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#436911 Scientists Linked Artificial and ...

Posted on March 11, 2020 by Android

Scientists have linked up two silicon-based artificial neurons with a biological one across multiple countries into a fully-functional network. Using standard internet protocols, they established a chain of communication whereby an artificial neuron controls a living, biological one, and passes on the info to another artificial one.

Whoa.

We’ve talked plenty about brain-computer interfaces and novel computer chips that resemble the brain. We’ve covered how those “neuromorphic” chips could link up into tremendously powerful computing entities, using engineered communication nodes called artificial synapses.

As Moore’s law is dying, we even said that neuromorphic computing is one path towards the future of extremely powerful, low energy consumption artificial neural network-based computing—in hardware—that could in theory better link up with the brain. Because the chips “speak” the brain’s language, in theory they could become neuroprosthesis hubs far more advanced and “natural” than anything currently possible.

This month, an international team put all of those ingredients together, turning theory into reality.

The three labs, scattered across Padova, Italy, Zurich, Switzerland, and Southampton, England, collaborated to create a fully self-controlled, hybrid artificial-biological neural network that communicated using biological principles, but over the internet.

The three-neuron network, linked through artificial synapses that emulate the real thing, was able to reproduce a classic neuroscience experiment that’s considered the basis of learning and memory in the brain. In other words, artificial neuron and synapse “chips” have progressed to the point where they can actually use a biological neuron intermediary to form a circuit that, at least partially, behaves like the real thing.

That’s not to say cyborg brains are coming soon. The simulation only recreated a small network that supports excitatory transmission in the hippocampus—a critical region that supports memory—and most brain functions require enormous cross-talk between numerous neurons and circuits. Nevertheless, the study is a jaw-dropping demonstration of how far we’ve come in recreating biological neurons and synapses in artificial hardware.

And perhaps one day, the currently “experimental” neuromorphic hardware will be integrated into broken biological neural circuits as bridges to restore movement, memory, personality, and even a sense of self.

The Artificial Brain Boom
One important thing: this study relies heavily on a decade of research into neuromorphic computing, or the implementation of brain functions inside computer chips.

The best-known example is perhaps IBM’s TrueNorth, which leveraged the brain’s computational principles to build a completely different computer than what we have today. Today’s computers run on a von Neumann architecture, in which memory and processing modules are physically separate. In contrast, the brain’s computing and memory are simultaneously achieved at synapses, small “hubs” on individual neurons that talk to adjacent ones.

Because memory and processing occur on the same site, biological neurons don’t have to shuttle data back and forth between processing and storage compartments, massively reducing processing time and energy use. What’s more, a neuron’s history will also influence how it behaves in the future, increasing flexibility and adaptability compared to computers. With the rise of deep learning, which loosely mimics neural processing as the prima donna of AI, the need to reduce power while boosting speed and flexible learning is becoming ever more tantamount in the AI community.

Neuromorphic computing was partially born out of this need. Most chips utilize special ingredients that change their resistance (or other physical characteristics) to mimic how a neuron might adapt to stimulation. Some chips emulate a whole neuron, that is, how it responds to a history of stimulation—does it get easier or harder to fire? Others imitate synapses themselves, that is, how easily they will pass on the information to another neuron.

Although single neuromorphic chips have proven to be far more efficient and powerful than current computer chips running machine learning algorithms in toy problems, so far few people have tried putting the artificial components together with biological ones in the ultimate test.

That’s what this study did.

A Hybrid Network
Still with me? Let’s talk network.

It’s gonna sound complicated, but remember: learning is the formation of neural networks, and neurons that fire together wire together. To rephrase: when learning, neurons will spontaneously organize into networks so that future instances will re-trigger the entire network. To “wire” together, downstream neurons will become more responsive to their upstream neural partners, so that even a whisper will cause them to activate. In contrast, some types of stimulation will cause the downstream neuron to “chill out” so that only an upstream “shout” will trigger downstream activation.

Both these properties—easier or harder to activate downstream neurons—are essentially how the brain forms connections. The “amping up,” in neuroscience jargon, is long-term potentiation (LTP), whereas the down-tuning is LTD (long-term depression). These two phenomena were first discovered in the rodent hippocampus more than half a century ago, and ever since have been considered as the biological basis of how the brain learns and remembers, and implicated in neurological problems such as addition (seriously, you can’t pass Neuro 101 without learning about LTP and LTD!).

So it’s perhaps especially salient that one of the first artificial-brain hybrid networks recapitulated this classic result.

To visualize: the three-neuron network began in Switzerland, with an artificial neuron with the badass name of “silicon spiking neuron.” That neuron is linked to an artificial synapse, a “memristor” located in the UK, which is then linked to a biological rat neuron cultured in Italy. The rat neuron has a “smart” microelectrode, controlled by the artificial synapse, to stimulate it. This is the artificial-to-biological pathway.

Meanwhile, the rat neuron in Italy also has electrodes that listen in on its electrical signaling. This signaling is passed back to another artificial synapse in the UK, which is then used to control a second artificial neuron back in Switzerland. This is the biological-to-artificial pathway back. As a testimony in how far we’ve come in digitizing neural signaling, all of the biological neural responses are digitized and sent over the internet to control its far-out artificial partner.

Here’s the crux: to demonstrate a functional neural network, just having the biological neuron passively “pass on” electrical stimulation isn’t enough. It has to show the capacity to learn, that is, to be able to mimic the amping up and down-tuning that are LTP and LTD, respectively.

You’ve probably guessed the results: certain stimulation patterns to the first artificial neuron in Switzerland changed how the artificial synapse in the UK operated. This, in turn, changed the stimulation to the biological neuron, so that it either amped up or toned down depending on the input.

Similarly, the response of the biological neuron altered the second artificial synapse, which then controlled the output of the second artificial neuron. Altogether, the biological and artificial components seamlessly linked up, over thousands of miles, into a functional neural circuit.

Cyborg Mind-Meld
So…I’m still picking my jaw up off the floor.

It’s utterly insane seeing a classic neuroscience learning experiment repeated with an integrated network with artificial components. That said, a three-neuron network is far from the thousands of synapses (if not more) needed to truly re-establish a broken neural circuit in the hippocampus, which DARPA has been aiming to do. And LTP/LTD has come under fire recently as the de facto brain mechanism for learning, though so far they remain cemented as neuroscience dogma.

However, this is one of the few studies where you see fields coming together. As Richard Feynman famously said, “What I cannot recreate, I cannot understand.” Even though neuromorphic chips were built on a high-level rather than molecular-level understanding of how neurons work, the study shows that artificial versions can still synapse with their biological counterparts. We’re not just on the right path towards understanding the brain, we’re recreating it, in hardware—if just a little.

While the study doesn’t have immediate use cases, practically it does boost both the neuromorphic computing and neuroprosthetic fields.

“We are very excited with this new development,” said study author Dr. Themis Prodromakis at the University of Southampton. “On one side it sets the basis for a novel scenario that was never encountered during natural evolution, where biological and artificial neurons are linked together and communicate across global networks; laying the foundations for the Internet of Neuro-electronics. On the other hand, it brings new prospects to neuroprosthetic technologies, paving the way towards research into replacing dysfunctional parts of the brain with AI chips.”

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#435494 Driverless Electric Trucks Are Coming, ...

Posted on August 14, 2019 by Android

Self-driving and electric cars just don’t stop making headlines lately. Amazon invested in self-driving startup Aurora earlier this year. Waymo, Daimler, GM, along with startups like Zoox, have all launched or are planning to launch driverless taxis, many of them all-electric. People are even yanking driverless cars from their timeless natural habitat—roads—to try to teach them to navigate forests and deserts.

The future of driving, it would appear, is upon us.

But an equally important vehicle that often gets left out of the conversation is trucks; their relevance to our day-to-day lives may not be as visible as that of cars, but their impact is more profound than most of us realize.

Two recent developments in trucking point to a future of self-driving, electric semis hauling goods across the country, and likely doing so more quickly, cheaply, and safely than trucks do today.

Self-Driving in Texas
Last week, Kodiak Robotics announced it’s beginning its first commercial deliveries using self-driving trucks on a route from Dallas to Houston. The two cities sit about 240 miles apart, connected primarily by interstate 45. Kodiak is aiming to expand its reach far beyond the heart of Texas (if Dallas and Houston can be considered the heart, that is) to the state’s most far-flung cities, including El Paso to the west and Laredo to the south.

If self-driving trucks are going to be constrained to staying within state lines (and given that the laws regulating them differ by state, they will be for the foreseeable future), Texas is a pretty ideal option. It’s huge (thousands of miles of highway run both east-west and north-south), it’s warm (better than cold for driverless tech components like sensors), its proximity to Mexico means constant movement of both raw materials and manufactured goods (basically, you can’t have too many trucks in Texas), and most crucially, it’s lax on laws (driverless vehicles have been permitted there since 2017).

Spoiler, though—the trucks won’t be fully unmanned. They’ll have safety drivers to guide them onto and off of the highway, and to be there in case of any unexpected glitches.

California Goes (Even More) Electric
According to some top executives in the rideshare industry, automation is just one key component of the future of driving. Another is electricity replacing gas, and it’s not just carmakers that are plugging into the trend.

This week, Daimler Trucks North America announced completion of its first electric semis for customers Penske and NFI, to be used in the companies’ southern California operations. Scheduled to start operating later this month, the trucks will essentially be guinea pigs for testing integration of electric trucks into large-scale fleets; intel gleaned from the trucks’ performance will impact the design of later models.

Design-wise, the trucks aren’t much different from any other semi you’ve seen lumbering down the highway recently. Their range is about 250 miles—not bad if you think about how much more weight a semi is pulling than a passenger sedan—and they’ve been dubbed eCascadia, an electrified version of Freightliner’s heavy-duty Cascadia truck.

Batteries have a long way to go before they can store enough energy to make electric trucks truly viable (not to mention setting up a national charging infrastructure), but Daimler’s announcement is an important step towards an electrically-driven future.

Keep on Truckin’
Obviously, it’s more exciting to think about hailing one of those cute little Waymo cars with no steering wheel to shuttle you across town than it is to think about that 12-pack of toilet paper you ordered on Amazon cruising down the highway in a semi while the safety driver takes a snooze. But pushing driverless and electric tech in the trucking industry makes sense for a few big reasons.

Trucks mostly run long routes on interstate highways—with no pedestrians, stoplights, or other city-street obstacles to contend with, highway driving is much easier to automate. What glitches there are to be smoothed out may as well be smoothed out with cargo on board rather than people. And though you wouldn’t know it amid the frantic shouts of ‘a robot could take your job!’, the US is actually in the midst of a massive shortage of truck drivers—60,000 short as of earlier this year, to be exact.

As Todd Spencer, president of the Owner-Operator Independent Drivers Association, put it, “Trucking is an absolutely essential, critical industry to the nation, to everybody in it.” Alas, trucks get far less love than cars, but come on—probably 90 percent of the things you ate, bought, or used today were at some point moved by a truck.

Adding driverless and electric tech into that equation, then, should yield positive outcomes on all sides, whether we’re talking about cheaper 12-packs of toilet paper, fewer traffic fatalities due to human error, a less-strained labor force, a stronger economy… or something pretty cool to see as you cruise down the highway in your (driverless, electric, futuristic) car.

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#433668 A Decade of Commercial Space ...

Posted on September 28, 2018 by Android

In many industries, a decade is barely enough time to cause dramatic change unless something disruptive comes along—a new technology, business model, or service design. The space industry has recently been enjoying all three.

But 10 years ago, none of those innovations were guaranteed. In fact, on Sept. 28, 2008, an entire company watched and hoped as their flagship product attempted a final launch after three failures. With cash running low, this was the last shot. Over 21,000 kilograms of kerosene and liquid oxygen ignited and powered two booster stages off the launchpad.

This first official picture of the Soviet satellite Sputnik I was issued in Moscow Oct. 9, 1957. The satellite measured 1 foot, 11 inches and weighed 184 pounds. The Space Age began as the Soviet Union launched Sputnik, the first man-made satellite, into orbit, on Oct. 4, 1957.AP Photo/TASS
When that Falcon 1 rocket successfully reached orbit and the company secured a subsequent contract with NASA, SpaceX had survived its ‘startup dip’. That milestone, the first privately developed liquid-fueled rocket to reach orbit, ignited a new space industry that is changing our world, on this planet and beyond. What has happened in the intervening years, and what does it mean going forward?

While scientists are busy developing new technologies that address the countless technical problems of space, there is another segment of researchers, including myself, studying the business angle and the operations issues facing this new industry. In a recent paper, my colleague Christopher Tang and I investigate the questions firms need to answer in order to create a sustainable space industry and make it possible for humans to establish extraterrestrial bases, mine asteroids and extend space travel—all while governments play an increasingly smaller role in funding space enterprises. We believe these business solutions may hold the less-glamorous key to unlocking the galaxy.

The New Global Space Industry
When the Soviet Union launched their Sputnik program, putting a satellite in orbit in 1957, they kicked off a race to space fueled by international competition and Cold War fears. The Soviet Union and the United States played the primary roles, stringing together a series of “firsts” for the record books. The first chapter of the space race culminated with Neil Armstrong and Buzz Aldrin’s historic Apollo 11 moon landing which required massive public investment, on the order of US$25.4 billion, almost $200 billion in today’s dollars.

Competition characterized this early portion of space history. Eventually, that evolved into collaboration, with the International Space Station being a stellar example, as governments worked toward shared goals. Now, we’ve entered a new phase—openness—with private, commercial companies leading the way.

The industry for spacecraft and satellite launches is becoming more commercialized, due, in part, to shrinking government budgets. According to a report from the investment firm Space Angels, a record 120 venture capital firms invested over $3.9 billion in private space enterprises last year. The space industry is also becoming global, no longer dominated by the Cold War rivals, the United States and USSR.

In 2018 to date, there have been 72 orbital launches, an average of two per week, from launch pads in China, Russia, India, Japan, French Guinea, New Zealand, and the US.

The uptick in orbital launches of actual rockets as well as spacecraft launches, which includes satellites and probes launched from space, coincides with this openness over the past decade.

More governments, firms and even amateurs engage in various spacecraft launches than ever before. With more entities involved, innovation has flourished. As Roberson notes in Digital Trends, “Private, commercial spaceflight. Even lunar exploration, mining, and colonization—it’s suddenly all on the table, making the race for space today more vital than it has felt in years.”

Worldwide launches into space. Orbital launches include manned and unmanned spaceships launched into orbital flight from Earth. Spacecraft launches include all vehicles such as spaceships, satellites and probes launched from Earth or space. Wooten, J. and C. Tang (2018) Operations in space, Decision Sciences; Space Launch Report (Kyle 2017); Spacecraft Encyclopedia (Lafleur 2017), CC BY-ND

One can see this vitality plainly in the news. On Sept. 21, Japan announced that two of its unmanned rovers, dubbed Minerva-II-1, had landed on a small, distant asteroid. For perspective, the scale of this landing is similar to hitting a 6-centimeter target from 20,000 kilometers away. And earlier this year, people around the world watched in awe as SpaceX’s Falcon Heavy rocket successfully launched and, more impressively, returned its two boosters to a landing pad in a synchronized ballet of epic proportions.

Challenges and Opportunities
Amidst the growth of capital, firms, and knowledge, both researchers and practitioners must figure out how entities should manage their daily operations, organize their supply chain, and develop sustainable operations in space. This is complicated by the hurdles space poses: distance, gravity, inhospitable environments, and information scarcity.

One of the greatest challenges involves actually getting the things people want in space, into space. Manufacturing everything on Earth and then launching it with rockets is expensive and restrictive. A company called Made In Space is taking a different approach by maintaining an additive manufacturing facility on the International Space Station and 3D printing right in space. Tools, spare parts, and medical devices for the crew can all be created on demand. The benefits include more flexibility and better inventory management on the space station. In addition, certain products can be produced better in space than on Earth, such as pure optical fiber.

How should companies determine the value of manufacturing in space? Where should capacity be built and how should it be scaled up? The figure below breaks up the origin and destination of goods between Earth and space and arranges products into quadrants. Humans have mastered the lower left quadrant, made on Earth—for use on Earth. Moving clockwise from there, each quadrant introduces new challenges, for which we have less and less expertise.

A framework of Earth-space operations. Wooten, J. and C. Tang (2018) Operations in Space, Decision Sciences, CC BY-ND
I first became interested in this particular problem as I listened to a panel of robotics experts discuss building a colony on Mars (in our third quadrant). You can’t build the structures on Earth and easily send them to Mars, so you must manufacture there. But putting human builders in that extreme environment is equally problematic. Essentially, an entirely new mode of production using robots and automation in an advance envoy may be required.

Resources in Space
You might wonder where one gets the materials for manufacturing in space, but there is actually an abundance of resources: Metals for manufacturing can be found within asteroids, water for rocket fuel is frozen as ice on planets and moons, and rare elements like helium-3 for energy are embedded in the crust of the moon. If we brought that particular isotope back to Earth, we could eliminate our dependence on fossil fuels.

As demonstrated by the recent Minerva-II-1 asteroid landing, people are acquiring the technical know-how to locate and navigate to these materials. But extraction and transport are open questions.

How do these cases change the economics in the space industry? Already, companies like Planetary Resources, Moon Express, Deep Space Industries, and Asterank are organizing to address these opportunities. And scholars are beginning to outline how to navigate questions of property rights, exploitation and partnerships.

Threats From Space Junk
A computer-generated image of objects in Earth orbit that are currently being tracked. Approximately 95 percent of the objects in this illustration are orbital debris – not functional satellites. The dots represent the current location of each item. The orbital debris dots are scaled according to the image size of the graphic to optimize their visibility and are not scaled to Earth. NASA
The movie “Gravity” opens with a Russian satellite exploding, which sets off a chain reaction of destruction thanks to debris hitting a space shuttle, the Hubble telescope, and part of the International Space Station. The sequence, while not perfectly plausible as written, is a very real phenomenon. In fact, in 2013, a Russian satellite disintegrated when it was hit with fragments from a Chinese satellite that exploded in 2007. Known as the Kessler effect, the danger from the 500,000-plus pieces of space debris has already gotten some attention in public policy circles. How should one prevent, reduce or mitigate this risk? Quantifying the environmental impact of the space industry and addressing sustainable operations is still to come.

NASA scientist Mark Matney is seen through a fist-sized hole in a 3-inch thick piece of aluminum at Johnson Space Center’s orbital debris program lab. The hole was created by a thumb-size piece of material hitting the metal at very high speed simulating possible damage from space junk. AP Photo/Pat Sullivan
What’s Next?
It’s true that space is becoming just another place to do business. There are companies that will handle the logistics of getting your destined-for-space module on board a rocket; there are companies that will fly those rockets to the International Space Station; and there are others that can make a replacement part once there.

What comes next? In one sense, it’s anybody’s guess, but all signs point to this new industry forging ahead. A new breakthrough could alter the speed, but the course seems set: exploring farther away from home, whether that’s the moon, asteroids, or Mars. It’s hard to believe that 10 years ago, SpaceX launches were yet to be successful. Today, a vibrant private sector consists of scores of companies working on everything from commercial spacecraft and rocket propulsion to space mining and food production. The next step is working to solidify the business practices and mature the industry.

Standing in a large hall at the University of Pittsburgh as part of the White House Frontiers Conference, I see the future. Wrapped around my head are state-of-the-art virtual reality goggles. I’m looking at the surface of Mars. Every detail is immediate and crisp. This is not just a video game or an aimless exercise. The scientific community has poured resources into such efforts because exploration is preceded by information. And who knows, maybe 10 years from now, someone will be standing on the actual surface of Mars.

Image Credit: SpaceX

Joel Wooten, Assistant Professor of Management Science, University of South Carolina

This article is republished from The Conversation under a Creative Commons license. Read the original article. Continue reading →

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#432181 Putting AI in Your Pocket: MIT Chip Cuts ...

Posted on February 27, 2018 by Android

Neural networks are powerful things, but they need a lot of juice. Engineers at MIT have now developed a new chip that cuts neural nets’ power consumption by up to 95 percent, potentially allowing them to run on battery-powered mobile devices.

Smartphones these days are getting truly smart, with ever more AI-powered services like digital assistants and real-time translation. But typically the neural nets crunching the data for these services are in the cloud, with data from smartphones ferried back and forth.

That’s not ideal, as it requires a lot of communication bandwidth and means potentially sensitive data is being transmitted and stored on servers outside the user’s control. But the huge amounts of energy needed to power the GPUs neural networks run on make it impractical to implement them in devices that run on limited battery power.

Engineers at MIT have now designed a chip that cuts that power consumption by up to 95 percent by dramatically reducing the need to shuttle data back and forth between a chip’s memory and processors.

Neural nets consist of thousands of interconnected artificial neurons arranged in layers. Each neuron receives input from multiple neurons in the layer below it, and if the combined input passes a certain threshold it then transmits an output to multiple neurons above it. The strength of the connection between neurons is governed by a weight, which is set during training.

This means that for every neuron, the chip has to retrieve the input data for a particular connection and the connection weight from memory, multiply them, store the result, and then repeat the process for every input. That requires a lot of data to be moved around, expending a lot of energy.

The new MIT chip does away with that, instead computing all the inputs in parallel within the memory using analog circuits. That significantly reduces the amount of data that needs to be shoved around and results in major energy savings.

The approach requires the weights of the connections to be binary rather than a range of values, but previous theoretical work had suggested this wouldn’t dramatically impact accuracy, and the researchers found the chip’s results were generally within two to three percent of the conventional non-binary neural net running on a standard computer.

This isn’t the first time researchers have created chips that carry out processing in memory to reduce the power consumption of neural nets, but it’s the first time the approach has been used to run powerful convolutional neural networks popular for image-based AI applications.

“The results show impressive specifications for the energy-efficient implementation of convolution operations with memory arrays,” Dario Gil, vice president of artificial intelligence at IBM, said in a statement.

“It certainly will open the possibility to employ more complex convolutional neural networks for image and video classifications in IoT [the internet of things] in the future.”

It’s not just research groups working on this, though. The desire to get AI smarts into devices like smartphones, household appliances, and all kinds of IoT devices is driving the who’s who of Silicon Valley to pile into low-power AI chips.

Apple has already integrated its Neural Engine into the iPhone X to power things like its facial recognition technology, and Amazon is rumored to be developing its own custom AI chips for the next generation of its Echo digital assistant.

The big chip companies are also increasingly pivoting towards supporting advanced capabilities like machine learning, which has forced them to make their devices ever more energy-efficient. Earlier this year ARM unveiled two new chips: the Arm Machine Learning processor, aimed at general AI tasks from translation to facial recognition, and the Arm Object Detection processor for detecting things like faces in images.

Qualcomm’s latest mobile chip, the Snapdragon 845, features a GPU and is heavily focused on AI. The company has also released the Snapdragon 820E, which is aimed at drones, robots, and industrial devices.

Going a step further, IBM and Intel are developing neuromorphic chips whose architectures are inspired by the human brain and its incredible energy efficiency. That could theoretically allow IBM’s TrueNorth and Intel’s Loihi to run powerful machine learning on a fraction of the power of conventional chips, though they are both still highly experimental at this stage.

Getting these chips to run neural nets as powerful as those found in cloud services without burning through batteries too quickly will be a big challenge. But at the current pace of innovation, it doesn’t look like it will be too long before you’ll be packing some serious AI power in your pocket.

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#431689 Robotic Materials Will Distribute ...

Posted on December 5, 2017 by Android

The classical view of a robot as a mechanical body with a central “brain” that controls its behavior could soon be on its way out. The authors of a recent article in Science Robotics argue that future robots will have intelligence distributed throughout their bodies.
The concept, and the emerging discipline behind it, are variously referred to as “material robotics” or “robotic materials” and are essentially a synthesis of ideas from robotics and materials science. Proponents say advances in both fields are making it possible to create composite materials capable of combining sensing, actuation, computation, and communication and operating independently of a central processing unit.
Much of the inspiration for the field comes from nature, with practitioners pointing to the adaptive camouflage of the cuttlefish’s skin, the ability of bird wings to morph in response to different maneuvers, or the banyan tree’s ability to grow roots above ground to support new branches.
Adaptive camouflage and morphing wings have clear applications in the defense and aerospace sector, but the authors say similar principles could be used to create everything from smart tires able to calculate the traction needed for specific surfaces to grippers that can tailor their force to the kind of object they are grasping.
“Material robotics represents an acknowledgment that materials can absorb some of the challenges of acting and reacting to an uncertain world,” the authors write. “Embedding distributed sensors and actuators directly into the material of the robot’s body engages computational capabilities and offloads the rigid information and computational requirements from the central processing system.”
The idea of making materials more adaptive is not new, and there are already a host of “smart materials” that can respond to stimuli like heat, mechanical stress, or magnetic fields by doing things like producing a voltage or changing shape. These properties can be carefully tuned to create materials capable of a wide variety of functions such as movement, self-repair, or sensing.
The authors say synthesizing these kinds of smart materials, alongside other advanced materials like biocompatible conductors or biodegradable elastomers, is foundational to material robotics. But the approach also involves integration of many different capabilities in the same material, careful mechanical design to make the most of mechanical capabilities, and closing the loop between sensing and control within the materials themselves.
While there are stand-alone applications for such materials in the near term, like smart fabrics or robotic grippers, the long-term promise of the field is to distribute decision-making in future advanced robots. As they are imbued with ever more senses and capabilities, these machines will be required to shuttle huge amounts of control and feedback data to and fro, placing a strain on both their communication and computation abilities.
Materials that can process sensor data at the source and either autonomously react to it or filter the most relevant information to be passed on to the central processing unit could significantly ease this bottleneck. In a press release related to an earlier study, Nikolaus Correll, an assistant professor of computer science at the University of Colorado Boulder who is also an author of the current paper, pointed out this is a tactic used by the human body.
“The human sensory system automatically filters out things like the feeling of clothing rubbing on the skin,” he said. “An artificial skin with possibly thousands of sensors could do the same thing, and only report to a central ‘brain’ if it touches something new.”
There are still considerable challenges to realizing this vision, though, the authors say, noting that so far the young field has only produced proof of concepts. The biggest challenge remains manufacturing robotic materials in a way that combines all these capabilities in a small enough package at an affordable cost.
Luckily, the authors note, the field can draw on convergent advances in both materials science, such as the development of new bulk materials with inherent multifunctionality, and robotics, such as the ever tighter integration of components.
And they predict that doing away with the prevailing dichotomy of “brain versus body” could lay the foundations for the emergence of “robots with brains in their bodies—the foundation of inexpensive and ubiquitous robots that will step into the real world.”
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