Tag Archives: gadgets
#436507 The Weird, the Wacky, the Just Plain ...
As you know if you’ve ever been to, heard of, or read about the annual Consumer Electronics Show in Vegas, there’s no shortage of tech in any form: gadgets, gizmos, and concepts abound. You probably couldn’t see them all in a month even if you spent all day every day trying.
Given the sheer scale of the show, the number of exhibitors, and the inherent subjectivity of bestowing superlatives, it’s hard to pick out the coolest tech from CES. But I’m going to do it anyway; in no particular order, here are some of the products and concepts that I personally found most intriguing at this year’s event.
e-Novia’s Haptic Gloves
Italian startup e-Novia’s Weart glove uses a ‘sensing core’ to record tactile sensations and an ‘actuation core’ to reproduce those sensations onto the wearer’s skin. Haptic gloves will bring touch to VR and AR experiences, making them that much more life-like. The tech could also be applied to digitization of materials and in gaming and entertainment.
e-Novia’s modular haptic glove
I expected a full glove, but in fact there were two rings that attached to my fingers. Weart co-founder Giovanni Spagnoletti explained that they’re taking a modular approach, so as to better tailor the technology to different experiences. He then walked me through a virtual reality experience that was a sort of simulated science experiment: I had to lift a glass beaker, place it on a stove, pour in an ingredient, open a safe to access some dry ice, add that, and so on. As I went through the steps, I felt the beaker heat up and cool off at the expected times, and felt the liquid moving inside, as well as the pressure of my fingertips against the numbered buttons on the safe.
A virtual (but tactile) science experiment
There was a slight delay between my taking an action and feeling the corresponding tactile sensation, but on the whole, the haptic glove definitely made the experience more realistic—and more fun. Slightly less fun but definitely more significant, Spagnoletti told me Weart is working with a medical group to bring tactile sensations to VR training for surgeons.
Sarcos Robotics’ Exoskeleton
That tire may as well be a feather
Sarcos Robotics unveiled its Guardian XO full-body exoskeleton, which it says can safely lift up to 200 pounds across an extended work session. What’s cool about this particular exoskeleton is that it’s not just a prototype; the company announced a partnership with Delta airlines, which will be trialing the technology for aircraft maintenance, engine repair, and luggage handling. In a demo, I watched a petite female volunteer strap into the exoskeleton and easily lift a 50-pound weight with one hand, and a Sarcos employee lift and attach a heavy component of a propeller; she explained that the strength-augmenting function of the exoskeleton can easily be switched on or off—and the wearer’s hands released—to facilitate multi-step tasks.
Hyundai’s Flying Taxi
Where to?
Hyundai and Uber partnered to unveil an air taxi concept. With a 49-foot wingspan, 4 lift rotors, and 4 tilt rotors, the aircraft would be manned by a pilot and could carry 4 passengers at speeds up to 180 miles per hour. The companies say you’ll be able to ride across your city in one of these by 2030—we’ll see if the regulatory environment, public opinion, and other factors outside of technological capability let that happen.
Mercedes’ Avatar Concept Car
Welcome to the future
As evident from its name, Mercedes’ sweet new Vision AVTR concept car was inspired by the movie Avatar; director James Cameron helped design it. The all-electric car has no steering wheel, transparent doors, seats made of vegan leather, and 33 reptilian-scale-like flaps on the back; its design is meant to connect the driver with both the car and the surrounding environment in a natural, seamless way.
Next-generation scrolling
Offered the chance to ‘drive’ the car, I jumped on it. Placing my hand on the center console started the engine, and within seconds it had synced to my heartbeat, which reverberated through the car. The whole dashboard, from driver door to passenger door, is one big LED display. It showed a virtual landscape I could select by holding up my hand: as I moved my hand from left to right, different images were projected onto my open palm. Closing my hand on an image selected it, and suddenly it looked like I was in the middle of a lush green mountain range. Applying slight forward pressure on the center console made the car advance in the virtual landscape; it was essentially like playing a really cool video game.
Mercedes is aiming to have a carbon-neutral production fleet by 2039, and to reduce the amount of energy it uses during production by 40 percent by 2030. It’s unclear when—or whether—the man-machine-nature connecting features of the Vision AVTR will start showing up in production, but I for one will be on the lookout.
Waverly Labs’ In-Ear Translator
Waverly Labs unveiled its Ambassador translator earlier this year and has it on display at the show. It’s worn on the ear and uses a far-field microphone array with speech recognition to translate real-time conversations in 20 different languages. Besides in-ear audio, translations can also appear as text on an app or be broadcast live in a conference environment.
It’s kind of like a giant talking earring
I stopped by the booth and tested out the translator with Waverly senior software engineer Georgiy Konovalov. We each hooked on an earpiece, and first, he spoke to me in Russian. After a delay of a couple seconds, I heard his words in—slightly robotic, but fully comprehensible—English. Then we switched: I spoke to him in Spanish, my words popped up on his phone screen in Cyrillic, and he translated them back to English for me out loud.
On the whole, the demo was pretty cool. If you’ve ever been lost in a foreign country whose language you don’t speak, imagine how handy a gadget like this would come in. Let’s just hope that once they’re more widespread, these products don’t end up discouraging people from learning languages.
Not to be outdone, Google also announced updates to its Translate product, which is being deployed at information desks in JFK airport’s international terminal, in sports stadiums in Qatar, and by some large hotel chains.
Stratuscent’s Digital Nose
AI is making steady progress towards achieving human-like vision and hearing—but there’s been less work done on mimicking our sense of smell (maybe because it’s less useful in everyday applications). Stratuscent’s digital nose, which it says is based on NASA patents, uses chemical receptors and AI to identify both simple chemicals and complex scents. The company is aiming to create the world’s first comprehensive database of everyday scents, which it says it will use to make “intelligent decisions” for customers. What kind of decisions remains to be seen—and smelled.
Banner Image Credit: The Mercedes Vision AVTR concept car. Photo by Vanessa Bates Ramirez Continue reading →
Posted in Human Robots
#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
#435167 A Closer Look at the Robots Helping Us ...
Buck Rogers had Twiki. Luke Skywalker palled around with C-3PO and R2-D2. And astronauts aboard the International Space Station (ISS) now have their own robotic companions in space—Astrobee.
A pair of the cube-shaped robots were launched to the ISS during an April re-supply mission and are currently being commissioned for use on the space station. The free-flying space robots, dubbed Bumble and Honey, are the latest generation of robotic machines to join the human crew on the ISS.
Exploration of the solar system and beyond will require autonomous machines that can assist humans with numerous tasks—or go where we cannot. NASA has said repeatedly that robots will be instrumental in future space missions to the moon, Mars, and even to the icy moon Europa.
The Astrobee robots will specifically test robotic capabilities in zero gravity, replacing the SPHERES (Synchronized Position Hold, Engage, Reorient, Experimental Satellite) robots that have been on the ISS for more than a decade to test various technologies ranging from communications to navigation.
The 18-sided robots, each about the size of a volleyball or an oversized Dungeons and Dragons die, use CO2-based cold-gas thrusters for movement and a series of ultrasonic beacons for orientation. The Astrobee robots, on the other hand, can propel themselves autonomously around the interior of the ISS using electric fans and six cameras.
The modular design of the Astrobee robots means they are highly plug-and-play, capable of being reconfigured with different hardware modules. The robots’ software is also open-source, encouraging scientists and programmers to develop and test new algorithms and features.
And, yes, the Astrobee robots will be busy as bees once they are fully commissioned this fall, with experiments planned to begin next year. Scientists hope to learn more about how robots can assist space crews and perform caretaking duties on spacecraft.
Robots Working Together
The Astrobee robots are expected to be joined by a familiar “face” on the ISS later this year—the humanoid robot Robonaut.
Robonaut, also known as R2, was the first US-built robot on the ISS. It joined the crew back in 2011 without legs, which were added in 2014. However, the installation never entirely worked, as R2 experienced power failures that eventually led to its return to Earth last year to fix the problem. If all goes as planned, the space station’s first humanoid robot will return to the ISS to lend a hand to the astronauts and the new robotic arrivals.
In particular, NASA is interested in how the two different robotic platforms can complement each other, with an eye toward outfitting the agency’s proposed lunar orbital space station with various robots that can supplement a human crew.
“We don’t have definite plans for what would happen on the Gateway yet, but there’s a general recognition that intra-vehicular robots are important for space stations,” Astrobee technical lead Trey Smith in the NASA Intelligent Robotics Group told IEEE Spectrum. “And so, it would not be surprising to see a mobile manipulator like Robonaut, and a free flyer like Astrobee, on the Gateway.”
While the focus on R2 has been to test its capabilities in zero gravity and to use it for mundane or dangerous tasks in space, the technology enabling the humanoid robot has proven to be equally useful on Earth.
For example, R2 has amazing dexterity for a robot, with sensors, actuators, and tendons comparable to the nerves, muscles, and tendons in a human hand. Based on that design, engineers are working on a robotic glove that can help factory workers, for instance, do their jobs better while reducing the risk of repetitive injuries. R2 has also inspired development of a robotic exoskeleton for both astronauts in space and paraplegics on Earth.
Working Hard on Soft Robotics
While innovative and technologically sophisticated, Astrobee and Robonaut are typical robots in that neither one would do well in a limbo contest. In other words, most robots are limited in their flexibility and agility based on current hardware and materials.
A subfield of robotics known as soft robotics involves developing robots with highly pliant materials that mimic biological organisms in how they move. Scientists at NASA’s Langley Research Center are investigating how soft robots could help with future space exploration.
Specifically, the researchers are looking at a series of properties to understand how actuators—components responsible for moving a robotic part, such as Robonaut’s hand—can be built and used in space.
The team first 3D prints a mold and then pours a flexible material like silicone into the mold. Air bladders or chambers in the actuator expand and compress using just air.
Some of the first applications of soft robotics sound more tool-like than R2-D2-like. For example, two soft robots could connect to produce a temporary shelter for astronauts on the moon or serve as an impromptu wind shield during one of Mars’ infamous dust storms.
The idea is to use soft robots in situations that are “dangerous, dirty, or dull,” according to Jack Fitzpatrick, a NASA intern working on the soft robotics project at Langley.
Working on Mars
Of course, space robots aren’t only designed to assist humans. In many instances, they are the only option to explore even relatively close celestial bodies like Mars. Four American-made robotic rovers have been used to investigate the fourth planet from the sun since 1997.
Opportunity is perhaps the most famous, covering about 25 miles of terrain across Mars over 15 years. A dust storm knocked it out of commission last year, with NASA officially ending the mission in February.
However, the biggest and baddest of the Mars rovers, Curiosity, is still crawling across the Martian surface, sending back valuable data since 2012. The car-size robot carries 17 cameras, a laser to vaporize rocks for study, and a drill to collect samples. It is on the hunt for signs of biological life.
The next year or two could see a virtual traffic jam of robots to Mars. NASA’s Mars 2020 Rover is next in line to visit the Red Planet, sporting scientific gadgets like an X-ray fluorescence spectrometer for chemical analyses and ground-penetrating radar to see below the Martian surface.
This diagram shows the instrument payload for the Mars 2020 mission. Image Credit: NASA.
Meanwhile, the Europeans have teamed with the Russians on a rover called Rosalind Franklin, named after a famed British chemist, that will drill down into the Martian ground for evidence of past or present life as soon as 2021.
The Chinese are also preparing to begin searching for life on Mars using robots as soon as next year, as part of the country’s Mars Global Remote Sensing Orbiter and Small Rover program. The mission is scheduled to be the first in a series of launches that would culminate with bringing samples back from Mars to Earth.
Perhaps there is no more famous utterance in the universe of science fiction as “to boldly go where no one has gone before.” However, the fact is that human exploration of the solar system and beyond will only be possible with robots of different sizes, shapes, and sophistication.
Image Credit: NASA. Continue reading →
Posted in Human Robots
#432671 Stuff 3.0: The Era of Programmable ...
It’s the end of a long day in your apartment in the early 2040s. You decide your work is done for the day, stand up from your desk, and yawn. “Time for a film!” you say. The house responds to your cues. The desk splits into hundreds of tiny pieces, which flow behind you and take on shape again as a couch. The computer screen you were working on flows up the wall and expands into a flat projection screen. You relax into the couch and, after a few seconds, a remote control surfaces from one of its arms.
In a few seconds flat, you’ve gone from a neatly-equipped office to a home cinema…all within the same four walls. Who needs more than one room?
This is the dream of those who work on “programmable matter.”
In his recent book about AI, Max Tegmark makes a distinction between three different levels of computational sophistication for organisms. Life 1.0 is single-celled organisms like bacteria; here, hardware is indistinguishable from software. The behavior of the bacteria is encoded into its DNA; it cannot learn new things.
Life 2.0 is where humans live on the spectrum. We are more or less stuck with our hardware, but we can change our software by choosing to learn different things, say, Spanish instead of Italian. Much like managing space on your smartphone, your brain’s hardware will allow you to download only a certain number of packages, but, at least theoretically, you can learn new behaviors without changing your underlying genetic code.
Life 3.0 marks a step-change from this: creatures that can change both their hardware and software in something like a feedback loop. This is what Tegmark views as a true artificial intelligence—one that can learn to change its own base code, leading to an explosion in intelligence. Perhaps, with CRISPR and other gene-editing techniques, we could be using our “software” to doctor our “hardware” before too long.
Programmable matter extends this analogy to the things in our world: what if your sofa could “learn” how to become a writing desk? What if, instead of a Swiss Army knife with dozens of tool attachments, you just had a single tool that “knew” how to become any other tool you could require, on command? In the crowded cities of the future, could houses be replaced by single, OmniRoom apartments? It would save space, and perhaps resources too.
Such are the dreams, anyway.
But when engineering and manufacturing individual gadgets is such a complex process, you can imagine that making stuff that can turn into many different items can be extremely complicated. Professor Skylar Tibbits at MIT referred to it as 4D printing in a TED Talk, and the website for his research group, the Self-Assembly Lab, excitedly claims, “We have also identified the key ingredients for self-assembly as a simple set of responsive building blocks, energy and interactions that can be designed within nearly every material and machining process available. Self-assembly promises to enable breakthroughs across many disciplines, from biology to material science, software, robotics, manufacturing, transportation, infrastructure, construction, the arts, and even space exploration.”
Naturally, their projects are still in the early stages, but the Self-Assembly Lab and others are genuinely exploring just the kind of science fiction applications we mooted.
For example, there’s the cell-phone self-assembly project, which brings to mind eerie, 24/7 factories where mobile phones assemble themselves from 3D printed kits without human or robotic intervention. Okay, so the phones they’re making are hardly going to fly off the shelves as fashion items, but if all you want is something that works, it could cut manufacturing costs substantially and automate even more of the process.
One of the major hurdles to overcome in making programmable matter a reality is choosing the right fundamental building blocks. There’s a very important balance to strike. To create fine details, you need to have things that aren’t too big, so as to keep your rearranged matter from being too lumpy. This might make the building blocks useless for certain applications—for example, if you wanted to make tools for fine manipulation. With big pieces, it might be difficult to simulate a range of textures. On the other hand, if the pieces are too small, different problems can arise.
Imagine a setup where each piece is a small robot. You have to contain the robot’s power source and its brain, or at least some kind of signal-generator and signal-processor, all in the same compact unit. Perhaps you can imagine that one might be able to simulate a range of textures and strengths by changing the strength of the “bond” between individual units—your desk might need to be a little bit more firm than your bed, which might be nicer with a little more give.
Early steps toward creating this kind of matter have been taken by those who are developing modular robots. There are plenty of different groups working on this, including MIT, Lausanne, and the University of Brussels.
In the latter configuration, one individual robot acts as a centralized decision-maker, referred to as the brain unit, but additional robots can autonomously join the brain unit as and when needed to change the shape and structure of the overall system. Although the system is only ten units at present, it’s a proof-of-concept that control can be orchestrated over a modular system of robots; perhaps in the future, smaller versions of the same thing could be the components of Stuff 3.0.
You can imagine that with machine learning algorithms, such swarms of robots might be able to negotiate obstacles and respond to a changing environment more easily than an individual robot (those of you with techno-fear may read “respond to a changing environment” and imagine a robot seamlessly rearranging itself to allow a bullet to pass straight through without harm).
Speaking of robotics, the form of an ideal robot has been a subject of much debate. In fact, one of the major recent robotics competitions—DARPA’s Robotics Challenge—was won by a robot that could adapt, beating Boston Dynamics’ infamous ATLAS humanoid with the simple addition of a wheel that allowed it to drive as well as walk.
Rather than building robots into a humanoid shape (only sometimes useful), allowing them to evolve and discover the ideal form for performing whatever you’ve tasked them to do could prove far more useful. This is particularly true in disaster response, where expensive robots can still be more valuable than humans, but conditions can be very unpredictable and adaptability is key.
Further afield, many futurists imagine “foglets” as the tiny nanobots that will be capable of constructing anything from raw materials, somewhat like the “Santa Claus machine.” But you don’t necessarily need anything quite so indistinguishable from magic to be useful. Programmable matter that can respond and adapt to its surroundings could be used in all kinds of industrial applications. How about a pipe that can strengthen or weaken at will, or divert its direction on command?
We’re some way off from being able to order our beds to turn into bicycles. As with many tech ideas, it may turn out that the traditional low-tech solution is far more practical and cost-effective, even as we can imagine alternatives. But as the march to put a chip in every conceivable object goes on, it seems certain that inanimate objects are about to get a lot more animated.
Image Credit: PeterVrabel / Shutterstock.com Continue reading →
Posted in Human Robots