the that was
Speaker 1: the criteria that the robot used. Um and here's how
Speaker 1: it gets here. Let's break this down because that's that's
Speaker 1: like an overview of what happened, but it's really interesting
Speaker 1: when you get to the specifics. So what they wanted
Speaker 1: to do is they wanted to design a system that
Speaker 1: could design, test, and change robotic designs relatively rapidly. And
Speaker 1: it needed to be a a system that could work
Speaker 1: within the lab, but wasn't designed to be a practical
Speaker 1: system that could go through every single possible arrangement of
Speaker 1: the various pieces. For yeah, um, the pieces are essentially
Speaker 1: two different types of cubes that will get to in
Speaker 1: a second. So they wanted their approach to contain this
Speaker 1: is a quote from their paper paper, a limited number
Speaker 1: of evolutionary iterations, and that was, you know, the the
Speaker 1: idea of let's let evolution lead us to what should
Speaker 1: be the best version of these robotic designs, as opposed
Speaker 1: to just doing trial and error, measuring everything and then
Speaker 1: going with the top performer. Now, they did say in
Speaker 1: their paper that while this is really interesting and could
Speaker 1: in fact be a breakthrough in science and technology, you
Speaker 1: have to admit that there are limitations to this technology
Speaker 1: cannot necessarily be applied to mass production. Mass production relies
Speaker 1: heavily on automation. Automation is different from mass customization. So
Speaker 1: they said, you know, there there are certain practical applications
Speaker 1: for this kind of approach, but it's not gonna be
Speaker 1: like we're gonna design these robots and they are going
Speaker 1: to magically make all of our factories work at efficiency. Uh.
Speaker 1: It's uh. They said that there's a challenge to developing
Speaker 1: automas design of quote a large morphological diversity end quote
Speaker 1: morphological obviously meaning that the actual form of these things. Yeah.
Speaker 1: So the basic design of the experiment was to use
Speaker 1: a robotic arm that was the Mama butt, so it's
Speaker 1: a robotic arm, and had to gripper fingers, so just
Speaker 1: very simple robot um. One of the gripper fingers actually
Speaker 1: had a a little nozzle through which it could squirt glue,
Speaker 1: so they could glue pieces together. Uh, and it was
Speaker 1: connected to a computer that was running the evolutionary software
Speaker 1: to build the robots out of these little cube modules.
Speaker 1: And they had two types of modules. They had passive modules,
Speaker 1: which really just small wooden blocks that were painted black.
Speaker 1: The reason they were painted black was so that the
Speaker 1: cameras that the robot was using essentially as eyes, could
Speaker 1: easily pick them up. Let's see where the little passive
Speaker 1: ones were. So these couldn't do anything, right, they were
Speaker 1: just connectors. And then you had the active modules. These
Speaker 1: were a larger cube and one face of the cube
Speaker 1: was attached to a small motor that could rotate at
Speaker 1: a certain amplitude in frequency, so it could rotate the
Speaker 1: face of the cube, which is kind of the basis
Speaker 1: of the locomotion of the piece. Right, So, so imagine
Speaker 1: a Rubik's cube where only one side can rotate around,
Speaker 1: but it does so on a motor. That's essentially what
Speaker 1: we're talking about. And so different cubes would have faces
Speaker 1: that rotated at different speeds, and the all of the
Speaker 1: information was contained within the cube so that the robot
Speaker 1: would be aware of that. It wasn't like the robot
Speaker 1: was randomly picking blocks and some could turn at a
Speaker 1: certain speed and others would turn at half speed or
Speaker 1: twice speed or whatever. The robot was actually quote unquote
Speaker 1: aware of which cubes could do what and uh and
Speaker 1: by by fitting them together in different combinations, uh yeah,
Speaker 1: you could you could get the box to kind of
Speaker 1: kind of wiggle, to kind of hop and wiggle across
Speaker 1: the table. It reminds me um of the motion of
Speaker 1: some of the the less mobile toys and toy story
Speaker 1: um kind of just sort of like scuttling along it
Speaker 1: ones that didn't have legs. Yeah. Yeah, it's flipping adorable,
Speaker 1: you guys. I am going to see if we can
Speaker 1: find video that's share able to share with you because
Speaker 1: it is so cute. It makes me think of a
Speaker 1: lot of like, you know, wind up toys that that
Speaker 1: move because they're doing this repetitive motion. Uh, and that's
Speaker 1: enough to propel them across a surface. Uh, not with anything.
Speaker 1: They can't necessarily steer or anything, but they can move
Speaker 1: the same sort of thing. Like the idea was that,
Speaker 1: all right, well, if we pair this passive module with
Speaker 1: these two active modules in this configuration, will that create
Speaker 1: movement that's faster and more effective than this other design.
Speaker 1: So the computer they used was a regular desktop PC,
Speaker 1: nothing particularly special about it, but it was running a
Speaker 1: controller program that was using the matt Lab language m
Speaker 1: A T L A B and that's used for interactive
Speaker 1: environments of developing algorithms, among lots of other stuff. The
Speaker 1: robotic arm could easily grip and rotate any module it shows,
Speaker 1: and could stick two of them together using what they
Speaker 1: called a hot melt adhesive or h m A. Yeah,
Speaker 1: essentially like hot glue, like glue gun glue is more
Speaker 1: or less what that was, And it could build robots
Speaker 1: by combining multiple modules together. Each module had what they
Speaker 1: called a gene, which essentially described the type of module
Speaker 1: it was and the motor control of that module, and
Speaker 1: also included the construction parameters. Of the module construction parameters. Essentially,
Speaker 1: it was things like basic rules for the robot to
Speaker 1: understand so that it could effectively build a robot. In
Speaker 1: other words, if you want to build a robot, you're
Speaker 1: probably gonna need to put a larger piece down first
Speaker 1: before you put a smaller piece on top. It was
Speaker 1: building them vertically, so it needed to know, Hey, if
Speaker 1: you try and build a robot this other way, this
Speaker 1: thing's gonna fall over. So you need to be able
Speaker 1: to build them in you know, these are the general
Speaker 1: rules you need to follow, essentially the rules of physics,
Speaker 1: so that your robot will be built the way you
Speaker 1: intend to build it. Uh. So a robot was made
Speaker 1: up of these modules, and it was said to have
Speaker 1: a genome. It was the collection of these genes and
Speaker 1: uh these genes would either work or not work together.
Speaker 1: And robots could have between one to five genes modules. Yeah,
Speaker 1: now they if it's having five. The max was three
Speaker 1: active too passive. You could not have more than three
Speaker 1: active simply because it would make the robot too heavy
Speaker 1: for the gripp or to grip. Right. Yeah, that's that's
Speaker 1: one of those limiting factors in reality there. Yeah, so
Speaker 1: if it were more than fifty grams of mass. The
Speaker 1: robotic arm, the gripper just couldn't maintain a grip. It
Speaker 1: would drop it and it would break. And so practically
Speaker 1: you could have a maximum of three active and two
Speaker 1: passive pieces connected altogether. They didn't necessarily have to have
Speaker 1: that many. Some of them were only three or two
Speaker 1: uh two blocks large at large two blocks in total,
Speaker 1: but at any rate, the construction parameters were these very
Speaker 1: simple directions and it allowed a lot of flexibility. So
Speaker 1: the robot arm essentially could decide which modules to use
Speaker 1: and how to connect the two together or three or
Speaker 1: four however many together, and then test it so that
Speaker 1: the what would happen after it builds one of these,
Speaker 1: after it squirts the glue and everything. Um, you could
Speaker 1: actually send it a genome essentially a recipe saying here
Speaker 1: are the modules I want you to use and the
Speaker 1: configuration I want you to put them in, and this
Speaker 1: is going to represent the first generation. You could do that,
Speaker 1: or you could make it uh more of a random approach. Well.
Speaker 1: Once built, the mother robot would lift the finish modular robot,
Speaker 1: move it to the testing area, which is just a
Speaker 1: flat surface for the robot to crawl across, also known
Speaker 1: as the arena. In some cases it was a hard surface.
Speaker 1: In other cases they used a carpeted service, and at
Speaker 1: least in one they used a foam surface. Yeah. They
Speaker 1: they slowly moved towards the foam surface as they realized
Speaker 1: throughout the experiment that the robots were having undo trouble.
Speaker 1: I was sad to learn that the foam they were
Speaker 1: talking about was like, you know, kind of like mattress
Speaker 1: foam as opposed to foam party. It's a little bit
Speaker 1: robots just getting down and raving. I come from the
Speaker 1: specific time, y'all. Anyway, Wow, Joe to a foam party
Speaker 1: has been no pope, you like pink II. Here we go,
Speaker 1: all right. So the modular robots would then be activated wirelessly.
Speaker 1: They had inside of them essentially a receiver for Bluetooth
Speaker 1: or WiFi, and that would activate them to go into uh,
Speaker 1: you know, their basic motorized action. Yeah, and then there
Speaker 1: would be a couple of cameras, including an overhead camera
Speaker 1: that would measure their progress across the surface, and then
Speaker 1: they would essentially, uh take the the distance they traveled
Speaker 1: and within a certain amount of time. Originally they went
Speaker 1: with eight seconds, so after eight seconds, we see how
Speaker 1: far they've gone. Uh. They had to switch that to
Speaker 1: four seconds because later tests the modules were moving quote
Speaker 1: unquote so quickly. Yea, it was working, so they needed
Speaker 1: to have They needed to shorten the amount of time
Speaker 1: because otherwise the robots would just travel out of the
Speaker 1: view of the camera. So they had to shorten the
Speaker 1: amount of time so that the robot arm could make
Speaker 1: determinations of which ones were the most effective. Uh So,
Speaker 1: after testing, the modular robots were disassembled by hand, so
Speaker 1: the so the mother did not have to kill her babies. Uh,
Speaker 1: the unfeeling scientists got to do that. Uh. So then
Speaker 1: they had to also remove all the h a the
Speaker 1: hot melted agent material, and then they were replaced onto
Speaker 1: the work area. The work area as you would imagine. Uh,
Speaker 1: each module had its specific place in the work area,
Speaker 1: so that way the robot quote unquote knew where to
Speaker 1: go to pick specific modules. Because these machines are not
Speaker 1: that smart, right, you have to put the things in
Speaker 1: the right orientation and the right um location for the
Speaker 1: robot to be able to grab them. We've talked about
Speaker 1: this with other robots too that are combining objects if
Speaker 1: you have the objects in a specific order. I think
Speaker 1: we talked about this with the cooking robots. Specifically you
Speaker 1: have to have them in a specific place, to specific order,
Speaker 1: or else everything is just going to come out mixed
Speaker 1: up and awful. It couldn't recognize what was what. Yeah, yeah,
Speaker 1: it's just following like it understands quote unquote understands the
Speaker 1: uh qualities of each item, but only if the right
Speaker 1: item is in the right place. So if you put
Speaker 1: all the active modules where the past of one should
Speaker 1: be and vice versa, it would it's not gonna be
Speaker 1: able to tell the difference exactly. So at that point,
Speaker 1: the evolutionary system would begin to tweak the genomes. It
Speaker 1: could swap out genes in a genome, or it could
Speaker 1: combine different genomes. So essentially it would be like breeding
Speaker 1: two robots, saying, if if robot from this generation and
Speaker 1: another robot from that generation were to combine, here are
Speaker 1: the qualities that would emerge from that, keeping in mind
Speaker 1: essentially the robots choosing which qualities would emerge. Because you're
Speaker 1: still limited. You can't have more than five genes, so
Speaker 1: it's not like it would just be additive. It would
Speaker 1: have to be selective in which genes from which two
Speaker 1: got selected to be combined into a new one. And
Speaker 1: then the theory was that, or at least the hope
Speaker 1: was that it could design another generation of better performing robots,
Speaker 1: and some of them might not perform well. It may
Speaker 1: turn out that the genes from robot one and the
Speaker 1: genes from robots seven are not as compatible and they
Speaker 1: actually perform worse than either one or seven did individually
Speaker 1: in the generation before. And that happened a couple of times, Yes,
Speaker 1: it did. So they held a total of five experiments,
Speaker 1: and between these experiments they changed up the surface. They
Speaker 1: made some tweaks to various rules. They actually numbered them
Speaker 1: one A through one D and then two. Yeah, they
Speaker 1: did five five different runs, but four of them fit
Speaker 1: under the category of experiment one. So each experiment started
Speaker 1: with ten agents other in other words, ten basic robot
Speaker 1: designs UH that represented ten different genomes, and then each
Speaker 1: experiment ran through ten generations, so you've got a hundred
Speaker 1: different designs total per experiment and UH. The first four
Speaker 1: experiments started with some randomly generated designs consisting of one
Speaker 1: to three motorized elements, and they put all the construction
Speaker 1: constraints they had designed in play for those initial experiments.
Speaker 1: The testing environment was changed a couple of times. That's
Speaker 1: when they started with a hard ground, then they moved
Speaker 1: to the carpeted service and then the foam h and
Speaker 1: humans started helping out the mom about eventually to hold
Speaker 1: the component's study during the build phase, uh so that
Speaker 1: she would not drop them basically. Uh And they also
Speaker 1: began manually inspecting the baby bots to be sure that
Speaker 1: none of the pieces were going to collide with each
Speaker 1: other during the test, because that would damage the pieces
Speaker 1: and make things less fun for everyone involved. Yeah, especially
Speaker 1: since that would affect any future Yeah, because if you
Speaker 1: if the cube gets damaged, then you it's hard to
Speaker 1: determine if the robot would have performed better had it
Speaker 1: worked with a brand new cube. And uh So they
Speaker 1: were being very careful at that point. A little human
Speaker 1: intervention wasn't necessary because it was you know, this is
Speaker 1: like a proof of concept type of approach anyway, So
Speaker 1: the fifth experiment, the initial generation of agents was not
Speaker 1: generated randomly. Instead, they picked some of the best performing
Speaker 1: designs that came out of the previous experiments, So generation
Speaker 1: one was actually made up of robots that had already
Speaker 1: been built in the first four experiments, and just said,
Speaker 1: all right, let's made me think of you FC. Let's
Speaker 1: take the champions of all these different fighting disciplines and
Speaker 1: put them together and see what happens. Except instead of fighting,
Speaker 1: they're supposed to make babies, so totally different UFC. I
Speaker 1: guess in that sense, I would suppose. So yeah. I mean,
Speaker 1: I don't know a whole lot about the UFC, but
Speaker 1: I know a great article written by a certain Jonathan
Speaker 1: Strickland you should read. Uh. Interestingly, the test could that
Speaker 1: be found on how stuff works dot com. Could. In fact,
Speaker 1: if you go to how stuff works dot com and
Speaker 1: look how the UFC works, you will find an article
Speaker 1: I wrote ages ago. It was awesome. Um. So interestingly,
Speaker 1: the test that produced the most improvement from first generation
Speaker 1: to the last generation was the fourth test. They actually
Speaker 1: plotted out each generation's performance on a kind of a
Speaker 1: line graph chart, and UH, they average the ten robots fitness,
Speaker 1: that's what they called the performance of moving across the surface. UH,
Speaker 1: and they the fourth test saw steady improvement with one exception, uh,
Speaker 1: where at general ration five there was a slight dip
Speaker 1: in performance and generation ten saw very slight decline in
Speaker 1: performance from generation nine, so generation nine did the best
Speaker 1: out of the fourth test. The fifth test saw the
Speaker 1: longest declining trends. So this was the This was the
Speaker 1: one where they took the champions from the previous tests
Speaker 1: and started with those. So this is the DEVO group. Yeah, yes,
Speaker 1: and exactly. They had a devolution uh in generation over
Speaker 1: generation from three to seven, so their their performance actually declined,
Speaker 1: not steadily, but in each generation there was a decrease
Speaker 1: in fitness. But then it all turned around. Yeah, they
Speaker 1: started to see improvements again and all the way through
Speaker 1: to generation ten. So it was one of those things
Speaker 1: where by the end, I think in every single case,
Speaker 1: the generation ten robots outperformed the initial generation of robots
Speaker 1: in all the tests. There were some cases where generation
Speaker 1: ten didn't outperform, maybe generation in seven or eight, but
Speaker 1: in all of them they outperform generation one. Uh. It
Speaker 1: was really interesting. The fourth test, top performing agents in
Speaker 1: the first generation average two point eight centimeters per second,
Speaker 1: and by the tenth generation it had increased to six
Speaker 1: point seven centimeters per second. Yeah, so more than twice
Speaker 1: and twice yeah, yeah, so that's pretty exciting. That they
Speaker 1: were able to take this approach and increase the speed
Speaker 1: of these mobile agents by a factor more than a
Speaker 1: factor of two, which yeah, yeah, the researchers did. Uh.
Speaker 1: Did note that the disadvantage and having the mom about
Speaker 1: manually test each generation is that it takes time, and
Speaker 1: so there's a little bit of a payoff balance between
Speaker 1: running simulations first versus going straight into that real world testing.
Speaker 1: And they were talking about how they hoped to streamline
Speaker 1: the process in the future by using simulations to select
Speaker 1: the most likely successful models and then begin testing with
Speaker 1: those instead of kind of starting from scratch. Yeah. Uh.
Speaker 1: And in fact, they had talked in their paper or
Speaker 1: about how, yeah, this is this is a balance, right,
Speaker 1: because when you go with the pure simulation mode, it
Speaker 1: may turn out that when you when you transfer the
Speaker 1: simulation to reality, things don't behave exactly as you had anticipated.
Speaker 1: Perhaps the simulation was unable to take all the different
Speaker 1: factors into account, or it just maybe that, you know,
Speaker 1: it's just in the real world stuff behaves a little
Speaker 1: differently than the idealized virtual world. But a combination of
Speaker 1: the two is probably the best approach, because, like Laurence says,
Speaker 1: if you do everything physically. Then you need to have
Speaker 1: the luxury of time on your side, just because it
Speaker 1: will take this time to physically build these things, and
Speaker 1: plenty of blocks and glue, yeah, and lots of humans
Speaker 1: to d glue the blocks, and and these robots are
Speaker 1: just blocks, right, I mean this this is the about
Speaker 1: as unsophisticated a robot as you can get. So if
Speaker 1: we were talking about a robot designing a future robot
Speaker 1: capable of doing something really sophisticated, it would obviously take
Speaker 1: even more time. Yeah, I don't know. I've met some
Speaker 1: pretty ansophisticated robots. Well, I mean they really like cheese.
Speaker 1: It's they don't tip. Well, um, they prefer they prefer
Speaker 1: light beer to I p a s. Yeah. Yeah, that's
Speaker 1: that's why I was giggling a second second ago. That's sad,
Speaker 1: Please please continue. Well, no, no, The cool thing to
Speaker 1: think about is that imagine a future where we have
Speaker 1: machines capable of designing a new generation of machines that
Speaker 1: are better adept at doing whatever they need to do
Speaker 1: than the previous ones, and then can even learn from
Speaker 1: that and build even better ones in the future, perhaps
Speaker 1: even building a better computer to design the next generations
Speaker 1: and eventually you arrive at or deep thought, because that's
Speaker 1: exactly what deep thought was in Hitchecker's Guide to the Galaxy.
Speaker 1: Was that well, deep thought said it could give the
Speaker 1: answer to the question of life, the universe and everything,
Speaker 1: but could not give the question and said, in order
Speaker 1: to get to the question, it could design a computer
Speaker 1: that would be even more advanced and be able to
Speaker 1: answer that question after a really long time. But that's all.
Speaker 1: That's the best it could do, is that it could
Speaker 1: design a better machine than it to be able to
Speaker 1: answer or to come up with what the question was.
Speaker 1: Of course, we all remember the answers forty two question
Speaker 1: turned out to be was six times eight. Just shows
Speaker 1: you that something's wrong with the universe. But now this
Speaker 1: is really kind of I'll get you a computer. Um. Yeah,
Speaker 1: obviously it's a joke in hitchecker Sketch the Galaxy. But
Speaker 1: this is this is the neat ideas, This this approach
Speaker 1: where we can set certain types of machinery on a
Speaker 1: pathway to reach this possibly increasingly efficient means of evolution
Speaker 1: to create better tools. Um. This is also obviously one
Speaker 1: of the principles that underlies certain versions of the singularity, right,
Speaker 1: like this idea that we get to a point where
Speaker 1: evolution is so constant that there is no meaningful way
Speaker 1: to describe the present anymore because it's everything is changing
Speaker 1: all the time. And uh, and this is sort of
Speaker 1: the kind of stuff that would be necessary for that
Speaker 1: particular version of the singularity to come to pass, will it. Well,
Speaker 1: let's just say that based upon what's going on right now,
Speaker 1: it's gonna take some time. We got some real cute
Speaker 1: wiggly blocks in in the meanwhile, though, So yeah, I
Speaker 1: am still very skeptical that we will see anything close
Speaker 1: to the singularity on a time scale that kurtz Wild
Speaker 1: has predicted. Do you mean twenty to forty years? Yeah,
Speaker 1: it's so this was kind of fun. I mean, if you,
Speaker 1: if you get a chance, you can read the paper.
Speaker 1: The paper is actually quite easy to read. It's um
Speaker 1: it's not an inaccessible paper, and it is available. It
Speaker 1: is very accessible. It's it's available for free on the internet. Yep.
Speaker 1: So you can read all about the experiments. They go
Speaker 1: into detail. They really I didn't go into a lot
Speaker 1: of detail about the differences between the five different runs
Speaker 1: they did, just because it would have gotten super technical
Speaker 1: still understandable, but just bogged down in a lot of
Speaker 1: technical details. But it's all there in their their paper.
Speaker 1: We'll try to remember to link it on various forms
Speaker 1: of social media if you want to google it for yourself.
Speaker 1: The full name of the paper is Morphological Evolution of
Speaker 1: Physical Robots through model free phenotype development blin Yes. And
Speaker 1: if you want to get in touch with us and
Speaker 1: and ask us to cover a specific topic. Maybe there's
Speaker 1: something about the future you have always been curious about
Speaker 1: and would like to hear our take on it, let
Speaker 1: us know. So does an email the addresses f W
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