Artificial muscle computer performs as a universal Turing machine

Artificial muscle computer performs as a universal Turing machine
An illustration of Wolfram’s “2, 3” Turing machine, the simplest known universal Turing machine that can solve any computable problem. A machine head reads the tape, decides what to do based on the data it sees plus its internal state (1 or 0), and then write the data and moves a step left or right. The researchers here realized this Turing machine using artificial muscles to help perform logic functions and memory functions. Credit: O’Brien and Anderson. ©2013 American Institute of Physics

( —In 1936, Alan Turing showed that all computers are simply manifestations of an underlying logical architecture, no matter what materials they're made of. Although most of the computer's we're familiar with are made of silicon semiconductors, other computers have been made of DNA, light, legos, paper, and many other unconventional materials.

Now in a new study, scientists have built a computer made of artificial muscles that are themselves made of electroactive polymers. The artificial muscle computer is an example of the simplest known universal Turing machine, and as such it is capable of solving any computable problem given sufficient time and memory. By showing that artificial muscles can "think," the study paves the way for the development of smart, lifelike prostheses and soft robots that can conform to changing environments.

The authors, Benjamin Marc O'Brien and Iain Alexander Anderson at the University of Auckland in New Zealand, have published their study on the artificial muscle computer in a recent issue of Applied Physics Letters.

"To the best of our knowledge, this is the first time a computer has been built out of artificial muscles," O'Brien told "What makes it exciting is that the technology can be directly and intimately embedded into artificial muscle devices, giving them lifelike reflexes. Even though our computer has hard bits, the technology is fundamentally soft and stretchy, something that traditional methods of computation struggle with."

Video of the artificial muscle computer at work. Credit: O’Brien and Anderson. ©2013 American Institute of Physics

The artificial muscle computer is modeled on 's "2, 3" Turing machine architecture, which is the simplest known universal Turing machine. It consists of a machine head that reads symbols stored on a tape, and then based on the symbols and its own state (0 or 1), it follows a set of instructions that tells it what to write and store. The 2, 3 Turing machine is ideal to build with artificial muscles because of its simplicity. The researchers could theoretically solve any computational problem using just 13 muscles.

By expanding and contracting, the artificial muscles performed a variety of mechanisms involved in the computing process. For example, the muscles pushed sliding elements into position, and the sliding elements were used to encode data. Artificial muscles were also used to make the instruction set that the machine head uses to make decisions. In this case, when a muscle expands, it compresses a switch, causing it to conduct charge.

In its current version, the artificial muscle computer is very large (about 1 m3) and extremely slow (0.15 Hz). However, the researchers demonstrated that it could evolve the correct sequence of calculations in response to a test input, and they predict that the computer's performance could be significantly improved. In the future, the researchers also want to investigate whether this type of computer would perform better using an analog rather than digital architecture.

Artificial muscle computer performs as a universal Turing machine
(Left) The artificial muscle computer. (Right) Sample steps for a sequence of calculations performed by the computer. Credit: O’Brien and Anderson. ©2013 American Institute of Physics

Overall, the demonstration that artificial muscles can be made to compute and "think" has implications for future prosthetics and soft robots. By sensing, computing, and moving, could give these devices the ability to conform to complex and uncertain environments, as well as give them reflexes like the real muscles seen in nature.

"If you look at life you see these amazing capabilities and structures," O'Brien said. "The octopus, for example, has extremely dexterous infinite-degree-of-freedom manipulators. Such manipulators would be great for our own robots, but there is the huge challenge of how to control them—the degrees of freedom can overwhelm a central controller. Octopuses solve this by distributing neurons throughout their arms. With artificial muscle logic, we might one day be able to do the same."

The researchers plan to take several steps in order to reach these goals.

"In the future we would like to miniaturize the technology to make it go faster and become more portable; develop materials that last longer before failing; make the computer entirely soft; explore analogue architectures; and build a soft robotic manipulator with a built-in computer," O'Brien said.

The researchers have also recently formed a company called Stretch Sense that makes soft wireless stretch sensors using artificial muscle technology. In the future, they hope to commercialize their artificial muscle computing as well.

Explore further

New soft motor more closely resembles real muscles (w/ video)

More information: Benjamin Marc O'Brien and Iain Alexander Anderson. "An artificial muscle computer." Applied Physics Letters 102, 104102 (2013). DOI: 10.1063/1.4793648
Journal information: Applied Physics Letters

Copyright 2013
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Mar 28, 2013
Lego is plural and singular, like sheep and euro.

Mar 28, 2013
See the work of Gerald Pollack, who began by studying muscles, but ended up realizing that life's one big trick is its ability to structure its water into the gel state. Gels come with phase transition functionality, as well as *natural* ionic gradients, strongly suggesting that the pumps and channels within the cell membrane are not actually maintaining those ionic gradients across the membrane. It's simply the difference between bulk and structured water states, which can be flipped just like a transistor using proteins. Needless to say, this is fundamentally an electromagnetic process.

The living organism will one day turn our statistical theories for quantum mechanics into a small number of physical processes, and from Pollack's work, it's clear that the basis for the body's physical processes is the gel's phase transition.

Pollack's work is seeing replication in other laboratories, and there is much work left to be done creating systems biology simulations based upon gels.

Mar 28, 2013
"The living organism will one day turn our statistical theories for quantum mechanics into a small number of physical processes . . ."
Living organisms, like everything else, exist probabilistically- those pesky statistics aren't going anywhere.

Mar 29, 2013
Natello I think you are hinting at something I've heard more about? Microtubules are the highways for transporting vesicles, proteins, mitochondria etc. Things travel down microtubules, then hit junctions, or exit ramps. These exits are for individual synapses, and all that cargo ends up in a sort of logic gate where it can exit or remain on the highway. That decision is made by CaMKII, a responder of calcium. Synapses that are firing have more calcium and active CaMKII, and make cargo turn off towards their synapse, strengthening the synapse and meeting Hebbian theory.

Mar 29, 2013
Microtubules don't really affect physiology like that. Are you thinking of myelin sheaths?

Also, why would Stanford be educating their students on hoaxes? They are a highly respected institution for both the quality and integrity of their work.

Mar 29, 2013
Because physiology studies electrochemical gradients, which are made by channels on the cell surface, not microtubules that are enclosed within cytoplasm. Neuronal cells communicate by depolarizing. Microtubules don't depolarize the cell. But they do lead to the transport of proteins that enable depolarizations.

Apr 01, 2013
Cytoskeletal Signaling: Is Memory Encoded in Microtubule Lattices by CaMKII Phosphorylation?


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