Maarja Kruusmaa on biomimetic fish and lateral line sensing

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Season 2012
Season 2012
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How does a dead fish swim upstream, and what does that reveal about the hidden intelligence of body design? Maarja Kruusmaa explores the surprising physics of fish locomotion, lateral line sensing, and why propellers may not be the last word in underwater engineering.

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Kruusmaa challenges naive biomimetics, the tendency to copy everything from nature without understanding which features actually matter. She draws parallels to early propellers with feathers and cars with horse compartments, arguing that the real engineering challenge is identifying which biological principles are worth extracting. While propellers remain a mature and powerful technology, fish outperform them in energy efficiency and acoustic stealth, leaving almost no wake behind them. The key advantage lies in distributed actuation across hundreds of muscle fibers, something current motor technology cannot replicate at practical scales.

The conversation dives deep into fish swimming mechanics. Kruusmaa explains how swimming speed relates linearly to tail-beat frequency, while amplitude remains an independent variable. Fish control their propulsion primarily through stiffness modulation, which shifts resonance frequency and thereby changes amplitude. At cruising speed, fish use remarkably few anterior muscles while the rest of the body remains passive, explaining their extraordinary endurance. The discussion of a dead fish swimming upstream in George Lauder’s lab at Harvard illustrates how body morphology alone can generate propulsion in periodic turbulent environments, a striking example of morphological computation.

Kruusmaa introduces the concept of inverse biomimetics, using robotic fish as tools for biological discovery rather than just engineering products. Her work on artificial lateral line sensors demonstrates this approach: by selectively disabling parts of a robot’s sensory array, researchers can generate hypotheses about biological function that are difficult or impossible to test in living fish. The lateral line’s dual modality, measuring both flow velocity and pressure, enables fish to build complex hydrodynamic maps of their environment, a capability roboticists have barely begun to explore.

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Both the triumphs of humanity and its most evil deeds have resulted from collaboration. In a time where humanity is required to aspire to the former and minimize the latter, the question arises of how collaboration arises and why it fails. Surprisingly, this phenomenon, so central to who we are, is not well understood. Hence, a collaborative effort is required to understand collaboration in its full biological, psychological, sociological, cultural, and economic complexity and to translate this understanding into operational impact. This series of podcasts is one step toward achieving these complementary goals. The Collaboration Podcast presents interviews with people who are central orchestrators of collaboration in various domains including business, government, science, art, health, sustainability, and the military. The discussions were conducted by Prof. Dr. Paul F.M.J. Verschure and members of the Program Advisory Committee of the Ernst Strungmann Forum on Collaboration (https://www.esforum.de/forums/ESF32_Collaboration.html) during 2021 and had the goal to sketch a map of opportunities, challenges, and obstacles in human collaboration. The forum took place in May 2022, and now we would like to share this series of interviews with a broader audience. The full report of the Forum will be published in 2023 by MIT Press. The podcast was produced by the Convergent Science Network (https://www.convergentsciencenetwork.org/). Context: The stability of social systems depends critically on realizing sustainable methods of “collaboration,” yet how and by which means collaboration is achieved is not clearly understood; neither are the conditions or processes that lead to its breakdown or failure. Collaboration can be understood as cooperation between agents toward mutually constructed goals. Part of the reason for our lack of understanding is that the phenomenon of collaboration is, by nature, a highly multidisciplinary problem, and effective research into its complexities has been difficult to achieve across the broad range of scientific and technical disciplines involved. The need for a fundamental understanding of collaboration, however, has become increasingly important. Not only does humankind demand answers as it attempts to address critical challenges at multiple scales (e.g., climate change, migration, enhanced automation, social and economic inequality), but ever-increasing technological and economic means of interconnecting people and societies are disrupting long-established, familiar patterns of how we interact. Radical technological changes that are ongoing have the potential to reshape collaboration in ways that are currently hard to predict or influence (e.g., by altering configurations in interaction, information creation, and modes of communication). On one hand, such changes could disrupt hitherto stable forms of collaboration by affecting critical communication channels and traditional roles, as can be observed in the rapidly changing patterns in governance, commerce, and social interaction. Conversely, technology could lead to the emergence of novel, successful forms of collaboration that deviate from traditional “hierarchical” architectures. Evidence of this can be seen in areas as diverse as highly automated manufacturing plants, the open science movement, collaborative software repositories, user-centered services, and the sharing of economy-based modes of organization. Without a fundamental understanding of the mechanisms, processes, and boundary conditions of collaboration, it is not possible to evaluate or predict which of these possible scenarios are sustainable or even plausible. The Forum “How Collaboration Arises and Why it Fails” (May 8–13, 2022, Location: Frankfurt am Main, Germany) Chairs: Andreas Roepstorff and Paul Verschure Program Advisory Committee: Jenna Bednar, Julia R. Lupp, Bhavani R. Rao , Andreas Roepstorff, Ferdinand von Siemens, and Paul Verschure

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  • fast_forward00:00:03 - This is the Convergent Science Network podcast. Leading researchers in the domain
  • fast_forward00:00:10 - of neuroscience, brain theory and technology are interviewed by Paul Verschoor and Tony Prescott.
  • fast_forward00:00:19 - So this is Paul Verschoor with Maria Kruzma, one of the speakers in our BCBT summer school.
  • fast_forward00:00:27 - And um you focus very
  • fast_forward00:00:30 - much in your talk about building an
  • fast_forward00:00:32 - artificial fish right that was very much the topic the topic
  • fast_forward00:00:36 - of your of your talk and of a lot of your research is fish yes so um and you
  • fast_forward00:00:43 - started out with a very fundamental question like what when we talk about biomimetics
  • fast_forward00:00:49 - what do we really copy right that was your fundamental question you started out with So tell me,
  • fast_forward00:00:55 - what are we essentially copying in biomimetics and are we really copying?
  • fast_forward00:01:01 - I think we're copying, but we really don't always think of whether we are copying
  • fast_forward00:01:07 - the wrong or right things.
  • fast_forward00:01:10 - There is an approach in biomimetics that you can call the naive biomimetics approach.
  • fast_forward00:01:16 - You just copy everything. I think like first propellers had feathers because
  • fast_forward00:01:21 - people thought the feathers are very important things.
  • fast_forward00:01:25 - You know, on every flying object
  • fast_forward00:01:28 - you have feathers, so you want to do propellers, you have feathers too.
  • fast_forward00:01:31 - But in the end they understood that the helicopters are hovering very well without feathers.
  • fast_forward00:01:37 - From the point of view of aerodynamics, this doesn't really matter in this case.
  • fast_forward00:01:41 - And I think if we look back to our own research now, 10 years ago, after 10 years,
  • fast_forward00:01:48 - the projects that we are doing right now could look equally funny because of
  • fast_forward00:01:52 - these wrong ways and wrong approaches we are taking and copying irrelevant things.
  • fast_forward00:01:58 - Like first cars in history they
  • fast_forward00:02:01 - had a place in front of the car for
  • fast_forward00:02:04 - a horse and it was there for a long time because cars
  • fast_forward00:02:08 - involved from the horse carriage before people
  • fast_forward00:02:11 - understood that these places for a horse but they are completely unnecessary
  • fast_forward00:02:15 - and redesign the cars and i suppose we do the same things and if we ask a question
  • fast_forward00:02:21 - like what should we copy or what is relevant and then we maybe can avoid taking
  • fast_forward00:02:27 - this wrong direction once in a while. Mm-hmm.
  • fast_forward00:02:31 - So, um...
  • fast_forward00:02:34 - So, but if you now look at the fish case as a specific target of your research, right?
  • fast_forward00:02:40 - So, what makes fish so interesting?
  • fast_forward00:02:44 - Well, naive biomimetic approach says you fish are excellent swimmers.
  • fast_forward00:02:49 - Nothing swims as well as fish.
  • fast_forward00:02:52 - So, from this point of view, it makes sense to copy fish, see how it works,
  • fast_forward00:02:57 - and see how does it compare to a propelled vehicle.
  • fast_forward00:03:02 - But where you come to
  • fast_forward00:03:05 - difficulties i'm a little critical now about biomimetic approaches
  • fast_forward00:03:09 - actually in my own research too i think that propellers
  • fast_forward00:03:12 - are wonderful inventions you see nature has been working on methods to travel
  • fast_forward00:03:19 - underwater for millions and billions of years it never she never came out up
  • fast_forward00:03:25 - with a propeller but it's a wonderful simple device and very powerful So,
  • fast_forward00:03:30 - but definitely if you want, if you look at the technology of propellers,
  • fast_forward00:03:35 - what happens there for the last, I don't know, tens of years,
  • fast_forward00:03:40 - all the development is already very incremental.
  • fast_forward00:03:42 - And if you want to make a breakthrough to come up with something completely
  • fast_forward00:03:46 - different, then you could look at undulating bodies like fish have to try to
  • fast_forward00:03:53 - design something different. Mm-hmm.
  • fast_forward00:03:55 - But now, if you talk about propellers, you might find them fantastic,
  • fast_forward00:03:59 - but in some sense, fish seem to be more efficient swimmers than...
  • fast_forward00:04:03 - They are more efficient. They are more energy efficient.
  • fast_forward00:04:06 - That's one way why you want to look at fish.
  • fast_forward00:04:10 - And another thing is they're quiet. Right.
  • fast_forward00:04:13 - They don't leave the wake behind them. It means actually, if you look at the
  • fast_forward00:04:17 - wake behind the propeller, this is all energy got wasted in water.
  • fast_forward00:04:22 - So in that sense, I could argue that the very positive statement you made earlier
  • fast_forward00:04:28 - about propellers is maybe a little bit biased, but that's the current state
  • fast_forward00:04:32 - of the art. It's just the best we can do.
  • fast_forward00:04:34 - It's the best we can do, but I think we're doing very well.
  • fast_forward00:04:37 - We're researching on the fish robots, but they're not very efficient at the
  • fast_forward00:04:42 - moment. And so, really, do you understand what is a mechanism?
  • fast_forward00:04:45 - What makes her so efficient?
  • fast_forward00:04:47 - We still don't understand how we should mimic this technology.
  • fast_forward00:04:53 - One important aspect is that fish have distributed action.
  • fast_forward00:04:59 - They have hundreds and hundreds of muscle fibers all over the body.
  • fast_forward00:05:03 - And if you look at the current technology we have in hand, What is a reliable
  • fast_forward00:05:07 - technology is really still the technology of rotating motors.
  • fast_forward00:05:13 - Okay, we have other fancy things that I also used to work with,
  • fast_forward00:05:16 - like electric, electroactive polymers and artificial muscles and nitinol or
  • fast_forward00:05:22 - all these contracting actuators.
  • fast_forward00:05:23 - But they have their own issues. This is not a mature technology.
  • fast_forward00:05:29 - And now if you want to use a mature technology that we have,
  • fast_forward00:05:33 - Now it's just DC motors and make a distributed actuation system.
  • fast_forward00:05:37 - You end up with something very, very big and clumsy in the end.
  • fast_forward00:05:41 - You end up with big and clumsy. It becomes energy inefficient.
  • fast_forward00:05:45 - It becomes hard to control and really, really big. You don't make little agile,
  • fast_forward00:05:50 - simple devices out of that.
  • fast_forward00:05:54 - So we agree that in terms of our technology,
  • fast_forward00:05:58 - best solution to the problem fish are still doing better okay
  • fast_forward00:06:01 - so swimming fish do better fish do
  • fast_forward00:06:04 - better so but then what i would like to know um
  • fast_forward00:06:08 - what makes them better okay what are the tricks what are these principles that
  • fast_forward00:06:13 - as a biomimetic engineer you might want to extract from fish to build better
  • fast_forward00:06:18 - fish like machines right so what makes fish better swimmers every Every machine
  • fast_forward00:06:22 - works by transmitting energy from their own body to the surrounding water.
  • fast_forward00:06:28 - By transmitting the momentum of energy, they push themselves forward and move the water.
  • fast_forward00:06:34 - The way propellers do it is by rotating the plates.
  • fast_forward00:06:38 - The way fish do it is by undulating their bodies and creating a traveling wave.
  • fast_forward00:06:44 - And then it passes the momentum of the water. and how do they do it so efficiently?
  • fast_forward00:06:53 - We didn't know exactly. So there are, again, many aspects of it.
  • fast_forward00:06:57 - One is the body, embodiment itself.
  • fast_forward00:07:02 - Now when it comes to physics of compliant bodies and water interaction is a
  • fast_forward00:07:07 - terribly, terribly difficult field to go into to understand how compliant bodies
  • fast_forward00:07:13 - and water interact with each other.
  • fast_forward00:07:15 - There is no mathematician who could sit down and write up the equations and
  • fast_forward00:07:22 - say that this is optimal solution to the problem.
  • fast_forward00:07:24 - Nobody can do that. So what we have is more like an experimental approach.
  • fast_forward00:07:29 - We try to optimize surface area or the fins or we try to optimize the length
  • fast_forward00:07:35 - of the body or whatever comes to your mind.
  • fast_forward00:07:37 - And see that if we get more efficiency out of the fish. But now,
  • fast_forward00:07:42 - one thing that's interesting about fish is that they actually have developed
  • fast_forward00:07:47 - a pretty advanced sensing system.
  • fast_forward00:07:50 - To be able to really measure in detail the properties of the medium around them,
  • fast_forward00:07:57 - right? So these are these lateral line sensors.
  • fast_forward00:07:59 - Lateral line sensor is a very specific sensor.
  • fast_forward00:08:03 - In a way, it's very general because it's common for all fish,
  • fast_forward00:08:07 - all fish have a lateral line.
  • fast_forward00:08:11 - But on the other hand, it's only common for those creatures who live underwater. water.
  • fast_forward00:08:16 - Nobody walking on the ground or flying in the air doesn't have a lateral line.
  • fast_forward00:08:21 - And one problem with mimicking lateral line sensing is that it is not clear
  • fast_forward00:08:28 - understanding among biologists either how this marvelous sensing organ actually works,
  • fast_forward00:08:34 - in which cases is it active and in which cases it's passive, in which behaviors,
  • fast_forward00:08:42 - and how this information is processed in the brain of the fish.
  • fast_forward00:08:49 - So if you want to do a biomimetic approach of a lateral line,
  • fast_forward00:08:53 - it's like shooting in a dark room.
  • fast_forward00:08:55 - But here comes another aspect of biomimetics, which I would call inverse biomimetics.
  • fast_forward00:09:03 - We think of biomimetics that we go from science to technology.
  • fast_forward00:09:08 - Scientists find out something, they discover things. things,
  • fast_forward00:09:12 - investigate things that exist that already they discover, they make a discovery,
  • fast_forward00:09:17 - and then they pass over to us engineers.
  • fast_forward00:09:19 - And the engineers say, uh-huh, here is a great phenomenon. How do I get some
  • fast_forward00:09:24 - use out of it? And then they develop the technologies.
  • fast_forward00:09:27 - So what biomimetics actually does is it turns discoveries into inventions.
  • fast_forward00:09:34 - But if you did inverse biomimetics, you could do the other way around.
  • fast_forward00:09:38 - You turn inventions into discoveries.
  • fast_forward00:09:41 - And this is a little bit what we're doing with our fish lateral line experiments in philosophy project.
  • fast_forward00:09:48 - It's very hard for a biologist to investigate the lateral line.
  • fast_forward00:09:52 - It's a lot of work and I have a very deep respect to biologists who are working
  • fast_forward00:09:57 - with it because it takes lots of, lots of, lots of patience.
  • fast_forward00:09:59 - For example, if you want to understand what is the role of a certain modularity
  • fast_forward00:10:06 - of lateral line sensing, as a sense pleasure or the sense flow in certain behaviors,
  • fast_forward00:10:12 - it's very hard or even impossible to knock out some parts of the systems as
  • fast_forward00:10:17 - a pharmaceutical or surgical or do these experiments.
  • fast_forward00:10:20 - And sometimes they left only behavioral tools, which is very,
  • fast_forward00:10:24 - very hard work for people.
  • fast_forward00:10:27 - And here you could take a robot or a technology as a method for biologists.
  • fast_forward00:10:34 - Say that I have now my artificial robot here, artificial fish,
  • fast_forward00:10:38 - and it has an artificial lateral line.
  • fast_forward00:10:40 - What if I knock out the left part of the artificial lateral line and see what happens? Mm-hmm.
  • fast_forward00:10:46 - Does it give you anything as a biologist to think about?
  • fast_forward00:10:50 - And if you do the inverse biomimetics in the right way, you could have a very
  • fast_forward00:10:55 - big impact to what biologists can find out.
  • fast_forward00:10:59 - Right. And it also means that you might have what you want to shoot for then
  • fast_forward00:11:03 - is a continuous interaction between,
  • fast_forward00:11:06 - let's say, hypotheses generated from the scientific domain validated in the
  • fast_forward00:11:12 - engineering with predictions going back into the science.
  • fast_forward00:11:15 - Exactly. To close the loop. Exactly. That's the role the biomedics can play
  • fast_forward00:11:19 - here, which we clearly see also in your project.
  • fast_forward00:11:22 - Yes. But now, to look at the lateral line, essentially, it's a strip of sort
  • fast_forward00:11:27 - of hairs that runs along or hair-like elements that runs along the side of the fish's body.
  • fast_forward00:11:34 - And it also has similar sensors on its head or on other parts of the body.
  • fast_forward00:11:39 - And it's used to sense exactly what? What's the lateral line exactly sensing?
  • fast_forward00:11:44 - Lateral line has two modalities. Actually, there are two ways of measuring flows.
  • fast_forward00:11:49 - One is that you can measure flow velocity, and another thing,
  • fast_forward00:11:52 - you can measure pressure.
  • fast_forward00:11:54 - In very few conditions, when you have a laminar flow, they are very easily related
  • fast_forward00:11:59 - to each other by Bernoulli's law when they are inversely proportional.
  • fast_forward00:12:03 - But otherwise, if you are in a difficult hydrodynamic environment,
  • fast_forward00:12:07 - Then how does this different modality, do these different modalities add up
  • fast_forward00:12:12 - to each other is also a very complicated interplay.
  • fast_forward00:12:15 - But fish is equipped with both of the systems. They have canal lateral lines
  • fast_forward00:12:21 - and can measure the pressure difference.
  • fast_forward00:12:24 - And it has a surface lateral line, what biologists call neuromasts, that measure the flow.
  • fast_forward00:12:32 - These are just simply like little cantilevers, little beams that bend in a flow,
  • fast_forward00:12:38 - and depending on the angle of bending, you can figure out how strong is the flow.
  • fast_forward00:12:44 - So what happens next is very complicated and nobody exactly knows what happens
  • fast_forward00:12:49 - because some of the processing, signal processing of the signals is done in
  • fast_forward00:12:54 - a, so to speak, hardware already of the fish.
  • fast_forward00:12:57 - Because hair cells have a different stiffness and different height and they
  • fast_forward00:13:03 - probably also work as some sort of filters, low-pass, high-pass filters.
  • fast_forward00:13:09 - And some of the, if you think of how something like a fish, who is a rather
  • fast_forward00:13:14 - unsophisticated, uneducated creature, could do such a sophisticated signal processing.
  • fast_forward00:13:19 - So one answer to that is that it probably does it already in embodiment.
  • fast_forward00:13:24 - The sensors do some of the filtering and cleaning up the crap from all the noise
  • fast_forward00:13:29 - from what they're getting in. And then it goes to prey.
  • fast_forward00:13:33 - And then the fish makes a decision based on what it feels. Yeah,
  • fast_forward00:13:37 - but the decision this brain is going to make is in this domain of what's called rheotaxis, right?
  • fast_forward00:13:44 - It's like the response to a current, right?
  • fast_forward00:13:49 - That's one thing that fish do is that they respond to a stimulus which is a current or flow.
  • fast_forward00:13:56 - The simplest form of rheotaxis is when fish orient their nose against the flow.
  • fast_forward00:14:03 - There are several reasons of doing that. One reason is that if you're a migrating
  • fast_forward00:14:07 - fish, you have to go upstream to find your spawning place.
  • fast_forward00:14:13 - Then you have to know the direction of upstream.
  • fast_forward00:14:16 - And this is a classical example of a real Texas behavior.
  • fast_forward00:14:20 - But you could also just try to be in sweet spots on a flow where you get a lot
  • fast_forward00:14:25 - of orders and a lot of food flowing by.
  • fast_forward00:14:28 - And then you just sit there and open your mouth and hoping for the fresh plankton of the day.
  • fast_forward00:14:33 - Exactly. So what is interesting about that, right, is that these fish with the
  • fast_forward00:14:39 - lateral line sensor can also live in a world where you have these dynamic objects,
  • fast_forward00:14:45 - which are essentially the properties of the flow.
  • fast_forward00:14:48 - Right, where you can say, okay, here I have a flow profile that I like,
  • fast_forward00:14:53 - but here it's more like a flow obstacle that I should avoid.
  • fast_forward00:14:56 - Avoid so it's as if you walk through a mist right where you have high density
  • fast_forward00:15:01 - and low density spots in a field filled with mist so which you would navigate
  • fast_forward00:15:06 - and you would go for the open spots because that's where you would have more visibility,
  • fast_forward00:15:10 - it looks as if they have a very,
  • fast_forward00:15:14 - Pretty complex representation of the dynamics of the medium that they're in.
  • fast_forward00:15:17 - It's an interesting question. I don't know how much spatial memory the fish have.
  • fast_forward00:15:23 - But if you think of salmon, for example, that does orderly attacks and combining
  • fast_forward00:15:30 - it with information from the flow.
  • fast_forward00:15:33 - And it finds its home river where it was born once and then goes upstream and
  • fast_forward00:15:38 - migrates. So maybe it definitely fuses this also with other kind of information.
  • fast_forward00:15:44 - They use information from the magnetic field and in the air and all sort of different.
  • fast_forward00:15:49 - So I think all fish like other animals are patient optimizers.
  • fast_forward00:15:55 - They're just trying to find them a likelihood, maximal likelihood from all the
  • fast_forward00:16:01 - information they get in.
  • fast_forward00:16:02 - But if you're, I don't know what's going on in fish's brain,
  • fast_forward00:16:06 - but if you take it from an information theoretic point of view and you look
  • fast_forward00:16:10 - at this flow maps that fish possibly could have,
  • fast_forward00:16:12 - then you can think of that fish could
  • fast_forward00:16:15 - build up a really complicated map of discriminate hydrodynamic events.
  • fast_forward00:16:19 - Events tells that there is a certain hydrodynamic event
  • fast_forward00:16:23 - in one spot and certain in the other and then
  • fast_forward00:16:26 - you can think that it maybe adds up as kind of a
  • fast_forward00:16:29 - robot environmental map as a
  • fast_forward00:16:32 - topological map or a grid-based map or something you don't know what happens
  • fast_forward00:16:36 - in its brain but this is something very new that roboticists don't do they don't
  • fast_forward00:16:42 - build maps based on flow information but fish probably have this somehow incorporated
  • fast_forward00:16:47 - into their environmental mapping. Right, yeah.
  • fast_forward00:16:50 - That's very exciting. But then...
  • fast_forward00:16:55 - In some sense, we cannot ignore this whole issue that what starts to become
  • fast_forward00:17:01 - important now is also your relative size in terms of the medium you're in, right?
  • fast_forward00:17:06 - Like, for instance, if you're a whale, even though they're not fish,
  • fast_forward00:17:08 - but let's say you have that scale, or let's say you're a whale shark,
  • fast_forward00:17:11 - so you're still a fish, but you're very big, then in some sense,
  • fast_forward00:17:15 - a lot of this turbulence stuff, you just don't care about, right?
  • fast_forward00:17:18 - Because you will overcome a lot of this.
  • fast_forward00:17:21 - I think sharks care a lot. It has been shown that shark skin is actually a sensing organ.
  • fast_forward00:17:29 - And what sharks probably do is what is called eddy odoratexis,
  • fast_forward00:17:34 - that they get in the information of the smell and combine it with what they
  • fast_forward00:17:40 - feel with the rattler line about the local flow.
  • fast_forward00:17:44 - And then they make decisions where to go based on this information.
  • fast_forward00:17:48 - If you think of sharks' ability to localize plot,
  • fast_forward00:17:55 - even few drops of plot far, far away, they must have this ability very fastly
  • fast_forward00:18:02 - to… So that means it's not then relevant for navigation itself,
  • fast_forward00:18:07 - but what it's used for is for source localization.
  • fast_forward00:18:12 - We didn't know whether it's also relevant for navigation itself or just source localization.
  • fast_forward00:18:18 - But, well, it's kind of, I don't know whether it's a relevant question.
  • fast_forward00:18:22 - Why do you have to navigate anyway in order to get somewhere, right?
  • fast_forward00:18:25 - If you're a really small fish, right, then in some sense you want to be really
  • fast_forward00:18:30 - clear or you want to understand where the turbulence is because you have to overcome,
  • fast_forward00:18:35 - you have to generate a sort of propulsive force to overcome the counterforce
  • fast_forward00:18:39 - you receive from your environment.
  • fast_forward00:18:41 - So there, the scaling of yourself to the medium, also expressed in this Reynolds
  • fast_forward00:18:46 - number, will become pretty important.
  • fast_forward00:18:49 - Well, if you are a whale shark, that's something you don't have to worry about too much.
  • fast_forward00:18:53 - Yeah, but this is what is called the negatory of taxis.
  • fast_forward00:18:57 - So instead of going into the flow, you try to keep away from the stream because
  • fast_forward00:19:01 - you're such a small fish, you're afraid of being blown away with a stream.
  • fast_forward00:19:07 - So it's equally important whether you have a big Reynolds number or a small Reynolds number.
  • fast_forward00:19:12 - Probably your equipment is different and your mechanisms are a little different.
  • fast_forward00:19:17 - Okay, so that's a bit the question I want to get to.
  • fast_forward00:19:20 - So we are scaling this relationship or we quantify this relationship with the
  • fast_forward00:19:24 - so-called Reynolds number, right?
  • fast_forward00:19:25 - It tells you something about this relation between, let's say,
  • fast_forward00:19:29 - on the one of the forces that the medium exposes you to and those that you can
  • fast_forward00:19:32 - generate yourself. Right.
  • fast_forward00:19:34 - And so then the question is, if I'm operating at, let's say,
  • fast_forward00:19:39 - low Reynolds numbers, are my principles of operation qualitatively different
  • fast_forward00:19:44 - as when I'm operating on high Reynolds numbers?
  • fast_forward00:19:47 - And where is the transition exactly? So can you say something about that?
  • fast_forward00:19:51 - Is that understood in some way?
  • fast_forward00:19:52 - No, I don't think if I ask the same thing from biologists, there is no kind
  • fast_forward00:19:57 - of firm answer that, yeah, we established that already.
  • fast_forward00:20:01 - Both small and big fish have a lateral line, so it must mean it's somehow important.
  • fast_forward00:20:10 - If you're living in this medium, you have to sense this medium.
  • fast_forward00:20:13 - But nobody seems to be investigating what is the correlation between the environment
  • fast_forward00:20:20 - you're living in and the topology of your lateral line, for example.
  • fast_forward00:20:23 - Why some of the fish have more lateral line sensor in the head and none of them in the body.
  • fast_forward00:20:28 - And some fish have lots of sensors in the rear part of their body.
  • fast_forward00:20:32 - And does it depend on environment or the way they're feeding or lifestyle and
  • fast_forward00:20:38 - other aspects of the lifestyle?
  • fast_forward00:20:40 - Nobody has a beautiful, simple law to answer for that.
  • fast_forward00:20:45 - That's interesting. So, because what is interesting there is that it goes a
  • fast_forward00:20:51 - bit back to this earlier point that although first you think,
  • fast_forward00:20:54 - okay, fish swim, they swim in a turbulent medium,
  • fast_forward00:20:57 - they have to sense this medium to optimize swimming, so they need lateral line.
  • fast_forward00:21:01 - But then actually when you push it further, you see the lateral line might actually
  • fast_forward00:21:04 - be used for something completely different.
  • fast_forward00:21:06 - And that swimming comes almost for free for fish because, as you also mentioned in your talk,
  • fast_forward00:21:11 - that fish show a lower metabolic rate in turbulent environments as opposed to
  • fast_forward00:21:17 - non-turbulent environments, which seems a very surprising result,
  • fast_forward00:21:20 - isn't it? In a periodic turbulent environment. Okay, so explain.
  • fast_forward00:21:24 - Periodic turbulence is on moderate Reynolds numbers when you have like beautiful
  • fast_forward00:21:28 - regular vortices normally, say, appear behind some object.
  • fast_forward00:21:34 - Object and there's some evidence biology biologists suggesting that fish like
  • fast_forward00:21:40 - to be in environments like that and also if you catch a fish from environment
  • fast_forward00:21:44 - like that it's and investigates its metabolic rate it appears that fish are
  • fast_forward00:21:50 - less tired and there is a very interesting.
  • fast_forward00:21:54 - Experiment done in george lauder's lab in harvard with jimmy leo and his colleagues
  • fast_forward00:22:01 - with a dead fish swimming upstream.
  • fast_forward00:22:05 - So definitely their lateral line is not concerned at all because you're dead.
  • fast_forward00:22:09 - Exactly. You can't sense anything.
  • fast_forward00:22:11 - Even more, you can't even actuate yourself.
  • fast_forward00:22:14 - Okay. So the only thing you're left with is your body embodiment which somehow
  • fast_forward00:22:19 - has to create propulsion.
  • fast_forward00:22:24 - And the mechanism you can speculate behind it is the same mechanism that pushes the sail forward.
  • fast_forward00:22:31 - Just by taking advantage of the pressure differences in the vorticity.
  • fast_forward00:22:39 - And what is the role of red or white in finding so sweet environments they like to be in is not known.
  • fast_forward00:22:49 - If you ask biologists, they can't say you have a karmic gating.
  • fast_forward00:22:53 - This is a phenomenon when you stay in regular turbulence and you flap your tail
  • fast_forward00:22:58 - periodically with the same frequency of vortex shedding, whether this is a passive
  • fast_forward00:23:03 - or an active behavior. Mm-hmm.
  • fast_forward00:23:06 - So we don't know. But now this result of the dead fish swimming upstream was
  • fast_forward00:23:13 - published a while back, no? It's not very recent.
  • fast_forward00:23:15 - No, it's not very recent. I think, I'm not sure, but I think it's 2004 or something.
  • fast_forward00:23:21 - So are you aware of anyone being able to replicate that?
  • fast_forward00:23:24 - I have no idea anybody has replicated that so far.
  • fast_forward00:23:28 - I haven't heard of anybody repeating this experiment. And I've had plans to
  • fast_forward00:23:34 - repeat them with robotic fish in my laboratory, but I haven't got so far yet.
  • fast_forward00:23:39 - But what we are speculating by looking at the results of our own research could
  • fast_forward00:23:46 - be that what really matters is very delicate interplay between the turbulence
  • fast_forward00:23:53 - and the properties of the body.
  • fast_forward00:23:55 - So what really matters probably is that if you happen to be of the right size
  • fast_forward00:23:59 - with the right floppiness, perhaps, and in the right spot, you know,
  • fast_forward00:24:04 - that creates this phenomenon.
  • fast_forward00:24:05 - Just you don't see very often dead fish swimming and this could be the reason
  • fast_forward00:24:09 - why. Right. Yes, exactly.
  • fast_forward00:24:11 - What is very powerful about this is that you really see that the morphology
  • fast_forward00:24:16 - itself is actually solving a heart
  • fast_forward00:24:19 - problem that we intuitively might have thought of as requiring a brain.
  • fast_forward00:24:25 - So, that means that the passive dynamics of the body is structured in a way.
  • fast_forward00:24:31 - That it can actually propel itself, given that the medium has certain properties.
  • fast_forward00:24:37 - The same phenomenon has been demonstrated on the ground with passive walkers.
  • fast_forward00:24:43 - You put a robot that doesn't have any sensing or has any actuation,
  • fast_forward00:24:47 - you put it on a ramp, and it just keeps walking down.
  • fast_forward00:24:51 - But it's the same thing there, that you have to have the
  • fast_forward00:24:53 - right slope of the ramp in order to make it
  • fast_forward00:24:56 - occur how often uh in in in
  • fast_forward00:24:59 - your life are you walking down the slope and only
  • fast_forward00:25:02 - down the slope so probably because you're not doing that and normally their
  • fast_forward00:25:07 - ground is rough so this is why you're also developed actuation sensing and i
  • fast_forward00:25:11 - think the same holds for fish but if you think of the ramp what is different
  • fast_forward00:25:15 - in when you're fluid is that you're in a microgravity environment or zero gravity
  • fast_forward00:25:20 - environment compared to what you what
  • fast_forward00:25:22 - you're feeling in a ground. What you're feeling on a ground is a gravity.
  • fast_forward00:25:25 - And in water, you almost don't sense gravity at all. And it kind of suggests
  • fast_forward00:25:30 - your embodiment has to also be very different.
  • fast_forward00:25:32 - And now you have a solid ground behind you. You're walking.
  • fast_forward00:25:37 - You're a passive walker. But I think when you're interacting with the flow, the flow is compliant.
  • fast_forward00:25:43 - So I think it's more like compares to walking on a trampoline rather than along the street.
  • fast_forward00:25:51 - And this is why I think also it's harder to demonstrate underwater.
  • fast_forward00:25:56 - Exactly but now so okay now what we have what you're also what we see now in
  • fast_forward00:26:02 - these biomimetic principles of control,
  • fast_forward00:26:05 - the body gives you already a lot of functionality
  • fast_forward00:26:08 - some people would call this a morphological computation although I find this
  • fast_forward00:26:12 - is a bit of difficult there's also a concept from embodied intelligence which
  • fast_forward00:26:16 - actually is a little wider but says the same that you You outsource some of
  • fast_forward00:26:21 - the control burden to your embodiment without being conscious about this. Right. So, but now…,
  • fast_forward00:26:28 - I'm a fish, I'm alive, I have a brain, so I still want to control my swimming, right?
  • fast_forward00:26:32 - I cannot just flop around in some vortex and see where I'm ending up.
  • fast_forward00:26:36 - I want to no turn left, right, go up or down, catch prey, what have you.
  • fast_forward00:26:42 - So I'm going to control that. So let's first look at propulsion.
  • fast_forward00:26:46 - So what do we know really about, let's say, how I control my swim speed?
  • fast_forward00:26:50 - That's very interesting research, which is done by John Long in Vassar College.
  • fast_forward00:26:56 - Some years ago, and he has very elegant experiments demonstrating what are the
  • fast_forward00:27:04 - first-order control parameters of the fish.
  • fast_forward00:27:08 - So what fish seems to control is tailbeat frequency and stiffness and amplitude.
  • fast_forward00:27:16 - And if you look further into that, swimming speed is a second-order control
  • fast_forward00:27:21 - parameter. You control your swimming speed by controlling your stiffness,
  • fast_forward00:27:26 - by controlling your tail peak frequency, for example.
  • fast_forward00:27:30 - And what biologists have also demonstrated is there is a very beautiful and
  • fast_forward00:27:33 - simple control law, which says that swimming speed and tail peak frequency are
  • fast_forward00:27:38 - linearly related to each other.
  • fast_forward00:27:42 - For example, different from a tail peak amplitude, which is a completely independent variable.
  • fast_forward00:27:51 - But this seems strange. I mean, you would expect if I have a bigger fin,
  • fast_forward00:27:57 - right, a bigger surface, a tail fin, then I would need less beats to generate
  • fast_forward00:28:03 - the same propulsion. No?
  • fast_forward00:28:06 - But now you seem to say it was independent of the size, but dependent on the frequency.
  • fast_forward00:28:13 - It depends on the frequency linearly. What it does with the amplitude is not
  • fast_forward00:28:17 - known. It's how does amplitude contribute.
  • fast_forward00:28:20 - So it do vary their amplitude.
  • fast_forward00:28:22 - But there is not like a beautiful, simple relationship between how they control
  • fast_forward00:28:29 - their amplitude and how they propel themselves forward.
  • fast_forward00:28:33 - Okay. So what do we know there in terms of fish behavior? Do they,
  • fast_forward00:28:37 - in some continuous fashion, control their beat frequency or do they have preferred
  • fast_forward00:28:42 - frequencies at which they beat their tail?
  • fast_forward00:28:45 - You always have a preferred frequency. And the frequency is a resonance frequency.
  • fast_forward00:28:50 - Because on resonance frequency, you're beating your tail with a maximal amplitude.
  • fast_forward00:28:56 - And what fish could do in order to control their amplitude then is not directly
  • fast_forward00:29:01 - necessarily to change the amplitude, but change the stiffness.
  • fast_forward00:29:06 - Exactly. If you change the stiffness, you get a different resonance frequency.
  • fast_forward00:29:09 - And by having different resonance frequency, you get different amplitude.
  • fast_forward00:29:13 - So when you want to have high frequencies, you're probably making yourself stiffer.
  • fast_forward00:29:17 - And having a stiffer body gives you higher amplitude and higher frequencies.
  • fast_forward00:29:23 - This seems to be what fish are doing. Another interesting thing is that if fish
  • fast_forward00:29:28 - are steadily swimming, and the steadily swimming is called the cruising speed
  • fast_forward00:29:33 - of a fish, which is about one or two body lengths per second.
  • fast_forward00:29:36 - So if you're a fish and you're swimming at a cruising speed,
  • fast_forward00:29:40 - you can go on forever and forever and forever. You even don't get tired almost.
  • fast_forward00:29:45 - And it's shown that fish use only very few muscles to do that.
  • fast_forward00:29:51 - Most of its body is completely relaxed, and it uses a few of the anterior muscles
  • fast_forward00:29:57 - to create motion and pass a traveling wave along the completely or almost completely
  • fast_forward00:30:03 - passive tail. So this is why they don't get tired.
  • fast_forward00:30:07 - They're so good energy optimizers. That's impressive. It's something like when
  • fast_forward00:30:11 - you go and you run a marathon, you don't keep your legs stiff.
  • fast_forward00:30:17 - You're very relaxed, aren't you? Just because you want to conserve energy.
  • fast_forward00:30:21 - To have all these 42 kilometers going. But if you're a sprinter,
  • fast_forward00:30:25 - you do exactly the opposite.
  • fast_forward00:30:26 - Not exactly the opposite, but you use much more muscles in order to go very
  • fast_forward00:30:32 - fast in a long period of time.
  • fast_forward00:30:33 - Right, exactly. And fish do the same thing. They use their other muscles,
  • fast_forward00:30:37 - white muscles, when they have to do something very fast.
  • fast_forward00:30:40 - Saves their lives, get a prey. So this is for rapid turns, bursts,
  • fast_forward00:30:45 - acceleration, decelerations.
  • fast_forward00:30:47 - And they use it very seldom, only when they have to,
  • fast_forward00:30:51 - you because like a sprinter you get terribly tired of
  • fast_forward00:30:54 - it sure but now so i'm a fish i'm
  • fast_forward00:30:57 - flapping my my tail fin but i probably have
  • fast_forward00:30:59 - some oscillator sitting in my brain somewhere that's driving this
  • fast_forward00:31:02 - right and but now this oscillator is driving my the tail the the tail but i'm
  • fast_forward00:31:10 - i'm varying i'm varying the stiffness of my body in the meantime as well right
  • fast_forward00:31:15 - so um so then the question becomes,
  • fast_forward00:31:20 - should I control this oscillator that drives?
  • fast_forward00:31:26 - How do I control the fin, the tail fin oscillator relative to the stiffness
  • fast_forward00:31:32 - control I'm performing? What's the scaling there?
  • fast_forward00:31:36 - Yeah, I can answer you. I have no idea.
  • fast_forward00:31:39 - Okay. But you agree, no? It would be a pretty important relationship.
  • fast_forward00:31:44 - Absolutely. To manage. Absolutely. Absolutely.
  • fast_forward00:31:47 - Okay. Okay, because you could basically just break your muscles or the motor plant in any other way.
  • fast_forward00:31:53 - Absolutely, but I have no idea why they choose to control one parameter or the other.
  • fast_forward00:31:59 - So it's hard to ask fish, right? We can try, exactly. Okay, so now we know that...
  • fast_forward00:32:07 - How we're swimming, so we flap our tail to the frequencies, we control stiffness,
  • fast_forward00:32:14 - but then how does the sensing feed into that, right?
  • fast_forward00:32:19 - So, in some sense, I could then argue, well, I don't need much sensing to achieve that, right?
  • fast_forward00:32:25 - Well, if you have to control your swimming speed, so you probably,
  • fast_forward00:32:32 - what you do is like when you walk, you're walking with respect to some global reference frame.
  • fast_forward00:32:37 - So from this point of view, I don't know whether flow sensing is significant
  • fast_forward00:32:44 - here or not, because flow is also flowing, right?
  • fast_forward00:32:47 - Sure. So try to swim in a river and understand where you end up.
  • fast_forward00:32:51 - I don't know whether you ever got out swimming from a boat, and you're there
  • fast_forward00:32:56 - in a flow, and the boat is here, and you're swimming, and you kind of feel you're
  • fast_forward00:33:00 - staying still, or you're swimming in one direction.
  • fast_forward00:33:03 - And then you turn around and say, oh, where is the boat?
  • fast_forward00:33:06 - Oh, it's far, far away somewhere already. And you couldn't understand you're
  • fast_forward00:33:10 - being carried away with the flow. Right.
  • fast_forward00:33:13 - And probably fish have the same problem. I think they have the same problem.
  • fast_forward00:33:17 - Yes. What you feel in swimming, you can just extrapolate from when you're swimming, you're feeling track.
  • fast_forward00:33:28 - But the funny thing with the track is that, I don't know whether you ever thought
  • fast_forward00:33:33 - about it, but we humans don't have a track sensor.
  • fast_forward00:33:36 - True. Right? And we don't have a speed sensor.
  • fast_forward00:33:39 - Sensor so if you're sitting in a
  • fast_forward00:33:42 - car and you're driving very very fast way
  • fast_forward00:33:45 - too fast on a very good road which is sometimes too when
  • fast_forward00:33:48 - you have a very good car uh you're not
  • fast_forward00:33:51 - feeling anything you're not feeling the speed you could feel
  • fast_forward00:33:54 - if you close your eyes you could feel that you're almost still but what you're
  • fast_forward00:33:57 - feeling is acceleration try to drive a
  • fast_forward00:34:00 - car on a bumpy road on bad road and you're starting
  • fast_forward00:34:03 - to pump and your inner ear is going to react and you're
  • fast_forward00:34:06 - feeling the speed suddenly so uh why
  • fast_forward00:34:10 - do you know that you're going fast if you're going yourself not
  • fast_forward00:34:12 - by the car is that you're getting tired and this
  • fast_forward00:34:16 - is probably what fish is feeling when it swims against the stream either slowly
  • fast_forward00:34:21 - or or fast it's getting tired and it seems that the cruising speed of one two
  • fast_forward00:34:28 - body lengths is something that makes them least tired this is why they choose this sort of,
  • fast_forward00:34:34 - But could you imagine that the fish also adjusts its body stiffness relative
  • fast_forward00:34:38 - to the turbulence it's in?
  • fast_forward00:34:40 - Absolutely, I think so. Right. Because that's what also conserves a lot of energy, right? Absolutely.
  • fast_forward00:34:46 - To be more compliant with respect to the medium. Yeah.
  • fast_forward00:34:50 - But the other thing that was interesting in your data analysis is that you show
  • fast_forward00:34:56 - that in these relationships between, for instance, speed and tail fin beats,
  • fast_forward00:35:02 - that that relationship is essentially linear.
  • fast_forward00:35:05 - Do you think that's a coincidence? Is that just by accident,
  • fast_forward00:35:08 - or is there some biomimetic trick behind this?
  • fast_forward00:35:14 - I don't know. I'm almost fascinated about that. You know, there are 30,000 species of fish in the world.
  • fast_forward00:35:21 - And as far as I know, people haven't found a contrary example.
  • fast_forward00:35:25 - Why this law doesn't tell. So it's hard to believe for me, because I've always
  • fast_forward00:35:30 - thought that such a general law only holds in physics.
  • fast_forward00:35:34 - Because physics is physics. Like it's an unconscious world and it can't change in certain ways.
  • fast_forward00:35:42 - But biology is normally, it's like, you know, biology is always imprecise and
  • fast_forward00:35:47 - it's slippery and stinky and it can't be that beautiful.
  • fast_forward00:35:51 - Exactly. But actually there are also other laws in biology, more or less,
  • fast_forward00:35:57 - all statistical laws actually. In physics, you have precise laws.
  • fast_forward00:36:00 - In biology, you have statistical laws. But within the reasonable statistical
  • fast_forward00:36:05 - interval, these laws all hold.
  • fast_forward00:36:09 - And why is this like this? I don't know, but I'm very happy for that.
  • fast_forward00:36:13 - Because if I'm a control engineer, you know, there is nothing better for a control
  • fast_forward00:36:18 - engineer than a linear control law. Exactly.
  • fast_forward00:36:22 - Although maybe the precision you might find in physics is a bit overrated, right?
  • fast_forward00:36:26 - If you go to… Oh, yeah, yeah. So if you go down to, let's say,
  • fast_forward00:36:29 - the quantum level, then suddenly things become all probabilistic as well.
  • fast_forward00:36:32 - Absolutely. You have measurement errors and all these sort of things. But now…,
  • fast_forward00:36:37 - So now we've understood a lot about fish swimming, and subsequently you want
  • fast_forward00:36:42 - to build your robot to swim.
  • fast_forward00:36:44 - So how is that translation step? How do you do that? How do we go from this
  • fast_forward00:36:48 - now, our understanding of fish swimming, to actually a fish robot in a tank swimming?
  • fast_forward00:36:55 - We made many observations and tests from biology.
  • fast_forward00:36:59 - As you pointed out now, the most recent one is a linear control law,
  • fast_forward00:37:03 - which shows it also works for the fish. So we get a beautiful,
  • fast_forward00:37:07 - easy way to control our fish swimming speed.
  • fast_forward00:37:11 - And other things that we get from biology is the knowledge that if you swim
  • fast_forward00:37:17 - on cruising speeds, you only use the anterior muscles of your body.
  • fast_forward00:37:21 - What does it mean? It means that the rest of your body is just a passive carrier
  • fast_forward00:37:25 - of the traveling wave that passes energy onto the water.
  • fast_forward00:37:29 - And why it's very important from a technological point of view.
  • fast_forward00:37:33 - As we started our discussion with distributed actuation, which is very hard
  • fast_forward00:37:39 - to copy with the current technology we have, because we have rotating motors
  • fast_forward00:37:43 - that are very big and clumsy.
  • fast_forward00:37:46 - And what does it mean is that I can do a fish that has a single point of actuation only.
  • fast_forward00:37:52 - And this gives me a very reductionist approach.
  • fast_forward00:37:57 - So now, of course, everything depends on what my fitness function is.
  • fast_forward00:38:01 - If I don't build a robot that has to fight for its survival,
  • fast_forward00:38:06 - then I'm very happy with a single-point actuation.
  • fast_forward00:38:09 - But if I want to do a robot that can turn around rapidly, that some researchers
  • fast_forward00:38:13 - also in robotics are investigating, then probably this approach doesn't hold.
  • fast_forward00:38:17 - It has its limits. But in this case, I'm very happy that I, from biologists,
  • fast_forward00:38:23 - I derived a very reductionist approach that makes me able to develop simple robots.
  • fast_forward00:38:30 - But simple means normally reliable and cheap.
  • fast_forward00:38:33 - And these are two things that people appreciate in engineering.
  • fast_forward00:38:39 - Yes. But now tell me how you...
  • fast_forward00:38:42 - Practically built as a robot. What's the material you use for these fish, the robot fish?
  • fast_forward00:38:47 - We use silicons. We use all sorts of silicons because we have a technology how
  • fast_forward00:38:52 - we can vary the stiffness.
  • fast_forward00:38:53 - And we can manufacture tails or whatever body parts you need with whatever elasticity
  • fast_forward00:39:01 - and viscoelasticity you desire.
  • fast_forward00:39:03 - And by that we can also find the right relationship between stiffness of the
  • fast_forward00:39:11 - materials and the actuation momentums that we apply to the material.
  • fast_forward00:39:16 - And then actuation, how is that done? Oh, it's done with a simple servo motor.
  • fast_forward00:39:20 - There is fancier technologies, of course, with electroactive polymers,
  • fast_forward00:39:24 - artificial muscles and everything.
  • fast_forward00:39:26 - But if you wanted to do something cheap and reliable that people would buy in
  • fast_forward00:39:29 - the end. So maybe it's a right thing to do is to go for the conventional technology first.
  • fast_forward00:39:35 - So you have a name for this design methodology you pursued and you called it KISS.
  • fast_forward00:39:43 - Well, KISS is a very general, a well-known design methodology in all engineering,
  • fast_forward00:39:50 - which means keep it simple stupid.
  • fast_forward00:39:52 - And I think most of people who come from engineering know what KISS means,
  • fast_forward00:39:57 - especially software developments, where your software developers.
  • fast_forward00:40:02 - The code gets out of your hands so fast that if you're not taking a KISS approach,
  • fast_forward00:40:08 - you're doomed very soon.
  • fast_forward00:40:10 - And, yeah, we take this approach because I believe that from all the things
  • fast_forward00:40:18 - you can copy from biology,
  • fast_forward00:40:20 - you have to copy only the most important, most relevant things to get some things
  • fast_forward00:40:27 - that people can use later on.
  • fast_forward00:40:28 - And which is easy to make simple, reliable, and cheap.
  • fast_forward00:40:32 - But on the other hand, KISS methodology, I think, is also relevant in science.
  • fast_forward00:40:38 - Because if you want to keep things simple, it helps you this kind of approach.
  • fast_forward00:40:49 - Helps you to understand what is the importance of one or the other factor in
  • fast_forward00:40:55 - the phenomenon that you are investigating.
  • fast_forward00:40:56 - Complicating for example you take off the lateral line
  • fast_forward00:40:59 - sensing and you ask what happens if i take it off
  • fast_forward00:41:02 - i make my system very simple i take all sensors off what
  • fast_forward00:41:05 - can i still do with the system without any sensors at all what can i do with
  • fast_forward00:41:09 - a dead fish it's a very dead vicious simple right and then you establish well
  • fast_forward00:41:13 - certain things i still can do so i don't need a complicated system and this
  • fast_forward00:41:18 - gives me understanding what is actually the role of passive dynamics in a biological system.
  • fast_forward00:41:24 - Okay, now I put actuation in and ask, what can I do if I have an actuator?
  • fast_forward00:41:29 - And I establish what is this relevant for? And so on and so on.
  • fast_forward00:41:34 - And by just, you know, step by step complicating my system and controlling all
  • fast_forward00:41:39 - the parameters taking one or the other off, I can make generalizations.
  • fast_forward00:41:45 - But now, in some sense, I could also argue that what you're proposing is is
  • fast_forward00:41:51 - maybe also the opposite of KISS, right?
  • fast_forward00:41:56 - Because if you look at the outcome of this, you're saying, look,
  • fast_forward00:41:59 - if we want to understand fish swimming,
  • fast_forward00:42:01 - we have to actually now look at a much larger picture because suddenly we have
  • fast_forward00:42:05 - to include the morphology, we have to think about the actuation,
  • fast_forward00:42:09 - the sensing, or the sensing might be partially removed but not completely.
  • fast_forward00:42:13 - But, well, in a more traditional view, I could say, well, I have a more compartmentalized
  • fast_forward00:42:18 - view on how a certain function is realized and have clear defined modules.
  • fast_forward00:42:23 - They're well described with clear interfaces between them so I can understand what I'm doing.
  • fast_forward00:42:28 - While you are turning all that upside down now because you're saying, well, actually,
  • fast_forward00:42:33 - if the body itself can already swim and the actuation is actually modulating
  • fast_forward00:42:37 - these properties of the body and then the brain must be sort of modulating again
  • fast_forward00:42:42 - these properties of the actuation and the motor plant and so on.
  • fast_forward00:42:45 - So how is it actually simple? It also can be a complexification.
  • fast_forward00:42:50 - What you're describing here is a very fine, decent, respectable engineering method.
  • fast_forward00:42:56 - You take some modules that exist already and you build a new equipment. That's fine with that.
  • fast_forward00:43:02 - What I try to do is to create new models.
  • fast_forward00:43:06 - In engineering, you don't have a module for an embodiment.
  • fast_forward00:43:10 - So I'm not building a fish. I'm creating a method for building a fish.
  • fast_forward00:43:16 - Yeah, but the point then is...
  • fast_forward00:43:19 - You will have no more boxes if you want in that method, right? I invent new boxes.
  • fast_forward00:43:24 - Yeah, but what's your box then? Do you have a box morphology?
  • fast_forward00:43:27 - Yeah, I think I do have a box morphology.
  • fast_forward00:43:31 - And even within this box, I know already how to, if you tell me that my body
  • fast_forward00:43:37 - has to be with a certain viscoelasticity, I also have even,
  • fast_forward00:43:41 - well, I'm going to have engineering methods how you fabricate materials like this.
  • fast_forward00:43:47 - Okay. But then between the boxes, the interface between the boxes now also become constraints, right?
  • fast_forward00:43:52 - It's not only control signals or sensory signals that are exchanged between
  • fast_forward00:43:57 - these systems. Now these are also constraints.
  • fast_forward00:44:00 - For instance, my body has a certain stiffness.
  • fast_forward00:44:03 - Oh, yeah, but everybody has a certain stiffness. It's always been a constraint.
  • fast_forward00:44:07 - No, but I mean, this is not anymore an explicit control signal you exchange, right?
  • fast_forward00:44:12 - It becomes more a constraint on the control signals you might receive.
  • fast_forward00:44:16 - Yeah, but I'm not very worried of that because every system has constraints.
  • fast_forward00:44:22 - Every physical system has boundary conditions and constraints.
  • fast_forward00:44:25 - So you just have to establish them and build your control on top of that.
  • fast_forward00:44:29 - You're right, it can be complicated, especially when it comes to viscoelasticity.
  • fast_forward00:44:34 - People talk about soft robotics, but they talk about elastic robots.
  • fast_forward00:44:38 - They don't talk about viscoelastic robots. That's right.
  • fast_forward00:44:41 - Why they don't talk about viscoelastic robots? Because viscoelasticity is a
  • fast_forward00:44:47 - real pain for post-physicists and engineers to describe, to analyze,
  • fast_forward00:44:53 - and we're not even talking here about control because I don't know any viscoelastic
  • fast_forward00:44:58 - robots that people control. Right.
  • fast_forward00:45:01 - So if we go from your biomimetics to, let's say, this KISS methodology you're proposing,
  • fast_forward00:45:10 - do you see this as, let's say, a real paradigm shift in engineering or is this
  • fast_forward00:45:16 - more of an elaboration of the standard practice?
  • fast_forward00:45:22 - Well, I am ambitious enough to say it's a paradigm shift, of course,
  • fast_forward00:45:28 - but on the other hand, I know people have tried it in different fields quite a lot.
  • fast_forward00:45:36 - If you take a clever biomimetic approach, this is exactly the questions what you're asking.
  • fast_forward00:45:42 - What features are relevant that I should copy? So people have done it maybe
  • fast_forward00:45:47 - not so consciously, but there are many fine examples probably around already
  • fast_forward00:45:52 - where people in one way or other are using the same approach.
  • fast_forward00:45:57 - Right. But you are implying there's also a not so clever biomimetic approach.
  • fast_forward00:46:01 - Yeah, yeah, yeah.
  • fast_forward00:46:04 - Straightforward. So now tell me, what's your prediction with respect to the
  • fast_forward00:46:11 - viability of this approach?
  • fast_forward00:46:12 - When will we see the first artificial robot fish swim around the world,
  • fast_forward00:46:16 - let's say, autonomously?
  • fast_forward00:46:19 - You know, swim around the world autonomously actually doesn't depend exactly.
  • fast_forward00:46:24 - I believe it doesn't depend on the technology I am developing.
  • fast_forward00:46:30 - It depends on the technology of power sources.
  • fast_forward00:46:35 - But we imagine we have it solved, we live off plankton, we have a plankton driven robot fish.
  • fast_forward00:46:43 - Suppose we have a power source that lasts forever, so it's actually a very interesting
  • fast_forward00:46:47 - test to do and it's a great idea actually to have a grand challenge of robotic
  • fast_forward00:46:52 - fish swimming. Exactly, around the world.
  • fast_forward00:46:54 - Instead of overseas or somewhere. Exactly. It's actually a great thing to do.
  • fast_forward00:46:57 - Okay. I have to say is that our fish breaks down about every second day or so when we test it.
  • fast_forward00:47:03 - So we're very far from it at the moment. No, but what are you expecting?
  • fast_forward00:47:08 - So it's interesting, right? The power is still the biggest problem.
  • fast_forward00:47:11 - That's also why robots won't conquer the world anytime soon. Right.
  • fast_forward00:47:16 - But what's your expectation with respect to just the control of the swimming, for instance?
  • fast_forward00:47:22 - So when do you think we can really have mature systems that will not propel
  • fast_forward00:47:27 - themselves anymore with propellers as we know them, but really with fish-like
  • fast_forward00:47:31 - bodies that are really swimming?
  • fast_forward00:47:33 - Maybe I'm overconfident now here, but I think my beautiful fish would be definitely
  • fast_forward00:47:39 - able to swim overseas unless it gets eaten or something.
  • fast_forward00:47:44 - Okay. If I had to find manufacturing technology behind it, because what it really
  • fast_forward00:47:50 - comes to is the next step in biomimetics.
  • fast_forward00:47:54 - First you do your engineering, you develop new engineering approaches.
  • fast_forward00:47:58 - Then you apply those engineering approaches. But then it comes to manufacturing, right?
  • fast_forward00:48:05 - And manufacturing is a very important part of underwater robotics.
  • fast_forward00:48:11 - So the biggest problem in underwater robotics… It's water. Sure.
  • fast_forward00:48:15 - And the biggest problem is how to keep water out of the robot.
  • fast_forward00:48:22 - And what we can demonstrate is with commercial underwater vehicles,
  • fast_forward00:48:28 - where they manage to do it rather well.
  • fast_forward00:48:31 - So I would say that if I had good manufacturing technology, my fish and power
  • fast_forward00:48:39 - source would last forever. My fish would definitely be able to swim overseas.
  • fast_forward00:48:45 - Another problem is how long does it take? Exactly.
  • fast_forward00:48:50 - And another problem is where does it end up? Exactly. Like a letter in a bottle, no? Exactly.
  • fast_forward00:48:57 - It's an expensive bottle. Right. But now, so to finish up, two last questions.
  • fast_forward00:49:02 - So, Mariam, you're in this business for quite some time. you build up this your,
  • fast_forward00:49:09 - biorobotics lab in Estonia so.
  • fast_forward00:49:14 - What would be the one law of Maria that you would like us to adhere to in our
  • fast_forward00:49:19 - biomimetic exploration of the universe?
  • fast_forward00:49:22 - Maria's law, what is it? Oh, now you're going to ask something about,
  • fast_forward00:49:26 - like, does God exist or something. This is a so general question.
  • fast_forward00:49:30 - I don't have a single law. So I want to be very careful with it.
  • fast_forward00:49:33 - All my gifts approach for the fish works fine. I'm very happy with that.
  • fast_forward00:49:39 - But I didn't want to make grand claims.
  • fast_forward00:49:41 - Something like, this is generalizable to everything. Take this method and apply
  • fast_forward00:49:45 - to insects or apply them to other animals because then it comes,
  • fast_forward00:49:49 - I think what happens and it becomes very general.
  • fast_forward00:49:52 - And a very, very general law is applicable to everything, but it lacks details.
  • fast_forward00:50:01 - So it's like I can give you a law, but this law is like, you know,
  • fast_forward00:50:06 - I can also give you a law for playing piano. You know, the law for playing piano
  • fast_forward00:50:10 - is that you have to press the right key with the right finger at the right time.
  • fast_forward00:50:14 - And you always can play your pianos like that. But it doesn't help you really
  • fast_forward00:50:20 - to play anything, right?
  • fast_forward00:50:22 - So what I'm afraid of is to giving a similar very general suggestion like, you know, be happy.
  • fast_forward00:50:34 - Don't worry, be happy or something like this. But since you're not a professional
  • fast_forward00:50:38 - piano player, I can challenge you on this.
  • fast_forward00:50:40 - So your KISS methodology could be an example of a principle or a law you would propose.
  • fast_forward00:50:48 - Or what you tell me now, you're saying, look, all paradigms have limitations.
  • fast_forward00:50:53 - This would be another potential law. So in that sense, it's not you have no laws, right?
  • fast_forward00:50:57 - So we just have to agree on which one is the one today, right?
  • fast_forward00:51:03 - So what would be the one today? day i'm still
  • fast_forward00:51:06 - afraid of speaking trivialities if i say
  • fast_forward00:51:10 - that yes hello you have to do in biomimetics is
  • fast_forward00:51:13 - keep it seems so stupid it's just it's a little bit
  • fast_forward00:51:16 - of a triviality but maybe one thing if i if i'm now thinking that i'm a biomimetic
  • fast_forward00:51:21 - engineer and i want to have my problem which is something different maybe it's
  • fast_forward00:51:25 - insect flight or something so maybe really what i would do is is like a physicist
  • fast_forward00:51:31 - approach with taking there that we have a phenomenon And then we make a controlled experiment.
  • fast_forward00:51:36 - We try to knock out some phenomena and investigate what is the impact to the
  • fast_forward00:51:42 - other, to the performance of the system, and to apply this law within our box
  • fast_forward00:51:47 - of biomimetics. But, of course...
  • fast_forward00:51:51 - It's very hard to predict how scalable it is because the phenomena are dependent
  • fast_forward00:51:56 - on each other and your measurements are imprecise.
  • fast_forward00:51:59 - You're maybe not careful with some design parameters and so on.
  • fast_forward00:52:05 - But also, on the other hand, still physicists have the same problem,
  • fast_forward00:52:08 - right? Experimental physicists.
  • fast_forward00:52:10 - They don't know how the parameters depend on each other and they're still doing it.
  • fast_forward00:52:14 - That's why they do experiments, right? Yeah, exactly. Exactly.
  • fast_forward00:52:16 - So I think this experimental methodology,
  • fast_forward00:52:18 - if you want to have a key message, like what's the meaning of life or something,
  • fast_forward00:52:22 - then it would be to have a controlled experiment like this to establish a relative
  • fast_forward00:52:27 - importance of the phenomena to your system regarding your fitness function that you're aiming to.
  • fast_forward00:52:35 - Okay. So keep on experimenting. Yeah. That's the law. Yeah, it turned out to be awfully trivial.
  • fast_forward00:52:42 - Now we got the law. And then last one is a prediction. So if I go visit you
  • fast_forward00:52:47 - five years from now, and I'm going to say, look, five years back,
  • fast_forward00:52:51 - you made this one prediction.
  • fast_forward00:52:52 - Today, I'm checking with you whether it came true.
  • fast_forward00:52:55 - What is one prediction you would be willing to make today you're most enthusiastic
  • fast_forward00:52:59 - about, you feel most committed to right now?
  • fast_forward00:53:02 - Within biomimetics, you mean? Sure, within the envelope of our current discussion.
  • fast_forward00:53:11 - I think there are some very, if you look at technology, there are some very
  • fast_forward00:53:17 - general trends that are going on.
  • fast_forward00:53:20 - One thing is that technology is robotic technology.
  • fast_forward00:53:24 - If we talk about parametric robots, it's robots are moving closer to humans.
  • fast_forward00:53:29 - So we're moving from industry to service, which means that they have to be safer.
  • fast_forward00:53:34 - And one way to make them safer is to make them soft. That's just one solution
  • fast_forward00:53:38 - to making some safeguards. There are others, of course, learning methods and everything.
  • fast_forward00:53:42 - But one thing you can do is to make robots soft.
  • fast_forward00:53:47 - And, of course, there are also trends of miniaturization.
  • fast_forward00:53:52 - Happening at the same time and i'd
  • fast_forward00:53:57 - say it it's maybe going to taught
  • fast_forward00:54:01 - simplification in one in in some sense
  • fast_forward00:54:04 - that said in traditional robotics where so very much emphasis have been on a
  • fast_forward00:54:09 - control fine beautiful control of very complicated systems i think the prediction
  • fast_forward00:54:15 - is really that people are going to explore rather how to keep the control minimal
  • fast_forward00:54:20 - to make robots that are safe and robust.
  • fast_forward00:54:26 - Independent on whether your control laws are exactly correct or not.
  • fast_forward00:54:30 - Will we have those in five years? Is that what you're saying?
  • fast_forward00:54:33 - Five years, we'll have more safe and robust robots?
  • fast_forward00:54:37 - I think we definitely have more safe and robust robots. And I think also they're
  • fast_forward00:54:41 - going to be compliant because what you can see is an increasing trend.
  • fast_forward00:54:46 - More and more research papers are written on compliant robots.
  • fast_forward00:54:50 - They are now more restricted to compliant robot arms for very obvious reasons,
  • fast_forward00:54:56 - but I think it's also moving to other fields of the research.
  • fast_forward00:55:00 - So I think the next step then with compliant robots, we have a good theory for
  • fast_forward00:55:04 - making robots with a changing elasticity, changing stiffness.
  • fast_forward00:55:08 - And we understand also a theoretical background of it quite well.
  • fast_forward00:55:12 - But one thing that we have trouble understanding is viscoelasticity.
  • fast_forward00:55:17 - Well, most of the bodies are viscoelastic, and they are set for particular reasons.
  • fast_forward00:55:26 - It is that if you have a viscoelastic body, you dissipate energy.
  • fast_forward00:55:29 - When you dissipate energy, you filter
  • fast_forward00:55:32 - out disturbances, and filtering out disturbances is an important thing.
  • fast_forward00:55:37 - Right. So I think where it is going in five years, you're going also to see
  • fast_forward00:55:42 - research on this sort of embodied signal processing.
  • fast_forward00:55:47 - Okay, that's a good one. All right, I come back to you and check.
  • fast_forward00:55:51 - Well, yeah, it's a self-fulfilling prediction because I'm going to research
  • fast_forward00:55:55 - on it anyway. So you come back in five years, I fulfilled my prediction.
  • fast_forward00:55:59 - Okay, Marcia Kruzma, thank you very much for this conversation. Thank you.
  • fast_forward00:56:05 - The csn podcast was produced by the convergent science network of biometrics
  • fast_forward00:56:12 - and biohybrid systems a project funded by the european seventh research framework program,
  • fast_forward00:56:19 - for more interviews recorded lectures or upcoming conferences in the field of
  • fast_forward00:56:25 - biometrics and bio-hybrid systems, go to csnnetwork.eu.
  • fast_forward00:56:31 - Music.
  • fast_forward00:56:31 - And thank you for listening.

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