To explore the neural control mechanisms involved in the evolution of terrestrial locomotion from aquatic vertebrates, Auke Ijspeert, associate professor and head of the BioRobotics Laboratory at École Polytechnique Fédérale de Lausanne, and his colleagues built a swimming and walking salamander robot in 2007.[169] The robot was programmed to walk and swim using neural chains of central pattern generators, which produce rhythmic activation of local body and limb motors. With a simple linear increase in the stimulation of the artificial nervous system, the robot transitioned smoothly from land to water, switching from standing to traveling body waves. Because the behavioral match and mechanistic accuracy of the robotic salamander are both high in terms of locomotion and nervous activation, respectively, this ET provides a plausible model for the evolution of the neural control of walking in vertebrates.
FIGURE 7.10. Robot Madeleine as the first Transphibian, an amphibious surf-zone vehicle. Maddie uses large flippers that rotate unidirectionally on land, like wheels, to move from the beach into the water. Once in the water, she switches to fin-flapping mode. Transphibians are commercially available from iRobot, Inc., which purchased Nekton Research, LLC, in 2008. Photo is courtesy of Brett Hobson.
After all of our efforts to understand the evolution of early vertebrates and vertebrae, you won’t be surprised to know that we have an ET project underway at Vassar to help us out. To explore the morphospace of just the number of vertebrae, we’ve built MARMT (Mobile Autonomous Robot for Mechanical Testing), a swimming robot into which we can put propulsive tails with biomimetic vertebral columns. MARMT is another group project, with Jon Hirokawa, Sonia Roberts, Nicole Krenitsky, Carina Frias, Josh de Leeuw, and Marianne Porter all making important contributions to this Evolutionary Trekker. Rather than letting evolution create our variations, we simply vary the number of vertebrae from zero to eleven, keeping everything else the same, including the tail span. We then program MARMT to either swim steadily, using a variety of tail-beat frequencies, or to escape. MARMT accelerates faster and swims more rapidly with the stiffer tail that higher numbers of vertebrae produce.[170]
With Evolutionary Trekkers we learn how behavior varies in regions of morphospace (1) no longer occupied by living species, (2) never occupied by species, and/or (3) not visited by evolving robots. There you go, and there you are!
Chapter 8
SO LONG, AND THANKS FOR ALL THE ROBOTIC FISH[171]
I’VE SEEN THE FUTURE, AND IT’S FULL OF FISH. NOT REAL FISH, of course. We’ve eaten most of those. I mean the robotic kind. Robotic fish are taking the place of the real ones. At the London Aquarium in 2005 you could have seen three bejeweled robotic fish, built by Professor Huosheng Hu and his team at Essex University, swimming on display. A swimming coelacanth, built by Mitsubishi Heavy Industries, made news in 2001 by making the rounds in the AquaTom in the Fukui Prefecture.[172] The French company RobotSwim has their new Jessiko autonomous robotic fish, ready for your swimming pool, local aquarium, or exploration and marine-monitoring mission.[173] Maurizio Porfiri, an engineering professor at the Polytechnic Institute at New York University, is building a robotic fish to herd real fish away from danger.[174] His NYU colleague, Professor Farshad Khorrami, has created a company, FarCo Technologies, that builds biomimetic robotic fish for industrial and defense-related situations.[175]
Why all the fuss about robotic fish? What’s in it for you and me? Will a robotic fish become your best friend, save your life, or overthrow an evil dictator? Maybe. For certain, robotic fish will help us do what we can’t naturally do: be underwater. As extensions of our hands and eyes, robotic fish are embodied-brain tools we primates will use to probe the aquatic depths. And as we’ve seen throughout this book, robotic fish are built with representations of bits and pieces of real fish, sometimes to learn more about fish, if you are a crazy biological cognitive scientist, or, most of the time, to build better machines, if you are an engineer with a job to do.
We all fall prey to the notion that evolution by natural selection is a better engineer than a hominid with a PhD from MIT. The implicit basis for this romantic view of nature as an engineer is that evolution perfects: “Evolution is a slow but sure process of perfecting design to give a life-form a natural advantage in a competitive environment.”[176] Steve Vogel, in his book Cats’ Paws and Catapults, explains the counterargument to this perfection bias beautifully: “Nature does what she does very well indeed. But—and here’s the rub—why should she do so in the best possible way?”[177] Indeed.
Think about PreyRo. Even when that population of Evolvabots appears to have evolved a mechanically optimum tail stiffness, that doesn’t mean that an average of 5.7 is the perfect solution forever and for everywhere. It’s only the best solution—relative to others in the population—at that moment and place in the adaptive landscape. As we talked about in the last chapter, the adaptive landscape usually contains multiple peaks. In the face of such rugged terrain the best that evolution can hope to do is find the closest peak, the “local maximum” in the mountain lore of mathematicians. Even with evolution by natural selection in full hill-climbing mode, it can be pushed off course by random forces and prevented from getting underway by the historical constraints of the population’s genetic history.
Instead, evolution suffices. It may provide just-good-enough solutions that aren’t quite in time, but it doesn’t even have to do that. (Nice work if you can get it, eh?) Selection, that judgeless judging environment with which an individual ceaselessly and unknowingly interacts, plays a strong role in choosing the breeders who make the next generation. But by the time the next generation is on the scene, the world may have changed, creating a new adaptive landscape that the previous, and different, selection environment could not anticipate. Nothing in the rule book for the game of life says that the playing field has to be level or even has to stay the same. In fact, except in unusual places such as the abyssal zone at the bottom of the ocean, the adaptive landscape for any population is better thought of as an “adaptive seascape,” as suggested by Professor David Merrill in his eponymous book.
Given all of this complexity and contingency in the adaptive seascape, do we really want to assume that we can look to the living world to have solved all of our engineering problems and to have done so perfectly?
No, we don’t. But neither do we want to pretend that we have nothing to learn from nature. Engineers understand this, and they understand that they need more in their toolbox than just the dry goods—stiff steel, flexible plastic, compressive concrete, and resilient rubber—with which we’ve built the constructed world around us. Phil Leduc, associate professor of mechanical engineering at Carnegie Mellon University, works as a nanoengineer, manipulating proteins within living cells, linking the mechanical behavior of one to the biological function of the other in order to design bioinspired nanofactories. Kenneth Breuer, professor of engineering and director of the Fluid Dynamics Laboratory at Brown University, collaborates with Sharon Swartz, professor of biology at Brown University, to study the complex anatomy and behavior of flying bats and their extremely flexible wings as part of a larger project with engineers from the University of Michigan to build to a bat-inspired micro-air vehicle for the US Air Force Research Laboratory. Melina Hale, associate professor of organismal biology and anatomy at the University of Chicago and a fish expert working on robotic fish, offers this insight: “with all the tremendous work that has gone into designing and building robots, we are still far from having one that functions as well as a fish.”[178] It holds for any bioinspired project.
169
Auke Jan Ijspeert, Alessandro Crespi, Dimitri Ryczko, and Jean-Marie Cabelguen, “From Swimming to Walking: Is a Salamander Robot Driven by a Spinal Cord Model?”
170
You can read more about MARMT in J. H. Long Jr., N. Krenitsky, S. Roberts, J. Hirokawa, J. de Leeuw, and M. E. Porter, “Testing Biomimetic Structures in Bioinspired Robots: How Vertebrae Control the Stiffness of the Body and the Behavior of Fish-like Swimmers,”
171
Where would we be without Douglas Adams? This chapter title is an homage to the fourth book in his
174
Reported by the
175
Full disclosure here: I have been and currently am collaborating with Farshad and FarCo Technologies. However, I hold no financial stake in FarCo Technologies (www.farcotech.com/).
176
P. R. Bandyopadhyay, “Swimming and Flying in Nature—The Route Toward Applications: The Freeman Scholar Lecture,”
177
Steven Vogel,