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#435583 Soft Self-Healing Materials for Robots ...

Posted on September 11, 2019 by Android

If there’s one thing we know about robots, it’s that they break. They break, like, literally all the time. The software breaks. The hardware breaks. The bits that you think could never, ever, ever possibly break end up breaking just when you need them not to break the most, and then you have to try to explain what happened to your advisor who’s been standing there watching your robot fail and then stay up all night fixing the thing that seriously was not supposed to break.

While most of this is just a fundamental characteristic of robots that can’t be helped, the European Commission is funding a project called SHERO (Self HEaling soft RObotics) to try and solve at least some of those physical robot breaking problems through the use of structural materials that can autonomously heal themselves over and over again.

SHERO is a three year, €3 million collaboration between Vrije Universiteit Brussel, University of Cambridge, École Supérieure de Physique et de Chimie Industrielles de la ville de Paris (ESPCI-Paris), and Swiss Federal Laboratories for Materials Science and Technology (Empa). As the name SHERO suggests, the goal of the project is to develop soft materials that can completely recover from the kinds of damage that robots are likely to suffer in day to day operations, as well as the occasional more extreme accident.

Most materials, especially soft materials, are fixable somehow, whether it’s with super glue or duct tape. But fixing things involves a human first identifying when they’re broken, and then performing a potentially skill, labor, time, and money intensive task. SHERO’s soft materials will, eventually, make this entire process autonomous, allowing robots to self-identify damage and initiate healing on their own.

Photos: SHERO Project

The damaged robot finger [top] can operate normally after healing itself.

How the self-healing material works
What these self-healing materials can do is really pretty amazing. The researchers are actually developing two different types—the first one heals itself when there’s an application of heat, either internally or externally, which gives some control over when and how the healing process starts. For example, if the robot is handling stuff that’s dirty, you’d want to get it cleaned up before healing it so that dirt doesn’t become embedded in the material. This could mean that the robot either takes itself to a heating station, or it could activate some kind of embedded heating mechanism to be more self-sufficient.

The second kind of self-healing material is autonomous, in that it will heal itself at room temperature without any additional input, and is probably more suitable for relatively minor scrapes and cracks. Here are some numbers about how well the healing works:

Autonomous self-healing polymers do not require heat. They can heal damage at room temperature. Developing soft robotic systems from autonomous self-healing polymers excludes the need of additional heating devices… The healing however takes some time. The healing efficiency after 3 days, 7 days and 14 days is respectively 62 percent, 91 percent and 97 percent.

This material was used to develop a healable soft pneumatic hand. Relevant large cuts can be healed entirely without the need of external heat stimulus. Depending on the size of the damage and even more on the location of damage, the healing takes only seconds or up to a week. Damage on locations on the actuator that are subjected to very small stresses during actuation was healed instantaneously. Larger damages, like cutting the actuator completely in half, took 7 days to heal. But even this severe damage could be healed completely without the need of any external stimulus.

Applications of self-healing robots
Both of these materials can be mixed together, and their mechanical properties can be customized so that the structure that they’re a part of can be tuned to move in different ways. The researchers also plan on introducing flexible conductive sensors into the material, which will help sense damage as well as providing position feedback for control systems. A lot of development will happen over the next few years, and for more details, we spoke with Bram Vanderborght at Vrije Universiteit in Brussels.

IEEE Spectrum: How easy or difficult or expensive is it to produce these materials? Will they add significant cost to robotic grippers?

Bram Vanderborght: They are definitely more expensive materials, but it’s also a matter of size of production. At the moment, we’ve made a few kilograms of the material (enough to make several demonstrators), and the price already dropped significantly from when we ordered 100 grams of the material in the first phase of the project. So probably the cost of the gripper will be higher [than a regular gripper], but you won’t need to replace the gripper as often as other grippers that need to be replaced due to wear, so it can be an advantage.

Moreover due to the method of 3D printing the material, the surface is smoother and airtight (so no post-processing is required to make it airtight). Also, the smooth surface is better to avoid contamination for food handling, for example.

In commercial or industrial applications, gradual fatigue seems to be a more common issue than more abrupt trauma like cuts. How well does the self-healing work to improve durability over long periods of time?

We did not test for gradual fatigue over very long times. But both macroscopic and microscopic damage can be healed. So hopefully it can provide an answer here as well.

Image: SHERO Project

After developing a self-healing robot gripper, the researchers plan to use similar materials to build parts that can be used as the skeleton of robots, allowing them to repair themselves on a regular basis.

How much does the self-healing capability restrict the material properties? What are the limits for softness or hardness or smoothness or other characteristics of the material?

Typically the mechanical properties of networked polymers are much better than thermoplastics. Our material is a networked polymer but in which the crosslinks are reversible. We can change quite a lot of parameters in the design of the materials. So we can develop very stiff (fracture strain at 1.24 percent) and very elastic materials (fracture strain at 450 percent). The big advantage that our material has is we can mix it to have intermediate properties. Moreover, at the interface of the materials with different mechanical properties, we have the same chemical bonds, so the interface is perfect. While other materials, they may need to glue it, which gives local stresses and a weak spot.

When the material heals itself, is it less structurally sound in that spot? Can it heal damage that happens to the same spot over and over again?

In theory we can heal it an infinite amount of times. When the wound is not perfectly aligned, of course in that spot it will become weaker. Also too high temperatures lead to irreversible bonds, and impurities lead to weak spots.

Besides grippers and skins, what other potential robotics applications would this technology be useful for?

Most of self healing materials available now are used for coatings. What we are developing are structural components, therefore the mechanical properties of the material need to be good for such applications. So maybe part of the skeleton of the robot can be developed with such materials to make it lighter, since can be designed for regular repair. And for exceptional loads, it breaks and can be repaired like our human body.

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Posted in Human Robots

#435575 How an AI Startup Designed a Drug ...

Posted on September 9, 2019 by Android

Discovering a new drug can take decades, billions of dollars, and untold man hours from some of the smartest people on the planet. Now a startup says it’s taken a significant step towards speeding the process up using AI.

The typical drug discovery process involves carrying out physical tests on enormous libraries of molecules, and even with the help of robotics it’s an arduous process. The idea of sidestepping this by using computers to virtually screen for promising candidates has been around for decades. But progress has been underwhelming, and it’s still not a major part of commercial pipelines.

Recent advances in deep learning, however, have reignited hopes for the field, and major pharma companies have started tying up with AI-powered drug discovery startups. And now Insilico Medicine has used AI to design a molecule that effectively targets a protein involved in fibrosis—the formation of excess fibrous tissue—in mice in just 46 days.

The platform the company has developed combines two of the hottest sub-fields of AI: the generative adversarial networks, or GANs, which power deepfakes, and reinforcement learning, which is at the heart of the most impressive game-playing AI advances of recent years.

In a paper in Nature Biotechnology, the company’s researchers describe how they trained their model on all the molecules already known to target this protein as well as many other active molecules from various datasets. The model was then used to generate 30,000 candidate molecules.

Unlike most previous efforts, they went a step further and selected the most promising molecules for testing in the lab. The 30,000 candidates were whittled down to just 6 using more conventional drug discovery approaches and were then synthesized in the lab. They were put through increasingly stringent tests, but the leading candidate was found to be effective at targeting the desired protein and behaved as one would hope a drug would.

The authors are clear that the results are just a proof-of-concept, which company CEO Alex Zhavoronkov told Wired stemmed from a challenge set by a pharma partner to design a drug as quickly as possible. But they say they were able to carry out the process faster than traditional methods for a fraction of the cost.

There are some caveats. For a start, the protein being targeted is already very well known and multiple effective drugs exist for it. That gave the company a wealth of data to train their model on, something that isn’t the case for many of the diseases where we urgently need new drugs.

The company’s platform also only targets the very initial stages of the drug discovery process. The authors concede in their paper that the molecules would still take considerable optimization in the lab before they’d be true contenders for clinical trials.

“And that is where you will start to begin to commence to spend the vast piles of money that you will eventually go through in trying to get a drug to market,” writes Derek Lowe in his blog In The Pipeline. The part of the discovery process that the platform tackles represents a tiny fraction of the total cost of drug development, he says.

Nonetheless, the research is a definite advance for virtual screening technology and an important marker of the potential of AI for designing new medicines. Zhavoronkov also told Wired that this research was done more than a year ago, and they’ve since adapted the platform to go after harder drug targets with less data.

And with big pharma companies desperate to slash their ballooning development costs and find treatments for a host of intractable diseases, they can use all the help they can get.

Image Credit: freestocks.org / Unsplash Continue reading →

Posted in Human Robots

#435541 This Giant AI Chip Is the Size of an ...

Posted on August 26, 2019 by Android

People say size doesn’t matter, but when it comes to AI the makers of the largest computer chip ever beg to differ. There are plenty of question marks about the gargantuan processor, but its unconventional design could herald an innovative new era in silicon design.

Computer chips specialized to run deep learning algorithms are a booming area of research as hardware limitations begin to slow progress, and both established players and startups are vying to build the successor to the GPU, the specialized graphics chip that has become the workhorse of the AI industry.

On Monday Californian startup Cerebras came out of stealth mode to unveil an AI-focused processor that turns conventional wisdom on its head. For decades chip makers have been focused on making their products ever-smaller, but the Wafer Scale Engine (WSE) is the size of an iPad and features 1.2 trillion transistors, 400,000 cores, and 18 gigabytes of on-chip memory.

The Cerebras Wafer-Scale Engine (WSE) is the largest chip ever built. It measures 46,225 square millimeters and includes 1.2 trillion transistors. Optimized for artificial intelligence compute, the WSE is shown here for comparison alongside the largest graphics processing unit. Image Credit: Used with permission from Cerebras Systems.
There is a method to the madness, though. Currently, getting enough cores to run really large-scale deep learning applications means connecting banks of GPUs together. But shuffling data between these chips is a major drain on speed and energy efficiency because the wires connecting them are relatively slow.

Building all 400,000 cores into the same chip should get round that bottleneck, but there are reasons it’s not been done before, and Cerebras has had to come up with some clever hacks to get around those obstacles.

Regular computer chips are manufactured using a process called photolithography to etch transistors onto the surface of a wafer of silicon. The wafers are inches across, so multiple chips are built onto them at once and then split up afterwards. But at 8.5 inches across, the WSE uses the entire wafer for a single chip.

The problem is that while for standard chip-making processes any imperfections in manufacturing will at most lead to a few processors out of several hundred having to be ditched, for Cerebras it would mean scrapping the entire wafer. To get around this the company built in redundant circuits so that even if there are a few defects, the chip can route around them.

The other big issue with a giant chip is the enormous amount of heat the processors can kick off—so the company has had to design a proprietary water-cooling system. That, along with the fact that no one makes connections and packaging for giant chips, means the WSE won’t be sold as a stand-alone component, but as part of a pre-packaged server incorporating the cooling technology.

There are no details on costs or performance so far, but some customers have already been testing prototypes, and according to Cerebras results have been promising. CEO and co-founder Andrew Feldman told Fortune that early tests show they are reducing training time from months to minutes.

We’ll have to wait until the first systems ship to customers in September to see if those claims stand up. But Feldman told ZDNet that the design of their chip should help spur greater innovation in the way engineers design neural networks. Many cornerstones of this process—for instance, tackling data in batches rather than individual data points—are guided more by the hardware limitations of GPUs than by machine learning theory, but their chip will do away with many of those obstacles.

Whether that turns out to be the case or not, the WSE might be the first indication of an innovative new era in silicon design. When Google announced it’s AI-focused Tensor Processing Unit in 2016 it was a wake-up call for chipmakers that we need some out-of-the-box thinking to square the slowing of Moore’s Law with skyrocketing demand for computing power.

It’s not just tech giants’ AI server farms driving innovation. At the other end of the spectrum, the desire to embed intelligence in everyday objects and mobile devices is pushing demand for AI chips that can run on tiny amounts of power and squeeze into the smallest form factors.

These trends have spawned renewed interest in everything from brain-inspired neuromorphic chips to optical processors, but the WSE also shows that there might be mileage in simply taking a sideways look at some of the other design decisions chipmakers have made in the past rather than just pumping ever more transistors onto a chip.

This gigantic chip might be the first exhibit in a weird and wonderful new menagerie of exotic, AI-inspired silicon.

Image Credit: Used with permission from Cerebras Systems. Continue reading →

Posted in Human Robots

#435528 The Time for AI Is Now. Here’s Why

Posted on August 21, 2019 by Android

You hear a lot these days about the sheer transformative power of AI.

There’s pure intelligence: DeepMind’s algorithms readily beat humans at Go and StarCraft, and DeepStack triumphs over humans at no-limit hold’em poker. Often, these silicon brains generate gameplay strategies that don’t resemble anything from a human mind.

There’s astonishing speed: algorithms routinely surpass radiologists in diagnosing breast cancer, eye disease, and other ailments visible from medical imaging, essentially collapsing decades of expert training down to a few months.

Although AI’s silent touch is mainly felt today in the technological, financial, and health sectors, its impact across industries is rapidly spreading. At the Singularity University Global Summit in San Francisco this week Neil Jacobstein, Chair of AI and Robotics, painted a picture of a better AI-powered future for humanity that is already here.

Thanks to cloud-based cognitive platforms, sophisticated AI tools like deep learning are no longer relegated to academic labs. For startups looking to tackle humanity’s grand challenges, the tools to efficiently integrate AI into their missions are readily available. The progress of AI is massively accelerating—to the point you need help from AI to track its progress, joked Jacobstein.

Now is the time to consider how AI can impact your industry, and in the process, begin to envision a beneficial relationship with our machine coworkers. As Jacobstein stressed in his talk, the future of a brain-machine mindmeld is a collaborative intelligence that augments our own. “AI is reinventing the way we invent,” he said.

AI’s Rapid Revolution
Machine learning and other AI-based methods may seem academic and abstruse. But Jacobstein pointed out that there are already plenty of real-world AI application frameworks.

Their secret? Rather than coding from scratch, smaller companies—with big visions—are tapping into cloud-based solutions such as Google’s TensorFlow, Microsoft’s Azure, or Amazon’s AWS to kick off their AI journey. These platforms act as all-in-one solutions that not only clean and organize data, but also contain built-in security and drag-and-drop coding that allow anyone to experiment with complicated machine learning algorithms.

Google Cloud’s Anthos, for example, lets anyone migrate data from other servers—IBM Watson or AWS, for example—so users can leverage different computing platforms and algorithms to transform data into insights and solutions.

Rather than coding from scratch, it’s already possible to hop onto a platform and play around with it, said Jacobstein. That’s key: this democratization of AI is how anyone can begin exploring solutions to problems we didn’t even know we had, or those long thought improbable.

The acceleration is only continuing. Much of AI’s mind-bending pace is thanks to a massive infusion of funding. Microsoft recently injected $1 billion into OpenAI, the Elon Musk venture that engineers socially responsible artificial general intelligence (AGI).

The other revolution is in hardware, and Google, IBM, and NVIDIA—among others—are racing to manufacture computing chips tailored to machine learning.

Democratizing AI is like the birth of the printing press. Mechanical printing allowed anyone to become an author; today, an iPhone lets anyone film a movie masterpiece.

However, this diffusion of AI into the fabric of our lives means tech explorers need to bring skepticism to their AI solutions, giving them a dose of empathy, nuance, and humanity.

A Path Towards Ethical AI
The democratization of AI is a double-edged sword: as more people wield the technology’s power in real-world applications, problems embedded in deep learning threaten to disrupt those very judgment calls.

Much of the press on the dangers of AI focuses on superintelligence—AI that’s more adept at learning than humans—taking over the world, said Jacobstein. But the near-term threat, and far more insidious, is in humans misusing the technology.

Deepfakes, for example, allow AI rookies to paste one person’s head on a different body or put words into a person’s mouth. As the panel said, it pays to think of AI as a cybersecurity problem, one with currently shaky accountability and complexity, and one that fails at diversity and bias.

Take bias. Thanks to progress in natural language processing, Google Translate works nearly perfectly today, so much so that many consider the translation problem solved. Not true, the panel said. One famous example is how the algorithm translates gender-neutral terms like “doctor” into “he” and “nurse” into “she.”

These biases reflect our own, and it’s not just a data problem. To truly engineer objective AI systems, ones stripped of our society’s biases, we need to ask who is developing these systems, and consult those who will be impacted by the products. In addition to gender, racial bias is also rampant. For example, one recent report found that a supposedly objective crime-predicting system was trained on falsified data, resulting in outputs that further perpetuate corrupt police practices. Another study from Google just this month found that their hate speech detector more often labeled innocuous tweets from African-Americans as “obscene” compared to tweets from people of other ethnicities.

We often think of building AI as purely an engineering job, the panelists agreed. But similar to gene drives, germ-line genome editing, and other transformative—but dangerous—tools, AI needs to grow under the consultation of policymakers and other stakeholders. It pays to start young: educating newer generations on AI biases will mold malleable minds early, alerting them to the problem of bias and potentially mitigating risks.

As panelist Tess Posner from AI4ALL said, AI is rocket fuel for ambition. If young minds set out using the tools of AI to tackle their chosen problems, while fully aware of its inherent weaknesses, we can begin to build an AI-embedded future that is widely accessible and inclusive.

The bottom line: people who will be impacted by AI need to be in the room at the conception of an AI solution. People will be displaced by the new technology, and ethical AI has to consider how to mitigate human suffering during the transition. Just because AI looks like “magic fairy dust doesn’t mean that you’re home free,” the panelists said. You, the sentient human, bear the burden of being responsible for how you decide to approach the technology.

The time for AI is now. Let’s make it ethical.

Image Credit: GrAI / Shutterstock.com Continue reading →

Posted in Human Robots

#435522 Harvard’s Smart Exo-Shorts Talk to the ...

Posted on August 20, 2019 by Android

Exosuits don’t generally scream “fashionable” or “svelte.” Take the mind-controlled robotic exoskeleton that allowed a paraplegic man to kick off the World Cup back in 2014. Is it cool? Hell yeah. Is it practical? Not so much.

Yapping about wearability might seem childish when the technology already helps people with impaired mobility move around dexterously. But the lesson of the ill-fated Google Glassholes, which includes an awkward dorky head tilt and an assuming voice command, clearly shows that wearable computer assistants can’t just work technologically—they have to look natural and allow the user to behave like as usual. They have to, in a sense, disappear.

To Dr. Jose Pons at the Legs + Walking Ability Lab in Chicago, exosuits need three main selling points to make it in the real world. One, they have to physically interact with their wearer and seamlessly deliver assistance when needed. Two, they should cognitively interact with the host to guide and control the robot at all times. Finally, they need to feel like a second skin—move with the user without adding too much extra mass or reducing mobility.

This week, a US-Korean collaboration delivered the whole shebang in a Lululemon-style skin-hugging package combined with a retro waist pack. The portable exosuit, weighing only 11 pounds, looks like a pair of spandex shorts but can support the wearer’s hip movement when needed. Unlike their predecessors, the shorts are embedded with sensors that let them know when the wearer is walking versus running by analyzing gait.

Switching between the two movement modes may not seem like much, but what naturally comes to our brains doesn’t translate directly to smart exosuits. “Walking and running have fundamentally different biomechanics, which makes developing devices that assist both gaits challenging,” the team said. Their algorithm, computed in the cloud, allows the wearer to easily switch between both, with the shorts providing appropriate hip support that makes the movement experience seamless.

To Pons, who was not involved in the research but wrote a perspective piece, the study is an exciting step towards future exosuits that will eventually disappear under the skin—that is, implanted neural interfaces to control robotic assistance or activate the user’s own muscles.

“It is realistic to think that we will witness, in the next several years…robust human-robot interfaces to command wearable robotics based on…the neural code of movement in humans,” he said.

A “Smart” Exosuit Hack
There are a few ways you can hack a human body to move with an exosuit. One is using implanted electrodes inside the brain or muscles to decipher movement intent. With heavy practice, a neural implant can help paralyzed people walk again or dexterously move external robotic arms. But because the technique requires surgery, it’s not an immediate sell for people who experience low mobility because of aging or low muscle tone.

The other approach is to look to biophysics. Rather than decoding neural signals that control movement, here the idea is to measure gait and other physical positions in space to decipher intent. As you can probably guess, accurately deciphering user intent isn’t easy, especially when the wearable tries to accommodate multiple gaits. But the gains are many: there’s no surgery involved, and the wearable is low in energy consumption.

Double Trouble
The authors decided to tackle an everyday situation. You’re walking to catch the train to work, realize you’re late, and immediately start sprinting.

That seemingly easy conversion hides a complex switch in biomechanics. When you walk, your legs act like an inverted pendulum that swing towards a dedicated center in a predictable way. When you run, however, the legs move more like a spring-loaded system, and the joints involved in the motion differ from a casual stroll. Engineering an assistive wearable for each is relatively simple; making one for both is exceedingly hard.

Led by Dr. Conor Walsh at Harvard University, the team started with an intuitive idea: assisted walking and running requires specialized “actuation” profiles tailored to both. When the user is moving in a way that doesn’t require assistance, the wearable needs to be out of the way so that it doesn’t restrict mobility. A quick analysis found that assisting hip extension has the largest impact, because it’s important to both gaits and doesn’t add mass to the lower legs.

Building on that insight, the team made a waist belt connected to two thigh wraps, similar to a climbing harness. Two electrical motors embedded inside the device connect the waist belt to other components through a pulley system to help the hip joints move. The whole contraption weighed about 11 lbs and didn’t obstruct natural movement.

Next, the team programmed two separate supporting profiles for walking and running. The goal was to reduce the “metabolic cost” for both movements, so that the wearer expends as little energy as needed. To switch between the two programs, they used a cloud-based classification algorithm to measure changes in energy fluctuation to figure out what mode—running or walking—the user is in.

Smart Booster
Initial trials on treadmills were highly positive. Six male volunteers with similar age and build donned the exosuit and either ran or walked on the treadmill at varying inclines. The algorithm performed perfectly at distinguishing between the two gaits in all conditions, even at steep angles.

An outdoor test with eight volunteers also proved the algorithm nearly perfect. Even on uneven terrain, only two steps out of all test trials were misclassified. In an additional trial on mud or snow, the algorithm performed just as well.

“The system allows the wearer to use their preferred gait for each speed,” the team said.

Software excellence translated to performance. A test found that the exosuit reduced the energy for walking by over nine percent and running by four percent. It may not sound like much, but the range of improvement is meaningful in athletic performance. Putting things into perspective, the team said, the metabolic rate reduction during walking is similar to taking 16 pounds off at the waist.

The Wearable Exosuit Revolution
The study’s lightweight exoshorts are hardly the only players in town. Back in 2017, SRI International’s spin-off, Superflex, engineered an Aura suit to support mobility in the elderly. The Aura used a different mechanism: rather than a pulley system, it incorporated a type of smart material that contracts in a manner similar to human muscles when zapped with electricity.

Embedded with a myriad of sensors for motion, accelerometers and gyroscopes, Aura’s smartness came from mini-computers that measure how fast the wearer is moving and track the user’s posture. The data were integrated and processed locally inside hexagon-shaped computing pods near the thighs and upper back. The pods also acted as the control center for sending electrical zaps to give the wearer a boost when needed.

Around the same time, a collaboration between Harvard’s Wyss Institute and ReWalk Robotics introduced a fabric-based wearable robot to assist a wearer’s legs for balance and movement. Meanwhile, a Swiss team coated normal fabric with electroactive material to weave soft, pliable artificial “muscles” that move with the skin.

Although health support is the current goal, the military is obviously interested in similar technologies to enhance soldiers’ physicality. Superflex’s Aura, for example, was originally inspired by technology born from DARPA’s Warrior Web Program, which aimed to reduce a soldier’s mechanical load.

That said, military gear has had a long history of trickling down to consumer use. Similar to the way camouflage, cargo pants, and GORE-TEX trickled down into the consumer ecosphere, it’s not hard to imagine your local Target eventually stocking intelligent exowear.

Image and Video Credit: Wyss Institute at Harvard University. Continue reading →

Posted in Human Robots