This is the Convergent Science Network podcast. Leading researchers in the domain
of neuroscience, brain theory and technology are interviewed by Paul Verschoor and Tony Prescott.
So this is Paul Verschoor with Maria Kruzma, one of the speakers in our BCBT summer school.
And um you focus very
much in your talk about building an
artificial fish right that was very much the topic the topic
of your of your talk and of a lot of your research is fish yes so um and you
started out with a very fundamental question like what when we talk about biomimetics
what do we really copy right that was your fundamental question you started out with So tell me,
what are we essentially copying in biomimetics and are we really copying?
I think we're copying, but we really don't always think of whether we are copying
the wrong or right things.
There is an approach in biomimetics that you can call the naive biomimetics approach.
You just copy everything. I think like first propellers had feathers because
people thought the feathers are very important things.
You know, on every flying object
you have feathers, so you want to do propellers, you have feathers too.
But in the end they understood that the helicopters are hovering very well without feathers.
From the point of view of aerodynamics, this doesn't really matter in this case.
And I think if we look back to our own research now, 10 years ago, after 10 years,
the projects that we are doing right now could look equally funny because of
these wrong ways and wrong approaches we are taking and copying irrelevant things.
Like first cars in history they
had a place in front of the car for
a horse and it was there for a long time because cars
involved from the horse carriage before people
understood that these places for a horse but they are completely unnecessary
and redesign the cars and i suppose we do the same things and if we ask a question
like what should we copy or what is relevant and then we maybe can avoid taking
this wrong direction once in a while. Mm-hmm.
So, um...
So, but if you now look at the fish case as a specific target of your research, right?
So, what makes fish so interesting?
Well, naive biomimetic approach says you fish are excellent swimmers.
Nothing swims as well as fish.
So, from this point of view, it makes sense to copy fish, see how it works,
and see how does it compare to a propelled vehicle.
But where you come to
difficulties i'm a little critical now about biomimetic approaches
actually in my own research too i think that propellers
are wonderful inventions you see nature has been working on methods to travel
underwater for millions and billions of years it never she never came out up
with a propeller but it's a wonderful simple device and very powerful So,
but definitely if you want, if you look at the technology of propellers,
what happens there for the last, I don't know, tens of years,
all the development is already very incremental.
And if you want to make a breakthrough to come up with something completely
different, then you could look at undulating bodies like fish have to try to
design something different. Mm-hmm.
But now, if you talk about propellers, you might find them fantastic,
but in some sense, fish seem to be more efficient swimmers than...
They are more efficient. They are more energy efficient.
That's one way why you want to look at fish.
And another thing is they're quiet. Right.
They don't leave the wake behind them. It means actually, if you look at the
wake behind the propeller, this is all energy got wasted in water.
So in that sense, I could argue that the very positive statement you made earlier
about propellers is maybe a little bit biased, but that's the current state
of the art. It's just the best we can do.
It's the best we can do, but I think we're doing very well.
We're researching on the fish robots, but they're not very efficient at the
moment. And so, really, do you understand what is a mechanism?
What makes her so efficient?
We still don't understand how we should mimic this technology.
One important aspect is that fish have distributed action.
They have hundreds and hundreds of muscle fibers all over the body.
And if you look at the current technology we have in hand, What is a reliable
technology is really still the technology of rotating motors.
Okay, we have other fancy things that I also used to work with,
like electric, electroactive polymers and artificial muscles and nitinol or
all these contracting actuators.
But they have their own issues. This is not a mature technology.
And now if you want to use a mature technology that we have,
Now it's just DC motors and make a distributed actuation system.
You end up with something very, very big and clumsy in the end.
You end up with big and clumsy. It becomes energy inefficient.
It becomes hard to control and really, really big. You don't make little agile,
simple devices out of that.
So we agree that in terms of our technology,
best solution to the problem fish are still doing better okay
so swimming fish do better fish do
better so but then what i would like to know um
what makes them better okay what are the tricks what are these principles that
as a biomimetic engineer you might want to extract from fish to build better
fish like machines right so what makes fish better swimmers every Every machine
works by transmitting energy from their own body to the surrounding water.
By transmitting the momentum of energy, they push themselves forward and move the water.
The way propellers do it is by rotating the plates.
The way fish do it is by undulating their bodies and creating a traveling wave.
And then it passes the momentum of the water. and how do they do it so efficiently?
We didn't know exactly. So there are, again, many aspects of it.
One is the body, embodiment itself.
Now when it comes to physics of compliant bodies and water interaction is a
terribly, terribly difficult field to go into to understand how compliant bodies
and water interact with each other.
There is no mathematician who could sit down and write up the equations and
say that this is optimal solution to the problem.
Nobody can do that. So what we have is more like an experimental approach.
We try to optimize surface area or the fins or we try to optimize the length
of the body or whatever comes to your mind.
And see that if we get more efficiency out of the fish. But now,
one thing that's interesting about fish is that they actually have developed
a pretty advanced sensing system.
To be able to really measure in detail the properties of the medium around them,
right? So these are these lateral line sensors.
Lateral line sensor is a very specific sensor.
In a way, it's very general because it's common for all fish,
all fish have a lateral line.
But on the other hand, it's only common for those creatures who live underwater. water.
Nobody walking on the ground or flying in the air doesn't have a lateral line.
And one problem with mimicking lateral line sensing is that it is not clear
understanding among biologists either how this marvelous sensing organ actually works,
in which cases is it active and in which cases it's passive, in which behaviors,
and how this information is processed in the brain of the fish.
So if you want to do a biomimetic approach of a lateral line,
it's like shooting in a dark room.
But here comes another aspect of biomimetics, which I would call inverse biomimetics.
We think of biomimetics that we go from science to technology.
Scientists find out something, they discover things. things,
investigate things that exist that already they discover, they make a discovery,
and then they pass over to us engineers.
And the engineers say, uh-huh, here is a great phenomenon. How do I get some
use out of it? And then they develop the technologies.
So what biomimetics actually does is it turns discoveries into inventions.
But if you did inverse biomimetics, you could do the other way around.
You turn inventions into discoveries.
And this is a little bit what we're doing with our fish lateral line experiments in philosophy project.
It's very hard for a biologist to investigate the lateral line.
It's a lot of work and I have a very deep respect to biologists who are working
with it because it takes lots of, lots of, lots of patience.
For example, if you want to understand what is the role of a certain modularity
of lateral line sensing, as a sense pleasure or the sense flow in certain behaviors,
it's very hard or even impossible to knock out some parts of the systems as
a pharmaceutical or surgical or do these experiments.
And sometimes they left only behavioral tools, which is very,
very hard work for people.
And here you could take a robot or a technology as a method for biologists.
Say that I have now my artificial robot here, artificial fish,
and it has an artificial lateral line.
What if I knock out the left part of the artificial lateral line and see what happens? Mm-hmm.
Does it give you anything as a biologist to think about?
And if you do the inverse biomimetics in the right way, you could have a very
big impact to what biologists can find out.
Right. And it also means that you might have what you want to shoot for then
is a continuous interaction between,
let's say, hypotheses generated from the scientific domain validated in the
engineering with predictions going back into the science.
Exactly. To close the loop. Exactly. That's the role the biomedics can play
here, which we clearly see also in your project.
Yes. But now, to look at the lateral line, essentially, it's a strip of sort
of hairs that runs along or hair-like elements that runs along the side of the fish's body.
And it also has similar sensors on its head or on other parts of the body.
And it's used to sense exactly what? What's the lateral line exactly sensing?
Lateral line has two modalities. Actually, there are two ways of measuring flows.
One is that you can measure flow velocity, and another thing,
you can measure pressure.
In very few conditions, when you have a laminar flow, they are very easily related
to each other by Bernoulli's law when they are inversely proportional.
But otherwise, if you are in a difficult hydrodynamic environment,
Then how does this different modality, do these different modalities add up
to each other is also a very complicated interplay.
But fish is equipped with both of the systems. They have canal lateral lines
and can measure the pressure difference.
And it has a surface lateral line, what biologists call neuromasts, that measure the flow.
These are just simply like little cantilevers, little beams that bend in a flow,
and depending on the angle of bending, you can figure out how strong is the flow.
So what happens next is very complicated and nobody exactly knows what happens
because some of the processing, signal processing of the signals is done in
a, so to speak, hardware already of the fish.
Because hair cells have a different stiffness and different height and they
probably also work as some sort of filters, low-pass, high-pass filters.
And some of the, if you think of how something like a fish, who is a rather
unsophisticated, uneducated creature, could do such a sophisticated signal processing.
So one answer to that is that it probably does it already in embodiment.
The sensors do some of the filtering and cleaning up the crap from all the noise
from what they're getting in. And then it goes to prey.
And then the fish makes a decision based on what it feels. Yeah,
but the decision this brain is going to make is in this domain of what's called rheotaxis, right?
It's like the response to a current, right?
That's one thing that fish do is that they respond to a stimulus which is a current or flow.
The simplest form of rheotaxis is when fish orient their nose against the flow.
There are several reasons of doing that. One reason is that if you're a migrating
fish, you have to go upstream to find your spawning place.
Then you have to know the direction of upstream.
And this is a classical example of a real Texas behavior.
But you could also just try to be in sweet spots on a flow where you get a lot
of orders and a lot of food flowing by.
And then you just sit there and open your mouth and hoping for the fresh plankton of the day.
Exactly. So what is interesting about that, right, is that these fish with the
lateral line sensor can also live in a world where you have these dynamic objects,
which are essentially the properties of the flow.
Right, where you can say, okay, here I have a flow profile that I like,
but here it's more like a flow obstacle that I should avoid.
Avoid so it's as if you walk through a mist right where you have high density
and low density spots in a field filled with mist so which you would navigate
and you would go for the open spots because that's where you would have more visibility,
it looks as if they have a very,
Pretty complex representation of the dynamics of the medium that they're in.
It's an interesting question. I don't know how much spatial memory the fish have.
But if you think of salmon, for example, that does orderly attacks and combining
it with information from the flow.
And it finds its home river where it was born once and then goes upstream and
migrates. So maybe it definitely fuses this also with other kind of information.
They use information from the magnetic field and in the air and all sort of different.
So I think all fish like other animals are patient optimizers.
They're just trying to find them a likelihood, maximal likelihood from all the
information they get in.
But if you're, I don't know what's going on in fish's brain,
but if you take it from an information theoretic point of view and you look
at this flow maps that fish possibly could have,
then you can think of that fish could
build up a really complicated map of discriminate hydrodynamic events.
Events tells that there is a certain hydrodynamic event
in one spot and certain in the other and then
you can think that it maybe adds up as kind of a
robot environmental map as a
topological map or a grid-based map or something you don't know what happens
in its brain but this is something very new that roboticists don't do they don't
build maps based on flow information but fish probably have this somehow incorporated
into their environmental mapping. Right, yeah.
That's very exciting. But then...
In some sense, we cannot ignore this whole issue that what starts to become
important now is also your relative size in terms of the medium you're in, right?
Like, for instance, if you're a whale, even though they're not fish,
but let's say you have that scale, or let's say you're a whale shark,
so you're still a fish, but you're very big, then in some sense,
a lot of this turbulence stuff, you just don't care about, right?
Because you will overcome a lot of this.
I think sharks care a lot. It has been shown that shark skin is actually a sensing organ.
And what sharks probably do is what is called eddy odoratexis,
that they get in the information of the smell and combine it with what they
feel with the rattler line about the local flow.
And then they make decisions where to go based on this information.
If you think of sharks' ability to localize plot,
even few drops of plot far, far away, they must have this ability very fastly
to… So that means it's not then relevant for navigation itself,
but what it's used for is for source localization.
We didn't know whether it's also relevant for navigation itself or just source localization.
But, well, it's kind of, I don't know whether it's a relevant question.
Why do you have to navigate anyway in order to get somewhere, right?
If you're a really small fish, right, then in some sense you want to be really
clear or you want to understand where the turbulence is because you have to overcome,
you have to generate a sort of propulsive force to overcome the counterforce
you receive from your environment.
So there, the scaling of yourself to the medium, also expressed in this Reynolds
number, will become pretty important.
Well, if you are a whale shark, that's something you don't have to worry about too much.
Yeah, but this is what is called the negatory of taxis.
So instead of going into the flow, you try to keep away from the stream because
you're such a small fish, you're afraid of being blown away with a stream.
So it's equally important whether you have a big Reynolds number or a small Reynolds number.
Probably your equipment is different and your mechanisms are a little different.
Okay, so that's a bit the question I want to get to.
So we are scaling this relationship or we quantify this relationship with the
so-called Reynolds number, right?
It tells you something about this relation between, let's say,
on the one of the forces that the medium exposes you to and those that you can
generate yourself. Right.
And so then the question is, if I'm operating at, let's say,
low Reynolds numbers, are my principles of operation qualitatively different
as when I'm operating on high Reynolds numbers?
And where is the transition exactly? So can you say something about that?
Is that understood in some way?
No, I don't think if I ask the same thing from biologists, there is no kind
of firm answer that, yeah, we established that already.
Both small and big fish have a lateral line, so it must mean it's somehow important.
If you're living in this medium, you have to sense this medium.
But nobody seems to be investigating what is the correlation between the environment
you're living in and the topology of your lateral line, for example.
Why some of the fish have more lateral line sensor in the head and none of them in the body.
And some fish have lots of sensors in the rear part of their body.
And does it depend on environment or the way they're feeding or lifestyle and
other aspects of the lifestyle?
Nobody has a beautiful, simple law to answer for that.
That's interesting. So, because what is interesting there is that it goes a
bit back to this earlier point that although first you think,
okay, fish swim, they swim in a turbulent medium,
they have to sense this medium to optimize swimming, so they need lateral line.
But then actually when you push it further, you see the lateral line might actually
be used for something completely different.
And that swimming comes almost for free for fish because, as you also mentioned in your talk,
that fish show a lower metabolic rate in turbulent environments as opposed to
non-turbulent environments, which seems a very surprising result,
isn't it? In a periodic turbulent environment. Okay, so explain.
Periodic turbulence is on moderate Reynolds numbers when you have like beautiful
regular vortices normally, say, appear behind some object.
Object and there's some evidence biology biologists suggesting that fish like
to be in environments like that and also if you catch a fish from environment
like that it's and investigates its metabolic rate it appears that fish are
less tired and there is a very interesting.
Experiment done in george lauder's lab in harvard with jimmy leo and his colleagues
with a dead fish swimming upstream.
So definitely their lateral line is not concerned at all because you're dead.
Exactly. You can't sense anything.
Even more, you can't even actuate yourself.
Okay. So the only thing you're left with is your body embodiment which somehow
has to create propulsion.
And the mechanism you can speculate behind it is the same mechanism that pushes the sail forward.
Just by taking advantage of the pressure differences in the vorticity.
And what is the role of red or white in finding so sweet environments they like to be in is not known.
If you ask biologists, they can't say you have a karmic gating.
This is a phenomenon when you stay in regular turbulence and you flap your tail
periodically with the same frequency of vortex shedding, whether this is a passive
or an active behavior. Mm-hmm.
So we don't know. But now this result of the dead fish swimming upstream was
published a while back, no? It's not very recent.
No, it's not very recent. I think, I'm not sure, but I think it's 2004 or something.
So are you aware of anyone being able to replicate that?
I have no idea anybody has replicated that so far.
I haven't heard of anybody repeating this experiment. And I've had plans to
repeat them with robotic fish in my laboratory, but I haven't got so far yet.
But what we are speculating by looking at the results of our own research could
be that what really matters is very delicate interplay between the turbulence
and the properties of the body.
So what really matters probably is that if you happen to be of the right size
with the right floppiness, perhaps, and in the right spot, you know,
that creates this phenomenon.
Just you don't see very often dead fish swimming and this could be the reason
why. Right. Yes, exactly.
What is very powerful about this is that you really see that the morphology
itself is actually solving a heart
problem that we intuitively might have thought of as requiring a brain.
So, that means that the passive dynamics of the body is structured in a way.
That it can actually propel itself, given that the medium has certain properties.
The same phenomenon has been demonstrated on the ground with passive walkers.
You put a robot that doesn't have any sensing or has any actuation,
you put it on a ramp, and it just keeps walking down.
But it's the same thing there, that you have to have the
right slope of the ramp in order to make it
occur how often uh in in in
your life are you walking down the slope and only
down the slope so probably because you're not doing that and normally their
ground is rough so this is why you're also developed actuation sensing and i
think the same holds for fish but if you think of the ramp what is different
in when you're fluid is that you're in a microgravity environment or zero gravity
environment compared to what you what
you're feeling in a ground. What you're feeling on a ground is a gravity.
And in water, you almost don't sense gravity at all. And it kind of suggests
your embodiment has to also be very different.
And now you have a solid ground behind you. You're walking.
You're a passive walker. But I think when you're interacting with the flow, the flow is compliant.
So I think it's more like compares to walking on a trampoline rather than along the street.
And this is why I think also it's harder to demonstrate underwater.
Exactly but now so okay now what we have what you're also what we see now in
these biomimetic principles of control,
the body gives you already a lot of functionality
some people would call this a morphological computation although I find this
is a bit of difficult there's also a concept from embodied intelligence which
actually is a little wider but says the same that you You outsource some of
the control burden to your embodiment without being conscious about this. Right. So, but now…,
I'm a fish, I'm alive, I have a brain, so I still want to control my swimming, right?
I cannot just flop around in some vortex and see where I'm ending up.
I want to no turn left, right, go up or down, catch prey, what have you.
So I'm going to control that. So let's first look at propulsion.
So what do we know really about, let's say, how I control my swim speed?
That's very interesting research, which is done by John Long in Vassar College.
Some years ago, and he has very elegant experiments demonstrating what are the
first-order control parameters of the fish.
So what fish seems to control is tailbeat frequency and stiffness and amplitude.
And if you look further into that, swimming speed is a second-order control
parameter. You control your swimming speed by controlling your stiffness,
by controlling your tail peak frequency, for example.
And what biologists have also demonstrated is there is a very beautiful and
simple control law, which says that swimming speed and tail peak frequency are
linearly related to each other.
For example, different from a tail peak amplitude, which is a completely independent variable.
But this seems strange. I mean, you would expect if I have a bigger fin,
right, a bigger surface, a tail fin, then I would need less beats to generate
the same propulsion. No?
But now you seem to say it was independent of the size, but dependent on the frequency.
It depends on the frequency linearly. What it does with the amplitude is not
known. It's how does amplitude contribute.
So it do vary their amplitude.
But there is not like a beautiful, simple relationship between how they control
their amplitude and how they propel themselves forward.
Okay. So what do we know there in terms of fish behavior? Do they,
in some continuous fashion, control their beat frequency or do they have preferred
frequencies at which they beat their tail?
You always have a preferred frequency. And the frequency is a resonance frequency.
Because on resonance frequency, you're beating your tail with a maximal amplitude.
And what fish could do in order to control their amplitude then is not directly
necessarily to change the amplitude, but change the stiffness.
Exactly. If you change the stiffness, you get a different resonance frequency.
And by having different resonance frequency, you get different amplitude.
So when you want to have high frequencies, you're probably making yourself stiffer.
And having a stiffer body gives you higher amplitude and higher frequencies.
This seems to be what fish are doing. Another interesting thing is that if fish
are steadily swimming, and the steadily swimming is called the cruising speed
of a fish, which is about one or two body lengths per second.
So if you're a fish and you're swimming at a cruising speed,
you can go on forever and forever and forever. You even don't get tired almost.
And it's shown that fish use only very few muscles to do that.
Most of its body is completely relaxed, and it uses a few of the anterior muscles
to create motion and pass a traveling wave along the completely or almost completely
passive tail. So this is why they don't get tired.
They're so good energy optimizers. That's impressive. It's something like when
you go and you run a marathon, you don't keep your legs stiff.
You're very relaxed, aren't you? Just because you want to conserve energy.
To have all these 42 kilometers going. But if you're a sprinter,
you do exactly the opposite.
Not exactly the opposite, but you use much more muscles in order to go very
fast in a long period of time.
Right, exactly. And fish do the same thing. They use their other muscles,
white muscles, when they have to do something very fast.
Saves their lives, get a prey. So this is for rapid turns, bursts,
acceleration, decelerations.
And they use it very seldom, only when they have to,
you because like a sprinter you get terribly tired of
it sure but now so i'm a fish i'm
flapping my my tail fin but i probably have
some oscillator sitting in my brain somewhere that's driving this
right and but now this oscillator is driving my the tail the the tail but i'm
i'm varying i'm varying the stiffness of my body in the meantime as well right
so um so then the question becomes,
should I control this oscillator that drives?
How do I control the fin, the tail fin oscillator relative to the stiffness
control I'm performing? What's the scaling there?
Yeah, I can answer you. I have no idea.
Okay. But you agree, no? It would be a pretty important relationship.
Absolutely. To manage. Absolutely. Absolutely.
Okay. Okay, because you could basically just break your muscles or the motor plant in any other way.
Absolutely, but I have no idea why they choose to control one parameter or the other.
So it's hard to ask fish, right? We can try, exactly. Okay, so now we know that...
How we're swimming, so we flap our tail to the frequencies, we control stiffness,
but then how does the sensing feed into that, right?
So, in some sense, I could then argue, well, I don't need much sensing to achieve that, right?
Well, if you have to control your swimming speed, so you probably,
what you do is like when you walk, you're walking with respect to some global reference frame.
So from this point of view, I don't know whether flow sensing is significant
here or not, because flow is also flowing, right?
Sure. So try to swim in a river and understand where you end up.
I don't know whether you ever got out swimming from a boat, and you're there
in a flow, and the boat is here, and you're swimming, and you kind of feel you're
staying still, or you're swimming in one direction.
And then you turn around and say, oh, where is the boat?
Oh, it's far, far away somewhere already. And you couldn't understand you're
being carried away with the flow. Right.
And probably fish have the same problem. I think they have the same problem.
Yes. What you feel in swimming, you can just extrapolate from when you're swimming, you're feeling track.
But the funny thing with the track is that, I don't know whether you ever thought
about it, but we humans don't have a track sensor.
True. Right? And we don't have a speed sensor.
Sensor so if you're sitting in a
car and you're driving very very fast way
too fast on a very good road which is sometimes too when
you have a very good car uh you're not
feeling anything you're not feeling the speed you could feel
if you close your eyes you could feel that you're almost still but what you're
feeling is acceleration try to drive a
car on a bumpy road on bad road and you're starting
to pump and your inner ear is going to react and you're
feeling the speed suddenly so uh why
do you know that you're going fast if you're going yourself not
by the car is that you're getting tired and this
is probably what fish is feeling when it swims against the stream either slowly
or or fast it's getting tired and it seems that the cruising speed of one two
body lengths is something that makes them least tired this is why they choose this sort of,
But could you imagine that the fish also adjusts its body stiffness relative
to the turbulence it's in?
Absolutely, I think so. Right. Because that's what also conserves a lot of energy, right? Absolutely.
To be more compliant with respect to the medium. Yeah.
But the other thing that was interesting in your data analysis is that you show
that in these relationships between, for instance, speed and tail fin beats,
that that relationship is essentially linear.
Do you think that's a coincidence? Is that just by accident,
or is there some biomimetic trick behind this?
I don't know. I'm almost fascinated about that. You know, there are 30,000 species of fish in the world.
And as far as I know, people haven't found a contrary example.
Why this law doesn't tell. So it's hard to believe for me, because I've always
thought that such a general law only holds in physics.
Because physics is physics. Like it's an unconscious world and it can't change in certain ways.
But biology is normally, it's like, you know, biology is always imprecise and
it's slippery and stinky and it can't be that beautiful.
Exactly. But actually there are also other laws in biology, more or less,
all statistical laws actually. In physics, you have precise laws.
In biology, you have statistical laws. But within the reasonable statistical
interval, these laws all hold.
And why is this like this? I don't know, but I'm very happy for that.
Because if I'm a control engineer, you know, there is nothing better for a control
engineer than a linear control law. Exactly.
Although maybe the precision you might find in physics is a bit overrated, right?
If you go to… Oh, yeah, yeah. So if you go down to, let's say,
the quantum level, then suddenly things become all probabilistic as well.
Absolutely. You have measurement errors and all these sort of things. But now…,
So now we've understood a lot about fish swimming, and subsequently you want
to build your robot to swim.
So how is that translation step? How do you do that? How do we go from this
now, our understanding of fish swimming, to actually a fish robot in a tank swimming?
We made many observations and tests from biology.
As you pointed out now, the most recent one is a linear control law,
which shows it also works for the fish. So we get a beautiful,
easy way to control our fish swimming speed.
And other things that we get from biology is the knowledge that if you swim
on cruising speeds, you only use the anterior muscles of your body.
What does it mean? It means that the rest of your body is just a passive carrier
of the traveling wave that passes energy onto the water.
And why it's very important from a technological point of view.
As we started our discussion with distributed actuation, which is very hard
to copy with the current technology we have, because we have rotating motors
that are very big and clumsy.
And what does it mean is that I can do a fish that has a single point of actuation only.
And this gives me a very reductionist approach.
So now, of course, everything depends on what my fitness function is.
If I don't build a robot that has to fight for its survival,
then I'm very happy with a single-point actuation.
But if I want to do a robot that can turn around rapidly, that some researchers
also in robotics are investigating, then probably this approach doesn't hold.
It has its limits. But in this case, I'm very happy that I, from biologists,
I derived a very reductionist approach that makes me able to develop simple robots.
But simple means normally reliable and cheap.
And these are two things that people appreciate in engineering.
Yes. But now tell me how you...
Practically built as a robot. What's the material you use for these fish, the robot fish?
We use silicons. We use all sorts of silicons because we have a technology how
we can vary the stiffness.
And we can manufacture tails or whatever body parts you need with whatever elasticity
and viscoelasticity you desire.
And by that we can also find the right relationship between stiffness of the
materials and the actuation momentums that we apply to the material.
And then actuation, how is that done? Oh, it's done with a simple servo motor.
There is fancier technologies, of course, with electroactive polymers,
artificial muscles and everything.
But if you wanted to do something cheap and reliable that people would buy in
the end. So maybe it's a right thing to do is to go for the conventional technology first.
So you have a name for this design methodology you pursued and you called it KISS.
Well, KISS is a very general, a well-known design methodology in all engineering,
which means keep it simple stupid.
And I think most of people who come from engineering know what KISS means,
especially software developments, where your software developers.
The code gets out of your hands so fast that if you're not taking a KISS approach,
you're doomed very soon.
And, yeah, we take this approach because I believe that from all the things
you can copy from biology,
you have to copy only the most important, most relevant things to get some things
that people can use later on.
And which is easy to make simple, reliable, and cheap.
But on the other hand, KISS methodology, I think, is also relevant in science.
Because if you want to keep things simple, it helps you this kind of approach.
Helps you to understand what is the importance of one or the other factor in
the phenomenon that you are investigating.
Complicating for example you take off the lateral line
sensing and you ask what happens if i take it off
i make my system very simple i take all sensors off what
can i still do with the system without any sensors at all what can i do with
a dead fish it's a very dead vicious simple right and then you establish well
certain things i still can do so i don't need a complicated system and this
gives me understanding what is actually the role of passive dynamics in a biological system.
Okay, now I put actuation in and ask, what can I do if I have an actuator?
And I establish what is this relevant for? And so on and so on.
And by just, you know, step by step complicating my system and controlling all
the parameters taking one or the other off, I can make generalizations.
But now, in some sense, I could also argue that what you're proposing is is
maybe also the opposite of KISS, right?
Because if you look at the outcome of this, you're saying, look,
if we want to understand fish swimming,
we have to actually now look at a much larger picture because suddenly we have
to include the morphology, we have to think about the actuation,
the sensing, or the sensing might be partially removed but not completely.
But, well, in a more traditional view, I could say, well, I have a more compartmentalized
view on how a certain function is realized and have clear defined modules.
They're well described with clear interfaces between them so I can understand what I'm doing.
While you are turning all that upside down now because you're saying, well, actually,
if the body itself can already swim and the actuation is actually modulating
these properties of the body and then the brain must be sort of modulating again
these properties of the actuation and the motor plant and so on.
So how is it actually simple? It also can be a complexification.
What you're describing here is a very fine, decent, respectable engineering method.
You take some modules that exist already and you build a new equipment. That's fine with that.
What I try to do is to create new models.
In engineering, you don't have a module for an embodiment.
So I'm not building a fish. I'm creating a method for building a fish.
Yeah, but the point then is...
You will have no more boxes if you want in that method, right? I invent new boxes.
Yeah, but what's your box then? Do you have a box morphology?
Yeah, I think I do have a box morphology.
And even within this box, I know already how to, if you tell me that my body
has to be with a certain viscoelasticity, I also have even,
well, I'm going to have engineering methods how you fabricate materials like this.
Okay. But then between the boxes, the interface between the boxes now also become constraints, right?
It's not only control signals or sensory signals that are exchanged between
these systems. Now these are also constraints.
For instance, my body has a certain stiffness.
Oh, yeah, but everybody has a certain stiffness. It's always been a constraint.
No, but I mean, this is not anymore an explicit control signal you exchange, right?
It becomes more a constraint on the control signals you might receive.
Yeah, but I'm not very worried of that because every system has constraints.
Every physical system has boundary conditions and constraints.
So you just have to establish them and build your control on top of that.
You're right, it can be complicated, especially when it comes to viscoelasticity.
People talk about soft robotics, but they talk about elastic robots.
They don't talk about viscoelastic robots. That's right.
Why they don't talk about viscoelastic robots? Because viscoelasticity is a
real pain for post-physicists and engineers to describe, to analyze,
and we're not even talking here about control because I don't know any viscoelastic
robots that people control. Right.
So if we go from your biomimetics to, let's say, this KISS methodology you're proposing,
do you see this as, let's say, a real paradigm shift in engineering or is this
more of an elaboration of the standard practice?
Well, I am ambitious enough to say it's a paradigm shift, of course,
but on the other hand, I know people have tried it in different fields quite a lot.
If you take a clever biomimetic approach, this is exactly the questions what you're asking.
What features are relevant that I should copy? So people have done it maybe
not so consciously, but there are many fine examples probably around already
where people in one way or other are using the same approach.
Right. But you are implying there's also a not so clever biomimetic approach.
Yeah, yeah, yeah.
Straightforward. So now tell me, what's your prediction with respect to the
viability of this approach?
When will we see the first artificial robot fish swim around the world,
let's say, autonomously?
You know, swim around the world autonomously actually doesn't depend exactly.
I believe it doesn't depend on the technology I am developing.
It depends on the technology of power sources.
But we imagine we have it solved, we live off plankton, we have a plankton driven robot fish.
Suppose we have a power source that lasts forever, so it's actually a very interesting
test to do and it's a great idea actually to have a grand challenge of robotic
fish swimming. Exactly, around the world.
Instead of overseas or somewhere. Exactly. It's actually a great thing to do.
Okay. I have to say is that our fish breaks down about every second day or so when we test it.
So we're very far from it at the moment. No, but what are you expecting?
So it's interesting, right? The power is still the biggest problem.
That's also why robots won't conquer the world anytime soon. Right.
But what's your expectation with respect to just the control of the swimming, for instance?
So when do you think we can really have mature systems that will not propel
themselves anymore with propellers as we know them, but really with fish-like
bodies that are really swimming?
Maybe I'm overconfident now here, but I think my beautiful fish would be definitely
able to swim overseas unless it gets eaten or something.
Okay. If I had to find manufacturing technology behind it, because what it really
comes to is the next step in biomimetics.
First you do your engineering, you develop new engineering approaches.
Then you apply those engineering approaches. But then it comes to manufacturing, right?
And manufacturing is a very important part of underwater robotics.
So the biggest problem in underwater robotics… It's water. Sure.
And the biggest problem is how to keep water out of the robot.
And what we can demonstrate is with commercial underwater vehicles,
where they manage to do it rather well.
So I would say that if I had good manufacturing technology, my fish and power
source would last forever. My fish would definitely be able to swim overseas.
Another problem is how long does it take? Exactly.
And another problem is where does it end up? Exactly. Like a letter in a bottle, no? Exactly.
It's an expensive bottle. Right. But now, so to finish up, two last questions.
So, Mariam, you're in this business for quite some time. you build up this your,
biorobotics lab in Estonia so.
What would be the one law of Maria that you would like us to adhere to in our
biomimetic exploration of the universe?
Maria's law, what is it? Oh, now you're going to ask something about,
like, does God exist or something. This is a so general question.
I don't have a single law. So I want to be very careful with it.
All my gifts approach for the fish works fine. I'm very happy with that.
But I didn't want to make grand claims.
Something like, this is generalizable to everything. Take this method and apply
to insects or apply them to other animals because then it comes,
I think what happens and it becomes very general.
And a very, very general law is applicable to everything, but it lacks details.
So it's like I can give you a law, but this law is like, you know,
I can also give you a law for playing piano. You know, the law for playing piano
is that you have to press the right key with the right finger at the right time.
And you always can play your pianos like that. But it doesn't help you really
to play anything, right?
So what I'm afraid of is to giving a similar very general suggestion like, you know, be happy.
Don't worry, be happy or something like this. But since you're not a professional
piano player, I can challenge you on this.
So your KISS methodology could be an example of a principle or a law you would propose.
Or what you tell me now, you're saying, look, all paradigms have limitations.
This would be another potential law. So in that sense, it's not you have no laws, right?
So we just have to agree on which one is the one today, right?
So what would be the one today? day i'm still
afraid of speaking trivialities if i say
that yes hello you have to do in biomimetics is
keep it seems so stupid it's just it's a little bit
of a triviality but maybe one thing if i if i'm now thinking that i'm a biomimetic
engineer and i want to have my problem which is something different maybe it's
insect flight or something so maybe really what i would do is is like a physicist
approach with taking there that we have a phenomenon And then we make a controlled experiment.
We try to knock out some phenomena and investigate what is the impact to the
other, to the performance of the system, and to apply this law within our box
of biomimetics. But, of course...
It's very hard to predict how scalable it is because the phenomena are dependent
on each other and your measurements are imprecise.
You're maybe not careful with some design parameters and so on.
But also, on the other hand, still physicists have the same problem,
right? Experimental physicists.
They don't know how the parameters depend on each other and they're still doing it.
That's why they do experiments, right? Yeah, exactly. Exactly.
So I think this experimental methodology,
if you want to have a key message, like what's the meaning of life or something,
then it would be to have a controlled experiment like this to establish a relative
importance of the phenomena to your system regarding your fitness function that you're aiming to.
Okay. So keep on experimenting. Yeah. That's the law. Yeah, it turned out to be awfully trivial.
Now we got the law. And then last one is a prediction. So if I go visit you
five years from now, and I'm going to say, look, five years back,
you made this one prediction.
Today, I'm checking with you whether it came true.
What is one prediction you would be willing to make today you're most enthusiastic
about, you feel most committed to right now?
Within biomimetics, you mean? Sure, within the envelope of our current discussion.
I think there are some very, if you look at technology, there are some very
general trends that are going on.
One thing is that technology is robotic technology.
If we talk about parametric robots, it's robots are moving closer to humans.
So we're moving from industry to service, which means that they have to be safer.
And one way to make them safer is to make them soft. That's just one solution
to making some safeguards. There are others, of course, learning methods and everything.
But one thing you can do is to make robots soft.
And, of course, there are also trends of miniaturization.
Happening at the same time and i'd
say it it's maybe going to taught
simplification in one in in some sense
that said in traditional robotics where so very much emphasis have been on a
control fine beautiful control of very complicated systems i think the prediction
is really that people are going to explore rather how to keep the control minimal
to make robots that are safe and robust.
Independent on whether your control laws are exactly correct or not.
Will we have those in five years? Is that what you're saying?
Five years, we'll have more safe and robust robots?
I think we definitely have more safe and robust robots. And I think also they're
going to be compliant because what you can see is an increasing trend.
More and more research papers are written on compliant robots.
They are now more restricted to compliant robot arms for very obvious reasons,
but I think it's also moving to other fields of the research.
So I think the next step then with compliant robots, we have a good theory for
making robots with a changing elasticity, changing stiffness.
And we understand also a theoretical background of it quite well.
But one thing that we have trouble understanding is viscoelasticity.
Well, most of the bodies are viscoelastic, and they are set for particular reasons.
It is that if you have a viscoelastic body, you dissipate energy.
When you dissipate energy, you filter
out disturbances, and filtering out disturbances is an important thing.
Right. So I think where it is going in five years, you're going also to see
research on this sort of embodied signal processing.
Okay, that's a good one. All right, I come back to you and check.
Well, yeah, it's a self-fulfilling prediction because I'm going to research
on it anyway. So you come back in five years, I fulfilled my prediction.
Okay, Marcia Kruzma, thank you very much for this conversation. Thank you.
The csn podcast was produced by the convergent science network of biometrics
and biohybrid systems a project funded by the european seventh research framework program,
for more interviews recorded lectures or upcoming conferences in the field of
biometrics and bio-hybrid systems, go to csnnetwork.eu.
Music.
And thank you for listening.