Tag Archives: fail

#437982 Superintelligent AI May Be Impossible to ...

It may be theoretically impossible for humans to control a superintelligent AI, a new study finds. Worse still, the research also quashes any hope for detecting such an unstoppable AI when it’s on the verge of being created.

Slightly less grim is the timetable. By at least one estimate, many decades lie ahead before any such existential computational reckoning could be in the cards for humanity.

Alongside news of AI besting humans at games such as chess, Go and Jeopardy have come fears that superintelligent machines smarter than the best human minds might one day run amok. “The question about whether superintelligence could be controlled if created is quite old,” says study lead author Manuel Alfonseca, a computer scientist at the Autonomous University of Madrid. “It goes back at least to Asimov’s First Law of Robotics, in the 1940s.”

The Three Laws of Robotics, first introduced in Isaac Asimov's 1942 short story “Runaround,” are as follows:

A robot may not injure a human being or, through inaction, allow a human being to come to harm.
A robot must obey the orders given it by human beings except where such orders would conflict with the First Law.
A robot must protect its own existence as long as such protection does not conflict with the First or Second Laws.

In 2014, philosopher Nick Bostrom, director of the Future of Humanity Institute at the University of Oxford, not only explored ways in which a superintelligent AI could destroy us but also investigated potential control strategies for such a machine—and the reasons they might not work.

Bostrom outlined two possible types of solutions of this “control problem.” One is to control what the AI can do, such as keeping it from connecting to the Internet, and the other is to control what it wants to do, such as teaching it rules and values so it would act in the best interests of humanity. The problem with the former is that Bostrom thought a supersmart machine could probably break free from any bonds we could make. With the latter, he essentially feared that humans might not be smart enough to train a superintelligent AI.

Now Alfonseca and his colleagues suggest it may be impossible to control a superintelligent AI, due to fundamental limits inherent to computing itself. They detailed their findings this month in the Journal of Artificial Intelligence Research.

The researchers suggested that any algorithm that sought to ensure a superintelligent AI cannot harm people had to first simulate the machine’s behavior to predict the potential consequences of its actions. This containment algorithm then would need to halt the supersmart machine if it might indeed do harm.

However, the scientists said it was impossible for any containment algorithm to simulate the AI’s behavior and predict with absolute certainty whether its actions might lead to harm. The algorithm could fail to correctly simulate the AI’s behavior or accurately predict the consequences of the AI’s actions and not recognize such failures.

“Asimov’s first law of robotics has been proved to be incomputable,” Alfonseca says, “and therefore unfeasible.”

We may not even know if we have created a superintelligent machine, the researchers say. This is a consequence of Rice’s theorem, which essentially states that one cannot in general figure anything out about what a computer program might output just by looking at the program, Alfonseca explains.

On the other hand, there’s no need to spruce up the guest room for our future robot overlords quite yet. Three important caveats to the research still leave plenty of uncertainty to the group’s predictions.

First, Alfonseca estimates AI’s moment of truth remains, he says, “At least two centuries in the future.”

Second, he says researchers do not know if so-called artificial general intelligence, also known as strong AI, is theoretically even feasible. “That is, a machine as intelligent as we are in an ample variety of fields,” Alfonseca explains.

Last, Alfonseca says, “We have not proved that superintelligences can never be controlled—only that they can’t always be controlled.”

Although it may not be possible to control a superintelligent artificial general intelligence, it should be possible to control a superintelligent narrow AI—one specialized for certain functions instead of being capable of a broad range of tasks like humans. “We already have superintelligences of this type,” Alfonseca says. “For instance, we have machines that can compute mathematics much faster than we can. This is [narrow] superintelligence, isn’t it?” Continue reading

Posted in Human Robots

#437957 Meet Assembloids, Mini Human Brains With ...

It’s not often that a twitching, snowman-shaped blob of 3D human tissue makes someone’s day.

But when Dr. Sergiu Pasca at Stanford University witnessed the tiny movement, he knew his lab had achieved something special. You see, the blob was evolved from three lab-grown chunks of human tissue: a mini-brain, mini-spinal cord, and mini-muscle. Each individual component, churned to eerie humanoid perfection inside bubbling incubators, is already a work of scientific genius. But Pasca took the extra step, marinating the three components together inside a soup of nutrients.

The result was a bizarre, Lego-like human tissue that replicates the basic circuits behind how we decide to move. Without external prompting, when churned together like ice cream, the three ingredients physically linked up into a fully functional circuit. The 3D mini-brain, through the information highway formed by the artificial spinal cord, was able to make the lab-grown muscle twitch on demand.

In other words, if you think isolated mini-brains—known formally as brain organoids—floating in a jar is creepy, upgrade your nightmares. The next big thing in probing the brain is assembloids—free-floating brain circuits—that now combine brain tissue with an external output.

The end goal isn’t to freak people out. Rather, it’s to recapitulate our nervous system, from input to output, inside the controlled environment of a Petri dish. An autonomous, living brain-spinal cord-muscle entity is an invaluable model for figuring out how our own brains direct the intricate muscle movements that allow us stay upright, walk, or type on a keyboard.

It’s the nexus toward more dexterous brain-machine interfaces, and a model to understand when brain-muscle connections fail—as in devastating conditions like Lou Gehrig’s disease or Parkinson’s, where people slowly lose muscle control due to the gradual death of neurons that control muscle function. Assembloids are a sort of “mini-me,” a workaround for testing potential treatments on a simple “replica” of a person rather than directly on a human.

From Organoids to Assembloids
The miniature snippet of the human nervous system has been a long time in the making.

It all started in 2014, when Dr. Madeleine Lancaster, then a post-doc at Stanford, grew a shockingly intricate 3D replica of human brain tissue inside a whirling incubator. Revolutionarily different than standard cell cultures, which grind up brain tissue to reconstruct as a flat network of cells, Lancaster’s 3D brain organoids were incredibly sophisticated in their recapitulation of the human brain during development. Subsequent studies further solidified their similarity to the developing brain of a fetus—not just in terms of neuron types, but also their connections and structure.

With the finding that these mini-brains sparked with electrical activity, bioethicists increasingly raised red flags that the blobs of human brain tissue—no larger than the size of a pea at most—could harbor the potential to develop a sense of awareness if further matured and with external input and output.

Despite these concerns, brain organoids became an instant hit. Because they’re made of human tissue—often taken from actual human patients and converted into stem-cell-like states—organoids harbor the same genetic makeup as their donors. This makes it possible to study perplexing conditions such as autism, schizophrenia, or other brain disorders in a dish. What’s more, because they’re grown in the lab, it’s possible to genetically edit the mini-brains to test potential genetic culprits in the search for a cure.

Yet mini-brains had an Achilles’ heel: not all were made the same. Rather, depending on the region of the brain that was reverse engineered, the cells had to be persuaded by different cocktails of chemical soups and maintained in isolation. It was a stark contrast to our own developing brains, where regions are connected through highways of neural networks and work in tandem.

Pasca faced the problem head-on. Betting on the brain’s self-assembling capacity, his team hypothesized that it might be possible to grow different mini-brains, each reflecting a different brain region, and have them fuse together into a synchronized band of neuron circuits to process information. Last year, his idea paid off.

In one mind-blowing study, his team grew two separate portions of the brain into blobs, one representing the cortex, the other a deeper part of the brain known to control reward and movement, called the striatum. Shockingly, when put together, the two blobs of human brain tissue fused into a functional couple, automatically establishing neural highways that resulted in one of the most sophisticated recapitulations of a human brain. Pasca crowned this tissue engineering crème-de-la-crème “assembloids,” a portmanteau between “assemble” and “organoids.”

“We have demonstrated that regionalized brain spheroids can be put together to form fused structures called brain assembloids,” said Pasca at the time.” [They] can then be used to investigate developmental processes that were previously inaccessible.”

And if that’s possible for wiring up a lab-grown brain, why wouldn’t it work for larger neural circuits?

Assembloids, Assemble
The new study is the fruition of that idea.

The team started with human skin cells, scraped off of eight healthy people, and transformed them into a stem-cell-like state, called iPSCs. These cells have long been touted as the breakthrough for personalized medical treatment, before each reflects the genetic makeup of its original host.

Using two separate cocktails, the team then generated mini-brains and mini-spinal cords using these iPSCs. The two components were placed together “in close proximity” for three days inside a lab incubator, gently floating around each other in an intricate dance. To the team’s surprise, under the microscope using tracers that glow in the dark, they saw highways of branches extending from one organoid to the other like arms in a tight embrace. When stimulated with electricity, the links fired up, suggesting that the connections weren’t just for show—they’re capable of transmitting information.

“We made the parts,” said Pasca, “but they knew how to put themselves together.”

Then came the ménage à trois. Once the mini-brain and spinal cord formed their double-decker ice cream scoop, the team overlaid them onto a layer of muscle cells—cultured separately into a human-like muscular structure. The end result was a somewhat bizarre and silly-looking snowman, made of three oddly-shaped spherical balls.

Yet against all odds, the brain-spinal cord assembly reached out to the lab-grown muscle. Using a variety of tools, including measuring muscle contraction, the team found that this utterly Frankenstein-like snowman was able to make the muscle component contract—in a way similar to how our muscles twitch when needed.

“Skeletal muscle doesn’t usually contract on its own,” said Pasca. “Seeing that first twitch in a lab dish immediately after cortical stimulation is something that’s not soon forgotten.”

When tested for longevity, the contraption lasted for up to 10 weeks without any sort of breakdown. Far from a one-shot wonder, the isolated circuit worked even better the longer each component was connected.

Pasca isn’t the first to give mini-brains an output channel. Last year, the queen of brain organoids, Lancaster, chopped up mature mini-brains into slices, which were then linked to muscle tissue through a cultured spinal cord. Assembloids are a step up, showing that it’s possible to automatically sew multiple nerve-linked structures together, such as brain and muscle, sans slicing.

The question is what happens when these assembloids become more sophisticated, edging ever closer to the inherent wiring that powers our movements. Pasca’s study targets outputs, but what about inputs? Can we wire input channels, such as retinal cells, to mini-brains that have a rudimentary visual cortex to process those examples? Learning, after all, depends on examples of our world, which are processed inside computational circuits and delivered as outputs—potentially, muscle contractions.

To be clear, few would argue that today’s mini-brains are capable of any sort of consciousness or awareness. But as mini-brains get increasingly more sophisticated, at what point can we consider them a sort of AI, capable of computation or even something that mimics thought? We don’t yet have an answer—but the debates are on.

Image Credit: christitzeimaging.com / Shutterstock.com Continue reading

Posted in Human Robots

#437709 iRobot Announces Major Software Update, ...

Since the release of the very first Roomba in 2002, iRobot’s long-term goal has been to deliver cleaner floors in a way that’s effortless and invisible. Which sounds pretty great, right? And arguably, iRobot has managed to do exactly this, with its most recent generation of robot vacuums that make their own maps and empty their own dustbins. For those of us who trust our robots, this is awesome, but iRobot has gradually been realizing that many Roomba users either don’t want this level of autonomy, or aren’t ready for it.

Today, iRobot is announcing a major new update to its app that represents a significant shift of its overall approach to home robot autonomy. Humans are being brought back into the loop through software that tries to learn when, where, and how you clean so that your Roomba can adapt itself to your life rather than the other way around.

To understand why this is such a shift for iRobot, let’s take a very brief look back at how the Roomba interface has evolved over the last couple of decades. The first generation of Roomba had three buttons on it that allowed (or required) the user to select whether the room being vacuumed was small or medium or large in size. iRobot ditched that system one generation later, replacing the room size buttons with one single “clean” button. Programmable scheduling meant that users no longer needed to push any buttons at all, and with Roombas able to find their way back to their docking stations, all you needed to do was empty the dustbin. And with the most recent few generations (the S and i series), the dustbin emptying is also done for you, reducing direct interaction with the robot to once a month or less.

Image: iRobot

iRobot CEO Colin Angle believes that working toward more intelligent human-robot collaboration is “the brave new frontier” of AI. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” he says. “But thinking that autonomy was the destination was where I was just completely wrong.”

The point that the top-end Roombas are at now reflects a goal that iRobot has been working toward since 2002: With autonomy, scheduling, and the clean base to empty the bin, you can set up your Roomba to vacuum when you’re not home, giving you cleaner floors every single day without you even being aware that the Roomba is hard at work while you’re out. It’s not just hands-off, it’s brain-off. No noise, no fuss, just things being cleaner thanks to the efforts of a robot that does its best to be invisible to you. Personally, I’ve been completely sold on this idea for home robots, and iRobot CEO Colin Angle was as well.

“I probably told you that the perfect Roomba is the Roomba that you never see, you never touch, you just come home everyday and it’s done the right thing,” Angle told us. “But customers don’t want that—they want to be able to control what the robot does. We started to hear this a couple years ago, and it took a while before it sunk in, but it made sense.”

How? Angle compares it to having a human come into your house to clean, but you weren’t allowed to tell them where or when to do their job. Maybe after a while, you’ll build up the amount of trust necessary for that to work, but in the short term, it would likely be frustrating. And people get frustrated with their Roombas for this reason. “The desire to have more control over what the robot does kept coming up, and for me, it required a pretty big shift in my view of what intelligence we were trying to build. Autonomy is not intelligence. We need to do something more.”

That something more, Angle says, is a partnership as opposed to autonomy. It’s an acknowledgement that not everyone has the same level of trust in robots as the people who build them. It’s an understanding that people want to have a feeling of control over their homes, that they have set up the way that they want, and that they’ve been cleaning the way that they want, and a robot shouldn’t just come in and do its own thing.

This change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware.

“Until the robot proves that it knows enough about your home and about the way that you want your home cleaned,” Angle says, “you can’t move forward.” He adds that this is one of those things that seem obvious in retrospect, but even if they’d wanted to address the issue before, they didn’t have the technology to solve the problem. Now they do. “This whole journey has been earning the right to take this next step, because a robot can’t be responsive if it’s incompetent,” Angle says. “But thinking that autonomy was the destination was where I was just completely wrong.”

The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.

Where to Clean
Knowing where to clean depends on your Roomba having a detailed and accurate map of its environment. For several generations now, Roombas have been using visual mapping and localization (VSLAM) to build persistent maps of your home. These maps have been used to tell the Roomba to clean in specific rooms, but that’s about it. With the new update, Roombas with cameras will be able to recognize some objects and features in your home, including chairs, tables, couches, and even countertops. The robots will use these features to identify where messes tend to happen so that they can focus on those areas—like around the dining room table or along the front of the couch.

We should take a minute here to clarify how the Roomba is using its camera. The original (primary?) purpose of the camera was for VSLAM, where the robot would take photos of your home, downsample them into QR-code-like patterns of light and dark, and then use those (with the assistance of other sensors) to navigate. Now the camera is also being used to take pictures of other stuff around your house to make that map more useful.

Photo: iRobot

The robots will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table.

This is done through machine learning using a library of images of common household objects from a floor perspective that iRobot had to develop from scratch. Angle clarified for us that this is all done via a neural net that runs on the robot, and that “no recognizable images are ever stored on the robot or kept, and no images ever leave the robot.” Worst case, if all the data iRobot has about your home gets somehow stolen, the hacker would only know that (for example) your dining room has a table in it and the approximate size and location of that table, because the map iRobot has of your place only stores symbolic representations rather than images.

Another useful new feature is intended to help manage the “evil Roomba places” (as Angle puts it) that every home has that cause Roombas to get stuck. If the place is evil enough that Roomba has to call you for help because it gave up completely, Roomba will now remember, and suggest that either you make some changes or that it stops cleaning there, which seems reasonable.

When to Clean
It turns out that the primary cause of mission failure for Roombas is not that they get stuck or that they run out of battery—it’s user cancellation, usually because the robot is getting in the way or being noisy when you don’t want it to be. “If you kill a Roomba’s job because it annoys you,” points out Angle, “how is that robot being a good partner? I think it’s an epic fail.” Of course, it’s not the robot’s fault, because Roombas only clean when we tell them to, which Angle says is part of the problem. “People actually aren’t very good at making their own schedules—they tend to oversimplify, and not think through what their schedules are actually about, which leads to lots of [figurative] Roomba death.”

To help you figure out when the robot should actually be cleaning, the new app will look for patterns in when you ask the robot to clean, and then recommend a schedule based on those patterns. That might mean the robot cleans different areas at different times every day of the week. The app will also make scheduling recommendations that are event-based as well, integrated with other smart home devices. Would you prefer the Roomba to clean every time you leave the house? The app can integrate with your security system (or garage door, or any number of other things) and take care of that for you.

More generally, Roomba will now try to fit into the kinds of cleaning routines that many people already have established. For example, the app may suggest an “after dinner” routine that cleans just around the kitchen and dining room table. The app will also, to some extent, pay attention to the environment and season. It might suggest increasing your vacuuming frequency if pollen counts are especially high, or if it’s pet shedding season and you have a dog. Unfortunately, Roomba isn’t (yet?) capable of recognizing dogs on its own, so the app has to cheat a little bit by asking you some basic questions.

A Smarter App

Image: iRobot

The previous iteration of the iRobot app (and Roombas themselves) are built around one big fat CLEAN button. The new approach instead tries to figure out in much more detail where the robot should clean, and when, using a mixture of autonomous technology and interaction with the user.

The app update, which should be available starting today, is free. The scheduling and recommendations will work on every Roomba model, although for object recognition and anything related to mapping, you’ll need one of the more recent and fancier models with a camera. Future app updates will happen on a more aggressive schedule. Major app releases should happen every six months, with incremental updates happening even more frequently than that.

Angle also told us that overall, this change in direction also represents a substantial shift in resources for iRobot, and the company has pivoted two-thirds of its engineering organization to focus on software-based collaborative intelligence rather than hardware. “It’s not like we’re done doing hardware,” Angle assured us. “But we do think about hardware differently. We view our robots as platforms that have longer life cycles, and each platform will be able to support multiple generations of software. We’ve kind of decoupled robot intelligence from hardware, and that’s a change.”

Angle believes that working toward more intelligent collaboration between humans and robots is “the brave new frontier of artificial intelligence. I expect it to be the frontier for a reasonable amount of time to come,” he adds. “We have a lot of work to do to create the type of easy-to-use experience that consumer robots need.” Continue reading

Posted in Human Robots

#437639 Boston Dynamics’ Spot Is Helping ...

In terms of places where you absolutely want a robot to go instead of you, what remains of the utterly destroyed Chernobyl Reactor 4 should be very near the top of your list. The reactor, which suffered a catastrophic meltdown in 1986, has been covered up in almost every way possible in an effort to keep its nuclear core contained. But eventually, that nuclear material is going to have to be dealt with somehow, and in order to do that, it’s important to understand which bits of it are just really bad, and which bits are the actual worst. And this is where Spot is stepping in to help.

The big open space that Spot is walking through is right next to what’s left of Reactor 4. Within six months of the disaster, Reactor 4 was covered in a sarcophagus made of concrete and steel to try and keep all the nasty nuclear fuel from leaking out more than it already had, and it still contains “30 tons of highly contaminated dust, 16 tons of uranium and plutonium, and 200 tons of radioactive lava.” Oof. Over the next 10 years, the sarcophagus slowly deteriorated, and despite the addition of that gigantic network of steel support beams that you can see in the video, in the late 1990s it was decided to erect an enormous building over the entire mess to try and stabilize it for as long as possible.

Reactor 4 is now snugly inside the massive New Safe Confinement (NSC) structure, and the idea is that eventually, the structure will allow for the safe disassembly of what’s left of the reactor, although nobody is quite sure how to do that. This is all just to say that the area inside of the containment structure offers a lot of good opportunities for robots to take over from humans.

This particular Spot is owned by the U.K. Atomic Energy Authority, and was packed off to Russia with the assistance of the Robotics and Artificial Intelligence in Nuclear (RAIN) initiative and the National Centre for Nuclear Robotics. Dr. Dave Megson-Smith, who is a researcher at the University of Bristol, in the U.K., and part of the Hot Robotics Facility at the National Nuclear User Facility, was one of the scientists lucky enough to accompany Spot on its adventure. Megson-Smith specializes in sensor development, and he equipped Spot with a collimated radiation sensor in addition to its mapping payload. “We actually built a map of the radiation coming out of the front wall of Chernobyl power plant as we were in there with it,” Megson-Smith told us, and was able to share this picture, which shows a map of gamma photon count rate:

Image: University of Bristol

Researchers equipped Spot with a collimated radiation sensor and use one of the data readings (gamma photon count rate) to create a map of the radiation coming out of the front wall of the Chernobyl power plant.

So what’s the reason you’d want to use a very expensive legged robot to wander around what looks like a very flat and robot friendly floor? As it turns out, the floor is very dusty in there, and a priority inside the NSC is to keep dust down as much as possible, since the dust is radioactive and gets on everything and is consequently the easiest way for radioactivity to escape the NSC. “You want to minimize picking up material, so we consider the total contact surface area,” says Megson-Smith. “If you use a legged system rather than a wheeled or tracked system, you have a much smaller footprint and you disturb the environment a lot less.” While it’s nice that Spot is nimble and can climb stairs and stuff, tracked vehicles can do that as well, so in this case, the primary driving factor of choosing a robot to work inside Chernobyl is minimizing those contact points.

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker”

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker” able to work in medium level contaminated environments.” As far as more dangerous areas go, there’s a lot of uncertainty about what Spot is actually capable of, according to Megson-Smith. “What you think the problems are, and what the industry thinks the problems are, are subtly different things.

We were thinking that we’d have to make robots incredibly radiation proof to go into these contaminated environments, but they said, “can you just give us a system that we can send into places where humans already can go, but where we just don’t want to send humans.” Making robots incredibly radiation proof is challenging, and without extensive testing and ruggedizing, failures can be frequent, as many robots discovered at Fukushima. Indeed, Megson-Smith that in Fukushima there’s a particular section that’s known as a “robot graveyard” where robots just go to die, and they’ve had to up their standards again and again to keep the robots from failing. “So the thing they’re worried about with Spot is, what is its tolerance? What components will fail, and what can we do to harden it?” he says. “We’re approaching Boston Dynamics at the moment to see if they’ll work with us to address some of those questions.

There’s been a small amount of testing of how robots fair under harsh radiation, Megson-Smith told us, including (relatively recently) a KUKA LBR800 arm, which “stopped operating after a large radiation dose of 164.55(±1.09) Gy to its end effector, and the component causing the failure was an optical encoder.” And in case you’re wondering how much radiation that is, a 1 to 2 Gy dose to the entire body gets you acute radiation sickness and possibly death, while 8 Gy is usually just straight-up death. The goal here is not to kill robots (I mean, it sort of is), but as Megson-Smith says, “if we can work out what the weak points are in a robotic system, can we address those, can we redesign those, or at least understand when they might start to fail?” Now all he has to do is convince Boston Dynamics to send them a Spot that they can zap until it keels over.

The goal for Spot in the short term is fully autonomous radiation mapping, which seems very possible. It’ll also get tested with a wider range of sensor packages, and (happily for the robot) this will all take place safely back at home in the U.K. As far as Chernobyl is concerned, robots will likely have a substantial role to play in the near future. “Ultimately, Chernobyl has to be taken apart and decommissioned. That’s the long-term plan for the facility. To do that, you first need to understand everything, which is where we come in with our sensor systems and robotic platforms,” Megson-Smith tells us. “Since there are entire swathes of the Chernobyl nuclear plant where people can’t go in, we’d need robots like Spot to do those environmental characterizations.” Continue reading

Posted in Human Robots

#437628 Video Friday: An In-Depth Look at Mesmer ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online]
IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

Bear Robotics, a robotics and artificial intelligence company, and SoftBank Robotics Group, a leading robotics manufacturer and solutions provider, have collaborated to bring a new robot named Servi to the food service and hospitality field.

[ Bear Robotics ]

A literal in-depth look at Engineered Arts’ Mesmer android.

[ Engineered Arts ]

Is your robot running ROS? Is it connected to the Internet? Are you actually in control of it right now? Are you sure?

I appreciate how the researchers admitted to finding two of their own robots as part of the scan, a Baxter and a drone.

[ Brown ]

Smile Robotics describes this as “(possibly) world’s first full-autonomous clear-up-the-table robot.”

We’re not qualified to make a judgement on the world firstness, but personally I hate clearing tables, so this robot has my vote.

Smile Robotics founder and CEO Takashi Ogura, along with chief engineer Mitsutaka Kabasawa and engineer Kazuya Kobayashi, are former Google roboticists. Ogura also worked at SCHAFT. Smile says its robot uses ROS and is controlled by a framework written mainly in Rust, adding: “We are hiring Rustacean Roboticists!”

[ Smile Robotics ]

We’re not entirely sure why, but Panasonic has released plans for an Internet of Things system for hamsters.

We devised a recipe for a “small animal healthcare device” that can measure the weight and activity of small animals, the temperature and humidity of the breeding environment, and manage their health. This healthcare device visualizes the health status and breeding environment of small animals and manages their health to promote early detection of diseases. While imagining the scene where a healthcare device is actually used for an important small animal that we treat with affection, we hope to help overcome the current difficult situation through manufacturing.

[ Panasonic ] via [ RobotStart ]

Researchers at Yale have developed a robotic fabric, a breakthrough that could lead to such innovations as adaptive clothing, self-deploying shelters, or lightweight shape-changing machinery.

The researchers focused on processing functional materials into fiber-form so they could be integrated into fabrics while retaining its advantageous properties. For example, they made variable stiffness fibers out of an epoxy embedded with particles of Field’s metal, an alloy that liquifies at relatively low temperatures. When cool, the particles are solid metal and make the material stiffer; when warm, the particles melt into liquid and make the material softer.

[ Yale ]

In collaboration with Armasuisse and SBB, RSL demonstrated the use of a teleoperated Menzi Muck M545 to clean up a rock slide in Central Switzerland. The machine can be operated from a teloperation platform with visual and motion feedback. The walking excavator features an active chassis that can adapt to uneven terrain.

[ ETHZ RSL ]

An international team of JKU researchers is continuing to develop their vision for robots made out of soft materials. A new article in the journal “Communications Materials” demonstrates just how these kinds of soft machines react using weak magnetic fields to move very quickly. A triangle-shaped robot can roll itself in air at high speed and walk forward when exposed to an alternating in-plane square wave magnetic field (3.5 mT, 1.5 Hz). The diameter of the robot is 18 mm with a thickness of 80 µm. A six-arm robot can grab, transport, and release non-magnetic objects such as a polyurethane foam cube controlled by a permanent magnet.

Okay but tell me more about that cute sheep.

[ JKU ]

Interbotix has this “research level robotic crawler,” which both looks mean and runs ROS, a dangerous combination.

And here’s how it all came together:

[ Interbotix ]

I guess if you call them “loitering missile systems” rather than “drones that blow things up” people are less likely to get upset?

[ AeroVironment ]

In this video, we show a planner for a master dual-arm robot to manipulate tethered tools with an assistant dual-arm robot’s help. The assistant robot provides assistance to the master robot by manipulating the tool cable and avoiding collisions. The provided assistance allows the master robot to perform tool placements on the robot workspace table to regrasp the tool, which would typically fail since the tool cable tension may change the tool positions. It also allows the master robot to perform tool handovers, which would normally cause entanglements or collisions with the cable and the environment without the assistance.

[ Harada Lab ]

This video shows a flexible and robust robotic system for autonomous drawing on 3D surfaces. The system takes 2D drawing strokes and a 3D target surface (mesh or point clouds) as input. It maps the 2D strokes onto the 3D surface and generates a robot motion to draw the mapped strokes using visual recognition, grasp pose reasoning, and motion planning.

[ Harada Lab ]

Weekly mobility test. This time the Warthog takes on a fallen tree. Will it cross it? The answer is in the video!

And the answer is: kinda?

[ NORLAB ]

One of the advantages of walking machines is their ability to apply forces in all directions and of various magnitudes to the environment. Many of the multi-legged robots are equipped with point contact feet as these simplify the design and control of the robot. The iStruct project focuses on the development of a foot that allows extensive contact with the environment.

[ DFKI ]

An urgent medical transport was simulated in NASA’s second Systems Integration and Operationalization (SIO) demonstration Sept. 28 with partner Bell Textron Inc. Bell used the remotely-piloted APT 70 to conduct a flight representing an urgent medical transport mission. It is envisioned in the future that an operational APT 70 could provide rapid medical transport for blood, organs, and perishable medical supplies (payload up to 70 pounds). The APT 70 is estimated to move three times as fast as ground transportation.

Always a little suspicious when the video just shows the drone flying, and sitting on the ground, but not that tricky transition between those two states.

[ NASA ]

A Lockheed Martin Robotics Seminar on “Socially Assistive Mobile Robots,” by Yi Guo from Stevens Institute of Technology.

The use of autonomous mobile robots in human environments is on the rise. Assistive robots have been seen in real-world environments, such as robot guides in airports, robot polices in public parks, and patrolling robots in supermarkets. In this talk, I will first present current research activities conducted in the Robotics and Automation Laboratory at Stevens. I’ll then focus on robot-assisted pedestrian regulation, where pedestrian flows are regulated and optimized through passive human-robot interaction.

[ UMD ]

This week’s CMU RI Seminar is by CMU’s Zachary Manchester, on “The World’s Tiniest Space Program.”

The aerospace industry has experienced a dramatic shift over the last decade: Flying a spacecraft has gone from something only national governments and large defense contractors could afford to something a small startup can accomplish on a shoestring budget. A virtuous cycle has developed where lower costs have led to more launches and the growth of new markets for space-based data. However, many barriers remain. This talk will focus on driving these trends to their ultimate limit by harnessing advances in electronics, planning, and control to build spacecraft that cost less than a new smartphone and can be deployed in large numbers.

[ CMU RI ] Continue reading

Posted in Human Robots