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#436065 From Mainframes to PCs: What Robot ...
This is a guest post. The views expressed here are solely those of the author and do not represent positions of IEEE Spectrum or the IEEE.
Autonomous robots are coming around slowly. We already got autonomous vacuum cleaners, autonomous lawn mowers, toys that bleep and blink, and (maybe) soon autonomous cars. Yet, generation after generation, we keep waiting for the robots that we all know from movies and TV shows. Instead, businesses seem to get farther and farther away from the robots that are able to do a large variety of tasks using general-purpose, human anatomy-inspired hardware.
Although these are the droids we have been looking for, anything that came close, such as Willow Garage’s PR2 or Rethink Robotics’ Baxter has bitten the dust. With building a robotic company being particularly hard, compounding business risk with technological risk, the trend goes from selling robots to selling actual services like mowing your lawn, provide taxi rides, fulfilling retail orders, or picking strawberries by the pound. Unfortunately for fans of R2-D2 and C-3PO, these kind of business models emphasize specialized, room- or fridge-sized hardware that is optimized for one very specific task, but does not contribute to a general-purpose robotic platform.
We have actually seen something very similar in the personal computer (PC) industry. In the 1950s, even though computers could be as big as an entire room and were only available to a selected few, the public already had a good idea of what computers would look like. A long list of fictional computers started to populate mainstream entertainment during that time. In a 1962 New York Times article titled “Pocket Computer to Replace Shopping List,” visionary scientist John Mauchly stated that “there is no reason to suppose the average boy or girl cannot be master of a personal computer.”
In 1968, Douglas Engelbart gave us the “mother of all demos,” browsing hypertext on a graphical screen and a mouse, and other ideas that have become standard only decades later. Now that we have finally seen all of this, it might be helpful to examine what actually enabled the computing revolution to learn where robotics is really at and what we need to do next.
The parallels between computers and robots
In the 1970s, mainframes were about to be replaced by the emerging class of mini-computers, fridge-sized devices that cost less than US $25,000 ($165,000 in 2019 dollars). These computers did not use punch-cards, but could be programmed in Fortran and BASIC, dramatically expanding the ease with which potential applications could be created. Yet it was still unclear whether mini-computers could ever replace big mainframes in applications that require fast and efficient processing of large amounts of data, let alone enter every living room. This is very similar to the robotics industry right now, where large-scale factory robots (mainframes) that have existed since the 1960s are seeing competition from a growing industry of collaborative robots that can safely work next to humans and can easily be installed and programmed (minicomputers). As in the ’70s, applications for these devices that reach system prices comparable to that of a luxury car are quite limited, and it is hard to see how they could ever become a consumer product.
Yet, as in the computer industry, successful architectures are quickly being cloned, driving prices down, and entirely new approaches on how to construct or program robotic arms are sprouting left and right. Arm makers are joined by manufacturers of autonomous carts, robotic grippers, and sensors. These components can be combined, paving the way for standard general purpose platforms that follow the model of the IBM PC, which built a capable, open architecture relying as much on commodity parts as possible.
General purpose robotic systems have not been successful for similar reasons that general purpose, also known as “personal,” computers took decades to emerge. Mainframes were custom-built for each application, while typewriters got smarter and smarter, not really leaving room for general purpose computers in between. Indeed, given the cost of hardware and the relatively little abilities of today’s autonomous robots, it is almost always smarter to build a special purpose machine than trying to make a collaborative mobile manipulator smart.
A current example is e-commerce grocery fulfillment. The current trend is to reserve underutilized parts of a brick-and-mortar store for a micro-fulfillment center that stores goods in little crates with an automated retrieval system and a (human) picker. A number of startups like Alert Innovation, Fabric, Ocado Technology, TakeOff Technologies, and Tompkins Robotics, to just name a few, have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves. Such a robotic store clerk would come much closer to our vision of a general purpose robot, but would require many copies of itself that crowd the aisles to churn out hundreds of orders per hour as a microwarehouse could. Although eventually more efficient, the margins in retail are already low and make it unlikely that this industry will produce the technological jump that we need to get friendly C-3POs manning the aisles.
Startups have raised hundreds of millions of venture capital recently to build mainframe equivalents of robotic fulfillment centers. This is in contrast with a robotic picker, which would drive through the aisles to restock and pick from shelves, and would come much closer to our vision of a general purpose robot.
Mainframes were also attacked from the bottom. Fascination with the new digital technology has led to a hobbyist movement to create microcomputers that were sold via mail order or at RadioShack. Initially, a large number of small businesses was selling tens, at most hundreds, of devices, usually as a kit and with wooden enclosures. This trend culminated into the “1977 Trinity” in the form of the Apple II, the Commodore PET, and the Tandy TRS-80, complete computers that were sold for prices around $2500 (TRS) to $5000 (Apple) in today’s dollars. The main application of these computers was their programmability (in BASIC), which would enable consumers to “learn to chart your biorhythms, balance your checking account, or even control your home environment,” according to an original Apple advertisement. Similarly, there exists a myriad of gadgets that explore different aspects of robotics such as mobility, manipulation, and entertainment.
As in the fledgling personal computing industry, the advertised functionality was at best a model of the real deal. A now-famous milestone in entertainment robotics was the original Sony’s Aibo, a robotic dog that was advertised to have many properties that a real dog has such as develop its own personality, play with a toy, and interact with its owner. Released in 1999, and re-launched in 2018, the platform has a solid following among hobbyists and academics who like its programmability, but probably only very few users who accept the device as a pet stand-in.
There also exist countless “build-your-own-robotic-arm” kits. One of the more successful examples is the uArm, which sells for around $800, and is advertised to perform pick and place, assembly, 3D printing, laser engraving, and many other things that sound like high value applications. Using compelling videos of the robot actually doing these things in a constrained environment has led to two successful crowd-funding campaigns, and have established the robot as a successful educational tool.
Finally, there exist platforms that allow hobbyist programmers to explore mobility to construct robots that patrol your house, deliver items, or provide their users with telepresence abilities. An example of that is the Misty II. Much like with the original Apple II, there remains a disconnect between the price of the hardware and the fidelity of the applications that were available.
For computers, this disconnect began to disappear with the invention of the first electronic spreadsheet software VisiCalc that spun out of Harvard in 1979 and prompted many people to buy an entire microcomputer just to run the program. VisiCalc was soon joined by WordStar, a word processing application, that sold for close to $2000 in today’s dollars. WordStar, too, would entice many people to buy the entire hardware just to use the software. The two programs are early examples of what became known as “killer application.”
With factory automation being mature, and robots with the price tag of a minicomputer being capable of driving around and autonomously carrying out many manipulation tasks, the robotics industry is somewhere where the PC industry was between 1973—the release of the Xerox Alto, the first computer with a graphical user interface, mouse, and special software—and 1979—when microcomputers in the under $5000 category began to take off.
Killer apps for robots
So what would it take for robotics to continue to advance like computers did? The market itself already has done a good job distilling what the possible killer apps are. VCs and customers alike push companies who have set out with lofty goals to reduce their offering to a simple value proposition. As a result, companies that started at opposite ends often converge to mirror images of each other that offer very similar autonomous carts, (bin) picking, palletizing, depalletizing, or sorting solutions. Each of these companies usually serves a single application to a single vertical—for example bin-picking clothes, transporting warehouse goods, or picking strawberries by the pound. They are trying to prove that their specific technology works without spreading themselves too thin.
Very few of these companies have really taken off. One example is Kiva Systems, which turned into the logistic robotics division of Amazon. Kiva and others are structured around sound value propositions that are grounded in well-known user needs. As these solutions are very specialized, however, it is unlikely that they result into any economies of scale of the same magnitude that early computer users who bought both a spreadsheet and a word processor application for their expensive minicomputer could enjoy. What would make these robotic solutions more interesting is when functionality becomes stackable. Instead of just being able to do bin picking, palletizing, and transportation with the same hardware, these three skills could be combined to model entire processes.
A skill that is yet little addressed by startups and is historically owned by the mainframe equivalent of robotics is assembly of simple mechatronic devices. The ability to assemble mechatronic parts is equivalent to other tasks such as changing a light bulb, changing the batteries in a remote control, or tending machines like a lever-based espresso machine. These tasks would involve the autonomous execution of complete workflows possible using a single machine, eventually leading to an explosion of industrial productivity across all sectors. For example, picking up an item from a bin, arranging it on the robot, moving it elsewhere, and placing it into a shelf or a machine is a process that equally applies to a manufacturing environment, a retail store, or someone’s kitchen.
Image: Robotic Materials Inc.
Autonomous, vision and force-based assembly of the
Siemens robot learning challenge.
Even though many of the above applications are becoming possible, it is still very hard to get a platform off the ground without added components that provide “killer app” value of their own. Interesting examples are Rethink Robotics or the Robot Operating System (ROS). Rethink Robotics’ Baxter and Sawyer robots pioneered a great user experience (like the 1973 Xerox Alto, really the first PC), but its applications were difficult to extend beyond simple pick-and-place and palletizing and depalletizing items.
ROS pioneered interprocess communication software that was adapted to robotic needs (multiple computers, different programming languages) and the idea of software modularity in robotics, but—in the absence of a common hardware platform—hasn’t yet delivered a single application, e.g. for navigation, path planning, or grasping, that performs beyond research-grade demonstration level and won’t get discarded once developers turn to production systems. At the same time, an increasing number of robotic devices, such as robot arms or 3D perception systems that offer intelligent functionality, provide other ways to wire them together that do not require an intermediary computer, while keeping close control over the real-time aspects of their hardware.
Image: Robotic Materials Inc.
Robotic Materials GPR-1 combines a MIR-100 autonomous cart with an UR-5 collaborative robotic arm, an onRobot force/torque sensor and Robotic Materials’ SmartHand to perform out-of-the-box mobile assembly, bin picking, palletizing, and depalletizing tasks.
At my company, Robotic Materials Inc., we have made strides to identify a few applications such as bin picking and assembly, making them configurable with a single click by combining machine learning and optimization with an intuitive user interface. Here, users can define object classes and how to grasp them using a web browser, which then appear as first-class objects in a robot-specific graphical programming language. We have also done this for assembly, allowing users to stack perception-based picking and force-based assembly primitives by simply dragging and dropping appropriate commands together.
While such an approach might answer the question of a killer app for robots priced in the “minicomputer” range, it is unclear how killer app-type value can be generated with robots in the less-than-$5000 category. A possible answer is two-fold: First, with low-cost arms, mobility platforms, and entertainment devices continuously improving, a confluence of technology readiness and user innovation, like with the Apple II and VisiCalc, will eventually happen. For example, there is not much innovation needed to turn Misty into a home security system; the uArm into a low-cost bin-picking system; or an Aibo-like device into a therapeutic system for the elderly or children with autism.
Second, robots and their components have to become dramatically cheaper. Indeed, computers have seen an exponential reduction in price accompanied by an exponential increase in computational power, thanks in great part to Moore’s Law. This development has helped robotics too, allowing us to reach breakthroughs in mobility and manipulation due to the ability to process massive amounts of image and depth data in real-time, and we can expect it to continue to do so.
Is there a Moore’s Law for robots?
One might ask, however, how a similar dynamics might be possible for robots as a whole, including all their motors and gears, and what a “Moore’s Law” would look like for the robotics industry. Here, it helps to remember that the perpetuation of Moore’s Law is not the reason, but the result of the PC revolution. Indeed, the first killer apps for bookkeeping, editing, and gaming were so good that they unleashed tremendous consumer demand, beating the benchmark on what was thought to be physically possible over and over again. (I vividly remember 56 kbps to be the absolute maximum data rate for copper phone lines until DSL appeared.)
That these economies of scale are also applicable to mechatronics is impressively demonstrated by the car industry. A good example is the 2020 Prius Prime, a highly computerized plug-in hybrid, that is available for one third of the cost of my company’s GPR-1 mobile manipulator while being orders of magnitude more complex, sporting an electrical motor, a combustion engine, and a myriad of sensors and computers. It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal. Given that these robots are part of the equation, actively lowering cost of production, this might happen as fast as never before in the history of industrialization.
It is therefore very well conceivable to produce a mobile manipulator that retails at one tenth of the cost of a modern car, once robotics enjoy similar mass-market appeal.
There is one more driver that might make robots exponentially more capable: the cloud. Once a general purpose robot has learned or was programmed with a new skill, it could share it with every other robot. At some point, a grocer who buys a robot could assume that it already knows how to recognize and handle 99 percent of the retail items in the store. Likewise, a manufacturer can assume that the robot can handle and assemble every item available from McMaster-Carr and Misumi. Finally, families could expect a robot to know every kitchen item that Ikea and Pottery Barn is selling. Sounds like a labor intense problem, but probably more manageable than collecting footage for Google’s Street View using cars, tricycles, and snowmobiles, among other vehicles.
Strategies for robot startups
While we are waiting for these two trends—better and better applications and hardware with decreasing cost—to converge, we as a community have to keep exploring what the canonical robotic applications beyond mobility, bin picking, palletizing, depalletizing, and assembly are. We must also continue to solve the fundamental challenges that stand in the way of making these solutions truly general and robust.
For both questions, it might help to look at the strategies that have been critical in the development of the personal computer, which might equally well apply to robotics:
Start with a solution to a problem your customers have. Unfortunately, their problem is almost never that they need your sensor, widget, or piece of code, but something that already costs them money or negatively affects them in some other way. Example: There are many more people who had a problem calculating their taxes (and wanted to buy VisiCalc) than writing their own solution in BASIC.
Build as little of your own hardware as necessary. Your business model should be stronger than the margin you can make on the hardware. Why taking the risk? Example: Why build your own typewriter if you can write the best typewriting application that makes it worth buying a computer just for that?
If your goal is a platform, make sure it comes with a killer application, which alone justifies the platform cost. Example: Microcomputer companies came and went until the “1977 Trinity” intersected with the killer apps spreadsheet and word processors. Corollary: You can also get lucky.
Use an open architecture, which creates an ecosystem where others compete on creating better components and peripherals, while allowing others to integrate your solution into their vertical and stack it with other devices. Example: Both the Apple II and the IBM PC were completely open architectures, enabling many clones, thereby growing the user and developer base.
It’s worthwhile pursuing this. With most business processes already being digitized, general purpose robots will allow us to fill in gaps in mobility and manipulation, increasing productivity at levels only limited by the amount of resources and energy that are available, possibly creating a utopia in which creativity becomes the ultimate currency. Maybe we’ll even get R2-D2.
Nikolaus Correll is an associate professor of computer science at the University of Colorado at Boulder where he works on mobile manipulation and other robotics applications. He’s co-founder and CTO of Robotic Materials Inc., which is supported by the National Science Foundation and the National Institute of Standards and Technology via their Small Business Innovative Research (SBIR) programs. Continue reading →
Posted in Human Robots
#435816 This Light-based Nervous System Helps ...
Last night, way past midnight, I stumbled onto my porch blindly grasping for my keys after a hellish day of international travel. Lights were low, I was half-asleep, yet my hand grabbed the keychain, found the lock, and opened the door.
If you’re rolling your eyes—yeah, it’s not exactly an epic feat for a human. Thanks to the intricate wiring between our brain and millions of sensors dotted on—and inside—our skin, we know exactly where our hand is in space and what it’s touching without needing visual confirmation. But this combined sense of the internal and the external is completely lost to robots, which generally rely on computer vision or surface mechanosensors to track their movements and their interaction with the outside world. It’s not always a winning strategy.
What if, instead, we could give robots an artificial nervous system?
This month, a team led by Dr. Rob Shepard at Cornell University did just that, with a seriously clever twist. Rather than mimicking the electric signals in our nervous system, his team turned to light. By embedding optical fibers inside a 3D printed stretchable material, the team engineered an “optical lace” that can detect changes in pressure less than a fraction of a pound, and pinpoint the location to a spot half the width of a tiny needle.
The invention isn’t just an artificial skin. Instead, the delicate fibers can be distributed both inside a robot and on its surface, giving it both a sense of tactile touch and—most importantly—an idea of its own body position in space. Optical lace isn’t a superficial coating of mechanical sensors; it’s an entire platform that may finally endow robots with nerve-like networks throughout the body.
Eventually, engineers hope to use this fleshy, washable material to coat the sharp, cold metal interior of current robots, transforming C-3PO more into the human-like hosts of Westworld. Robots with a “bodily” sense could act as better caretakers for the elderly, said Shepard, because they can assist fragile people without inadvertently bruising or otherwise harming them. The results were published in Science Robotics.
An Unconventional Marriage
The optical lace is especially creative because it marries two contrasting ideas: one biological-inspired, the other wholly alien.
The overarching idea for optical lace is based on the animal kingdom. Through sight, hearing, smell, taste, touch, and other senses, we’re able to interpret the outside world—something scientists call exteroception. Thanks to our nervous system, we perform these computations subconsciously, allowing us to constantly “perceive” what’s going on around us.
Our other perception is purely internal. Proprioception (sorry, it’s not called “inception” though it should be) is how we know where our body parts are in space without having to look at them, which lets us perform complex tasks when blind. Although less intuitive than exteroception, proprioception also relies on stretching and other deformations within the muscles and tendons and receptors under the skin, which generate electrical currents that shoot up into the brain for further interpretation.
In other words, in theory it’s possible to recreate both perceptions with a single information-carrying system.
Here’s where the alien factor comes in. Rather than using electrical properties, the team turned to light as their data carrier. They had good reason. “Compared with electricity, light carries information faster and with higher data densities,” the team explained. Light can also transmit in multiple directions simultaneously, and is less susceptible to electromagnetic interference. Although optical nervous systems don’t exist in the biological world, the team decided to improve on Mother Nature and give it a shot.
Optical Lace
The construction starts with engineering a “sheath” for the optical nerve fibers. The team first used an elastic polyurethane—a synthetic material used in foam cushioning, for example—to make a lattice structure filled with large pores, somewhat like a lattice pie crust. Thanks to rapid, high-resolution 3D printing, the scaffold can have different stiffness from top to bottom. To increase sensitivity to the outside world, the team made the top of the lattice soft and pliable, to better transfer force to mechanical sensors. In contrast, the “deeper” regions held their structure better, and kept their structure under pressure.
Now the fun part. The team next threaded stretchable “light guides” into the scaffold. These fibers transmit photons, and are illuminated with a blue LED light. One, the input light guide, ran horizontally across the soft top part of the scaffold. Others ran perpendicular to the input in a “U” shape, going from more surface regions to deeper ones. These are the output guides. The architecture loosely resembles the wiring in our skin and flesh.
Normally, the output guides are separated from the input by a small air gap. When pressed down, the input light fiber distorts slightly, and if the pressure is high enough, it contacts one of the output guides. This causes light from the input fiber to “leak” to the output one, so that it lights up—the stronger the pressure, the brighter the output.
“When the structure deforms, you have contact between the input line and the output lines, and the light jumps into these output loops in the structure, so you can tell where the contact is happening,” said study author Patricia Xu. “The intensity of this determines the intensity of the deformation itself.”
Double Perception
As a proof-of-concept for proprioception, the team made a cylindrical lace with one input and 12 output channels. They varied the stiffness of the scaffold along the cylinder, and by pressing down at different points, were able to calculate how much each part stretched and deformed—a prominent precursor to knowing where different regions of the structure are moving in space. It’s a very rudimentary sort of proprioception, but one that will become more sophisticated with increasing numbers of strategically-placed mechanosensors.
The test for exteroception was a whole lot stranger. Here, the team engineered another optical lace with 15 output channels and turned it into a squishy piano. When pressed down, an Arduino microcontroller translated light output signals into sound based on the position of each touch. The stronger the pressure, the louder the volume. While not a musical masterpiece, the demo proved their point: the optical lace faithfully reported the strength and location of each touch.
A More Efficient Robot
Although remarkably novel, the optical lace isn’t yet ready for prime time. One problem is scalability: because of light loss, the material is limited to a certain size. However, rather than coating an entire robot, it may help to add optical lace to body parts where perception is critical—for example, fingertips and hands.
The team sees plenty of potential to keep developing the artificial flesh. Depending on particular needs, both the light guides and scaffold can be modified for sensitivity, spatial resolution, and accuracy. Multiple optical fibers that measure for different aspects—pressure, pain, temperature—can potentially be embedded in the same region, giving robots a multitude of senses.
In this way, we hope to reduce the number of electronics and combine signals from multiple sensors without losing information, the authors said. By taking inspiration from biological networks, it may even be possible to use various inputs through an optical lace to control how the robot behaves, closing the loop from sensation to action.
Image Credit: Cornell Organic Robotics Lab. A flexible, porous lattice structure is threaded with stretchable optical fibers containing more than a dozen mechanosensors and attached to an LED light. When the lattice structure is pressed, the sensors pinpoint changes in the photon flow. Continue reading →
Posted in Human Robots
#435583 Soft Self-Healing Materials for Robots ...
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.
[ SHERO Project ] Continue reading →
Posted in Human Robots
#435528 The Time for AI Is Now. Here’s Why
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
#435152 The Futuristic Tech Disrupting Real ...
In the wake of the housing market collapse of 2008, one entrepreneur decided to dive right into the failing real estate industry. But this time, he didn’t buy any real estate to begin with. Instead, Glenn Sanford decided to launch the first-ever cloud-based real estate brokerage, eXp Realty.
Contracting virtual platform VirBELA to build out the company’s mega-campus in VR, eXp Realty demonstrates the power of a dematerialized workspace, throwing out hefty overhead costs and fundamentally redefining what ‘real estate’ really means. Ten years later, eXp Realty has an army of 14,000 agents across all 50 US states, 3 Canadian provinces, and 400 MLS market areas… all without a single physical office.
But VR is just one of many exponential technologies converging to revolutionize real estate and construction. As floating cities and driverless cars spread out your living options, AI and VR are together cutting out the middleman.
Already, the global construction industry is projected to surpass $12.9 trillion in 2022, and the total value of the US housing market alone grew to $33.3 trillion last year. Both vital for our daily lives, these industries will continue to explode in value, posing countless possibilities for disruption.
In this blog, I’ll be discussing the following trends:
New prime real estate locations;
Disintermediation of the real estate broker and search;
Materials science and 3D printing in construction.
Let’s dive in!
Location Location Location
Until today, location has been the name of the game when it comes to hunting down the best real estate. But constraints on land often drive up costs while limiting options, and urbanization is only exacerbating the problem.
Beyond the world of virtual real estate, two primary mechanisms are driving the creation of new locations.
(1) Floating Cities
Offshore habitation hubs, floating cities have long been conceived as a solution to rising sea levels, skyrocketing urban populations, and threatened ecosystems. In success, they will soon unlock an abundance of prime real estate, whether for scenic living, commerce, education, or recreation.
One pioneering model is that of Oceanix City, designed by Danish architect Bjarke Ingels and a host of other domain experts. Intended to adapt organically over time, Oceanix would consist of a galaxy of mass-produced, hexagonal floating modules, built as satellite “cities” off coastal urban centers and sustained by renewable energies.
While individual 4.5-acre platforms would each sustain 300 people, these hexagonal modules are designed to link into 75-acre tessellations sustaining up to 10,000 residents. Each anchored to the ocean floor using biorock, Oceanix cities are slated to be closed-loop systems, as external resources are continuously supplied by automated drone networks.
Electric boats or flying cars might zoom you to work, city-embedded water capture technologies would provide your water, and while vertical and outdoor farming supply your family meal, share economies would dominate goods provision.
AERIAL: Located in calm, sheltered waters, near coastal megacities, OCEANIX City will be an adaptable, sustainable, scalable, and affordable solution for human life on the ocean. Image Credit: OCEANIX/BIG-Bjarke Ingels Group.
Joined by countless government officials whose islands risk submersion at the hands of sea level rise, the UN is now getting on board. And just this year, seasteading is exiting the realm of science fiction and testing practical waters.
As French Polynesia seeks out robust solutions to sea level rise, their government has now joined forces with the San Francisco-based Seasteading Institute. With a newly designated special economic zone and 100 acres of beachfront, this joint Floating Island Project could even see up to a dozen inhabitable structures by 2020. And what better to fund the $60 million project than the team’s upcoming ICO?
But aside from creating new locations, autonomous vehicles (AVs) and flying cars are turning previously low-demand land into the prime real estate of tomorrow.
(2) Autonomous Electric Vehicles and Flying Cars
Today, the value of a location is a function of its proximity to your workplace, your city’s central business district, the best schools, or your closest friends.
But what happens when driverless cars desensitize you to distance, or Hyperloop and flying cars decimate your commute time? Historically, every time new transit methods have hit the mainstream, tolerance for distance has opened up right alongside them, further catalyzing city spread.
And just as Hyperloop and the Boring Company aim to make your commute immaterial, autonomous vehicle (AV) ridesharing services will spread out cities in two ways: (1) by drastically reducing parking spaces needed (vertical parking decks = more prime real estate); and (2) by untethering you from the steering wheel. Want an extra two hours of sleep on the way to work? Schedule a sleeper AV and nap on your route to the office. Need a car-turned-mobile-office? No problem.
Meanwhile, aerial taxis (i.e. flying cars) will allow you to escape ground congestion entirely, delivering you from bedroom to boardroom at decimated time scales.
Already working with regulators, Uber Elevate has staked ambitious plans for its UberAIR airborne taxi project. By 2023, Uber anticipates rolling out flying drones in its two first pilot cities, Los Angeles and Dallas. Flying between rooftop skyports, drones would carry passengers at a height of 1,000 to 2,000 feet at speeds between 100 to 200 mph. And while costs per ride are anticipated to resemble those of an Uber Black based on mileage, prices are projected to soon drop to those of an UberX.
But the true economic feat boils down to this: if I were to commute 50 to 100 kilometers, I could get two or three times the house for the same price. (Not to mention the extra living space offered up by my now-unneeded garage.)
All of a sudden, virtual reality, broadband, AVs, or high-speed vehicles are going to change where we live and where we work. So rather than living in a crowded, dense urban core for access to jobs and entertainment, our future of personalized, autonomous, low-cost transport opens the luxury of rural areas to all without compromising the benefits of a short commute.
Once these drivers multiply your real estate options, how will you select your next home?
Disintermediation: Say Bye to Your Broker
In a future of continuous and personalized preference-tracking, why hire a human agent who knows less about your needs and desires than a personal AI?
Just as disintermediation is cutting out bankers and insurance agents, so too is it closing in on real estate brokers. Over the next decade, as AI becomes your agent, VR will serve as your medium.
To paint a more vivid picture of how this will look, over 98 percent of your home search will be conducted from the comfort of your couch through next-generation VR headgear.
Once you’ve verbalized your primary desires for home location, finishings, size, etc. to your personal AI, it will offer you top picks, tour-able 24/7, with optional assistance by a virtual guide and constantly updated data. As a seller, this means potential buyers from two miles, or two continents, away.
Throughout each immersive VR tour, advanced eye-tracking software and a permissioned machine learning algorithm follow your gaze, further learn your likes and dislikes, and intelligently recommend other homes or commercial residences to visit.
Curious as to what the living room might look like with a fresh coat of blue paint and a white carpet? No problem! VR programs will be able to modify rendered environments instantly, changing countless variables, from furniture materials to even the sun’s orientation. Keen to input your own furniture into a VR-rendered home? Advanced AIs could one day compile all your existing furniture, electronics, clothing, decorations, and even books, virtually organizing them across any accommodating new space.
As 3D scanning technologies make extraordinary headway, VR renditions will only grow cheaper and higher resolution. One company called Immersive Media (disclosure: I’m an investor and advisor) has a platform for 360-degree video capture and distribution, and is already exploring real estate 360-degree video.
Smaller firms like Studio 216, Vieweet, Arch Virtual, ArX Solutions, and Rubicon Media can similarly capture and render models of various properties for clients and investors to view and explore. In essence, VR real estate platforms will allow you to explore any home for sale, do the remodel, and determine if it truly is the house of your dreams.
Once you’re ready to make a bid, your AI will even help estimate a bid, process and submit your offer. Real estate companies like Zillow, Trulia, Move, Redfin, ZipRealty (acquired by Realogy in 2014) and many others have already invested millions in machine learning applications to make search, valuation, consulting, and property management easier, faster, and much more accurate.
But what happens if the home you desire most means starting from scratch with new construction?
New Methods and Materials for Construction
For thousands of years, we’ve been constrained by the construction materials of nature. We built bricks from naturally abundant clay and shale, used tree limbs as our rooftops and beams, and mastered incredible structures in ancient Rome with the use of cement.
But construction is now on the cusp of a materials science revolution. Today, I’d like to focus on three key materials:
Upcycled Materials
Imagine if you could turn the world’s greatest waste products into their most essential building blocks. Thanks to UCLA researchers at CO2NCRETE, we can already do this with carbon emissions.
Today, concrete produces about five percent of all greenhouse gas (GHG) emissions. But what if concrete could instead conserve greenhouse emissions? CO2NCRETE engineers capture carbon from smokestacks and combine it with lime to create a new type of cement. The lab’s 3D printers then shape the upcycled concrete to build entirely new structures. Once conquered at scale, upcycled concrete will turn a former polluter into a future conserver.
Or what if we wanted to print new residences from local soil at hand? Marking an extraordinary convergence between robotics and 3D printing, the Institute of Advanced Architecture of Catalonia (IAAC) is already working on a solution.
In a major feat for low-cost construction in remote zones, IAAC has found a way to convert almost any soil into a building material with three times the tensile strength of industrial clay. Offering myriad benefits, including natural insulation, low GHG emissions, fire protection, air circulation, and thermal mediation, IAAC’s new 3D printed native soil can build houses on-site for as little as $1,000.
Nanomaterials
Nano- and micro-materials are ushering in a new era of smart, super-strong, and self-charging buildings. While carbon nanotubes dramatically increase the strength-to-weight ratio of skyscrapers, revolutionizing their structural flexibility, nanomaterials don’t stop here.
Several research teams are pioneering silicon nanoparticles to capture everyday light flowing through our windows. Little solar cells at the edges of windows then harvest this energy for ready use. Researchers at the US National Renewable Energy Lab have developed similar smart windows. Turning into solar panels when bathed in sunlight, these thermochromic windows will power our buildings, changing color as they do.
Self-Healing Infrastructure
The American Society of Civil Engineers estimates that the US needs to spend roughly $4.5 trillion to fix nationwide roads, bridges, dams, and common infrastructure by 2025. But what if infrastructure could fix itself?
Enter self-healing concrete. Engineers at Delft University have developed bio-concrete that can repair its own cracks. As head researcher Henk Jonkers explains, “What makes this limestone-producing bacteria so special is that they are able to survive in concrete for more than 200 years and come into play when the concrete is damaged. […] If cracks appear as a result of pressure on the concrete, the concrete will heal these cracks itself.”
But bio-concrete is only the beginning of self-healing technologies. As futurist architecture firms start printing plastic and carbon-fiber houses like the stunner seen below (using Branch Technologies’ 3D printing technology), engineers have begun tackling self-healing plastic.
And in a bid to go smart, burgeoning construction projects have started embedding sensors for preemptive detection. Beyond materials and sensors, however, construction methods are fast colliding into robotics and 3D printing.
While some startups and research institutes have leveraged robot swarm construction (namely, Harvard’s robotic termite-like swarm of programmed constructors), others have taken to large-scale autonomous robots.
One such example involves Fastbrick Robotics. After multiple iterations, the company’s Hadrian X end-to-end bricklaying robot can now autonomously build a fully livable, 180-square meter home in under 3 days. Using a laser-guided robotic attachment, the all-in-one brick-loaded truck simply drives to a construction site and directs blocks through its robotic arm in accordance with a 3D model.
Layhead. Image Credit: Fastbrick Robotics.
Meeting verified building standards, Hadrian and similar solutions hold massive promise in the long term, deployable across post-conflict refugee sites and regions recovering from natural catastrophes.
Imagine the implications. Eliminating human safety concerns and unlocking any environment, autonomous builder robots could collaboratively build massive structures in space or deep underwater habitats.
Final Thoughts
Where, how, and what we live in form a vital pillar of our everyday lives. The concept of “home” is unlikely to disappear anytime soon. At the same time, real estate and construction are two of the biggest playgrounds for technological convergence, each on the verge of revolutionary disruption.
As underlying shifts in transportation, land reclamation, and the definition of “space” (real vs. virtual) take hold, the real estate market is about to explode in value, spreading out urban centers on unprecedented scales and unlocking vast new prime “property.”
Meanwhile, converging advancements in AI and VR are fundamentally disrupting the way we design, build, and explore new residences. Just as mirror worlds create immersive, virtual real estate economies, VR tours and AI agents are absorbing both sides of the coin to entirely obliterate the middleman.
And as materials science breakthroughs meet new modes of construction, the only limits to tomorrow’s structures are those of our own imagination.
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Image Credit: OCEANIX/BIG-Bjarke Ingels Group. Continue reading →
Posted in Human Robots