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#439110 Robotic Exoskeletons Could One Day Walk ...
Engineers, using artificial intelligence and wearable cameras, now aim to help robotic exoskeletons walk by themselves.
Increasingly, researchers around the world are developing lower-body exoskeletons to help people walk. These are essentially walking robots users can strap to their legs to help them move.
One problem with such exoskeletons: They often depend on manual controls to switch from one mode of locomotion to another, such as from sitting to standing, or standing to walking, or walking on the ground to walking up or down stairs. Relying on joysticks or smartphone apps every time you want to switch the way you want to move can prove awkward and mentally taxing, says Brokoslaw Laschowski, a robotics researcher at the University of Waterloo in Canada.
Scientists are working on automated ways to help exoskeletons recognize when to switch locomotion modes — for instance, using sensors attached to legs that can detect bioelectric signals sent from your brain to your muscles telling them to move. However, this approach comes with a number of challenges, such as how how skin conductivity can change as a person’s skin gets sweatier or dries off.
Now several research groups are experimenting with a new approach: fitting exoskeleton users with wearable cameras to provide the machines with vision data that will let them operate autonomously. Artificial intelligence (AI) software can analyze this data to recognize stairs, doors, and other features of the surrounding environment and calculate how best to respond.
Laschowski leads the ExoNet project, the first open-source database of high-resolution wearable camera images of human locomotion scenarios. It holds more than 5.6 million images of indoor and outdoor real-world walking environments. The team used this data to train deep-learning algorithms; their convolutional neural networks can already automatically recognize different walking environments with 73 percent accuracy “despite the large variance in different surfaces and objects sensed by the wearable camera,” Laschowski notes.
According to Laschowski, a potential limitation of their work their reliance on conventional 2-D images, whereas depth cameras could also capture potentially useful distance data. He and his collaborators ultimately chose not to rely on depth cameras for a number of reasons, including the fact that the accuracy of depth measurements typically degrades in outdoor lighting and with increasing distance, he says.
In similar work, researchers in North Carolina had volunteers with cameras either mounted on their eyeglasses or strapped onto their knees walk through a variety of indoor and outdoor settings to capture the kind of image data exoskeletons might use to see the world around them. The aim? “To automate motion,” says Edgar Lobaton an electrical engineering researcher at North Carolina State University. He says they are focusing on how AI software might reduce uncertainty due to factors such as motion blur or overexposed images “to ensure safe operation. We want to ensure that we can really rely on the vision and AI portion before integrating it into the hardware.”
In the future, Laschowski and his colleagues will focus on improving the accuracy of their environmental analysis software with low computational and memory storage requirements, which are important for onboard, real-time operations on robotic exoskeletons. Lobaton and his team also seek to account for uncertainty introduced into their visual systems by movements .
Ultimately, the ExoNet researchers want to explore how AI software can transmit commands to exoskeletons so they can perform tasks such as climbing stairs or avoiding obstacles based on a system’s analysis of a user's current movements and the upcoming terrain. With autonomous cars as inspiration, they are seeking to develop autonomous exoskeletons that can handle the walking task without human input, Laschowski says.
However, Laschowski adds, “User safety is of the utmost importance, especially considering that we're working with individuals with mobility impairments,” resulting perhaps from advanced age or physical disabilities.
“The exoskeleton user will always have the ability to override the system should the classification algorithm or controller make a wrong decision.” Continue reading
Posted in Human Robots
#439105 This Robot Taught Itself to Walk in a ...
Recently, in a Berkeley lab, a robot called Cassie taught itself to walk, a little like a toddler might. Through trial and error, it learned to move in a simulated world. Then its handlers sent it strolling through a minefield of real-world tests to see how it’d fare.
And, as it turns out, it fared pretty damn well. With no further fine-tuning, the robot—which is basically just a pair of legs—was able to walk in all directions, squat down while walking, right itself when pushed off balance, and adjust to different kinds of surfaces.
It’s the first time a machine learning approach known as reinforcement learning has been so successfully applied in two-legged robots.
This likely isn’t the first robot video you’ve seen, nor the most polished.
For years, the internet has been enthralled by videos of robots doing far more than walking and regaining their balance. All that is table stakes these days. Boston Dynamics, the heavyweight champ of robot videos, regularly releases mind-blowing footage of robots doing parkour, back flips, and complex dance routines. At times, it can seem the world of iRobot is just around the corner.
This sense of awe is well-earned. Boston Dynamics is one of the world’s top makers of advanced robots.
But they still have to meticulously hand program and choreograph the movements of the robots in their videos. This is a powerful approach, and the Boston Dynamics team has done incredible things with it.
In real-world situations, however, robots need to be robust and resilient. They need to regularly deal with the unexpected, and no amount of choreography will do. Which is how, it’s hoped, machine learning can help.
Reinforcement learning has been most famously exploited by Alphabet’s DeepMind to train algorithms that thrash humans at some the most difficult games. Simplistically, it’s modeled on the way we learn. Touch the stove, get burned, don’t touch the damn thing again; say please, get a jelly bean, politely ask for another.
In Cassie’s case, the Berkeley team used reinforcement learning to train an algorithm to walk in a simulation. It’s not the first AI to learn to walk in this manner. But going from simulation to the real world doesn’t always translate.
Subtle differences between the two can (literally) trip up a fledgling robot as it tries out its sim skills for the first time.
To overcome this challenge, the researchers used two simulations instead of one. The first simulation, an open source training environment called MuJoCo, was where the algorithm drew upon a large library of possible movements and, through trial and error, learned to apply them. The second simulation, called Matlab SimMechanics, served as a low-stakes testing ground that more precisely matched real-world conditions.
Once the algorithm was good enough, it graduated to Cassie.
And amazingly, it didn’t need further polishing. Said another way, when it was born into the physical world—it knew how to walk just fine. In addition, it was also quite robust. The researchers write that two motors in Cassie’s knee malfunctioned during the experiment, but the robot was able to adjust and keep on trucking.
Other labs have been hard at work applying machine learning to robotics.
Last year Google used reinforcement learning to train a (simpler) four-legged robot. And OpenAI has used it with robotic arms. Boston Dynamics, too, will likely explore ways to augment their robots with machine learning. New approaches—like this one aimed at training multi-skilled robots or this one offering continuous learning beyond training—may also move the dial. It’s early yet, however, and there’s no telling when machine learning will exceed more traditional methods.
And in the meantime, Boston Dynamics bots are testing the commercial waters.
Still, robotics researchers, who were not part of the Berkeley team, think the approach is promising. Edward Johns, head of Imperial College London’s Robot Learning Lab, told MIT Technology Review, “This is one of the most successful examples I have seen.”
The Berkeley team hopes to build on that success by trying out “more dynamic and agile behaviors.” So, might a self-taught parkour-Cassie be headed our way? We’ll see.
Image Credit: University of California Berkeley Hybrid Robotics via YouTube Continue reading
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#439077 How Scientists Grew Human Muscles in Pig ...
The little pigs bouncing around the lab looked exceedingly normal. Yet their adorable exterior hid a remarkable secret: each piglet carried two different sets of genes. For now, both sets came from their own species. But one day, one of those sets may be human.
The piglets are chimeras—creatures with intermingled sets of genes, as if multiple entities were seamlessly mashed together. Named after the Greek lion-goat-serpent monsters, chimeras may hold the key to an endless supply of human organs and tissues for transplant. The crux is growing these human parts in another animal—one close enough in size and function to our own.
Last week, a team from the University of Minnesota unveiled two mind-bending chimeras. One was joyous little piglets, each propelled by muscles grown from a different pig. Another was pig embryos, transplanted into surrogate pigs, that developed human muscles for more than 20 days.
The study, led by Drs. Mary and Daniel Garry at the University of Minnesota, had a therapeutic point: engineering a brilliant way to replace muscle loss, especially for the muscles around our skeletons that allow us to move and navigate the world. Trauma and injury, such as from firearm wounds or car crashes, can damage muscle tissue beyond the point of repair. Unfortunately, muscles are also stubborn in that donor tissue from cadavers doesn’t usually “take” at the injury site. For now, there are no effective treatments for severe muscle death, called volumetric muscle loss.
The new human-pig hybrids are designed to tackle this problem. Muscle wasting aside, the study also points to a clever “hack” that increases the amount of human tissue inside a growing pig embryo.
If further improved, the technology could “provide an unlimited supply of organs for transplantation,” said Dr. Mary Garry to Inverse. What’s more, because the human tissue can be sourced from patients themselves, the risk of rejection by the immune system is relatively low—even when grown inside a pig.
“The shortage of organs for heart transplantation, vascular grafting, and skeletal muscle is staggering,” said Garry. Human-animal chimeras could have a “seismic impact” that transforms organ transplantation and helps solve the organ shortage crisis.
That is, if society accepts the idea of a semi-humanoid pig.
Wait…But How?
The new study took a page from previous chimera recipes.
The main ingredients and steps go like this: first, you need an embryo that lacks the ability to develop a tissue or organ. This leaves an “empty slot” of sorts that you can fill with another set of genes—pig, human, or even monkey.
Second, you need to fine-tune the recipe so that the embryos “take” the new genes, incorporating them into their bodies as if they were their own. Third, the new genes activate to instruct the growing embryo to make the necessary tissue or organs without harming the overall animal. Finally, the foreign genes need to stay put, without cells migrating to another body part—say, the brain.
Not exactly straightforward, eh? The piglets are technological wonders that mix cutting-edge gene editing with cloning technologies.
The team went for two chimeras: one with two sets of pig genes, the other with a pig and human mix. Both started with a pig embryo that can’t make its own skeletal muscles (those are the muscles surrounding your bones). Using CRISPR, the gene-editing Swiss Army Knife, they snipped out three genes that are absolutely necessary for those muscles to develop. Like hitting a bullseye with three arrows simultaneously, it’s already a technological feat.
Here’s the really clever part: the muscles around your bones have a slightly different genetic makeup than the ones that line your blood vessels or the ones that pump your heart. While the resulting pig embryos had severe muscle deformities as they developed, their hearts beat as normal. This means the gene editing cut only impacted skeletal muscles.
Then came step two: replacing the missing genes. Using a microneedle, the team injected a fertilized and slightly developed pig egg—called a blastomere—into the embryo. If left on its natural course, a blastomere eventually develops into another embryo. This step “smashes” the two sets of genes together, with the newcomer filling the muscle void. The hybrid embryo was then placed into a surrogate, and roughly four months later, chimeric piglets were born.
Equipped with foreign DNA, the little guys nevertheless seemed totally normal, nosing around the lab and running everywhere without obvious clumsy stumbles. Under the microscope, their “xenomorph” muscles were indistinguishable from run-of-the-mill average muscle tissue—no signs of damage or inflammation, and as stretchy and tough as muscles usually are. What’s more, the foreign DNA seemed to have only developed into muscles, even though they were prevalent across the body. Extensive fishing experiments found no trace of the injected set of genes inside blood vessels or the brain.
A Better Human-Pig Hybrid
Confident in their recipe, the team next repeated the experiment with human cells, with a twist. Instead of using controversial human embryonic stem cells, which are obtained from aborted fetuses, they relied on induced pluripotent stem cells (iPSCs). These are skin cells that have been reverted back into a stem cell state.
Unlike previous attempts at making human chimeras, the team then scoured the genetic landscape of how pig and human embryos develop to find any genetic “brakes” that could derail the process. One gene, TP53, stood out, which was then promptly eliminated with CRISPR.
This approach provides a way for future studies to similarly increase the efficiency of interspecies chimeras, the team said.
The human-pig embryos were then carefully grown inside surrogate pigs for less than a month, and extensively analyzed. By day 20, the hybrids had already grown detectable human skeletal muscle. Similar to the pig-pig chimeras, the team didn’t detect any signs that the human genes had sprouted cells that would eventually become neurons or other non-muscle cells.
For now, human-animal chimeras are not allowed to grow to term, in part to stem the theoretical possibility of engineering humanoid hybrid animals (shudder). However, a sentient human-pig chimera is something that the team specifically addressed. Through multiple experiments, they found no trace of human genes in the embryos’ brain stem cells 20 and 27 days into development. Similarly, human donor genes were absent in cells that would become the hybrid embryos’ reproductive cells.
Despite bioethical quandaries and legal restrictions, human-animal chimeras have taken off, both as a source of insight into human brain development and a well of personalized organs and tissues for transplant. In 2019, Japan lifted its ban on developing human brain cells inside animal embryos, as well as the term limit—to global controversy. There’s also the question of animal welfare, given that hybrid clones will essentially become involuntary organ donors.
As the debates rage on, scientists are nevertheless pushing the limits of human-animal chimeras, while treading as carefully as possible.
“Our data…support the feasibility of the generation of these interspecies chimeras, which will serve as a model for translational research or, one day, as a source for xenotransplantation,” the team said.
Image Credit: Christopher Carson on Unsplash Continue reading
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#439073 There’s a ‘New’ Nirvana Song Out, ...
One of the primary capabilities separating human intelligence from artificial intelligence is our ability to be creative—to use nothing but the world around us, our experiences, and our brains to create art. At present, AI needs to be extensively trained on human-made works of art in order to produce new work, so we’ve still got a leg up. That said, neural networks like OpenAI’s GPT-3 and Russian designer Nikolay Ironov have been able to create content indistinguishable from human-made work.
Now there’s another example of AI artistry that’s hard to tell apart from the real thing, and it’s sure to excite 90s alternative rock fans the world over: a brand-new, never-heard-before Nirvana song. Or, more accurately, a song written by a neural network that was trained on Nirvana’s music.
The song is called “Drowned in the Sun,” and it does have a pretty Nirvana-esque ring to it. The neural network that wrote it is Magenta, which was launched by Google in 2016 with the goal of training machines to create art—or as the tool’s website puts it, exploring the role of machine learning as a tool in the creative process. Magenta was built using TensorFlow, Google’s massive open-source software library focused on deep learning applications.
The song was written as part of an album called Lost Tapes of the 27 Club, a project carried out by a Toronto-based organization called Over the Bridge focused on mental health in the music industry.
Here’s how a computer was able to write a song in the unique style of a deceased musician. Music, 20 to 30 tracks, was fed into Magenta’s neural network in the form of MIDI files. MIDI stands for Musical Instrument Digital Interface, and the format contains the details of a song written in code that represents musical parameters like pitch and tempo. Components of each song, like vocal melody or rhythm guitar, were fed in one at a time.
The neural network found patterns in these different components, and got enough of a handle on them that when given a few notes to start from, it could use those patterns to predict what would come next; in this case, chords and melodies that sound like they could’ve been written by Kurt Cobain.
To be clear, Magenta didn’t spit out a ready-to-go song complete with lyrics. The AI wrote the music, but a different neural network wrote the lyrics (using essentially the same process as Magenta), and the team then sifted through “pages and pages” of output to find lyrics that fit the melodies Magenta created.
Eric Hogan, a singer for a Nirvana tribute band who the Over the Bridge team hired to sing “Drowned in the Sun,” felt that the lyrics were spot-on. “The song is saying, ‘I’m a weirdo, but I like it,’” he said. “That is total Kurt Cobain right there. The sentiment is exactly what he would have said.”
Cobain isn’t the only musician the Lost Tapes project tried to emulate; songs in the styles of Jimi Hendrix, Jim Morrison, and Amy Winehouse were also included. What all these artists have in common is that they died by suicide at the age of 27.
The project is meant to raise awareness around mental health, particularly among music industry professionals. It’s not hard to think of great artists of all persuasions—musicians, painters, writers, actors—whose lives are cut short due to severe depression and other mental health issues for which it can be hard to get help. These issues are sometimes romanticized, as suffering does tend to create art that’s meaningful, relatable, and timeless. But according to the Lost Tapes website, suicide attempts among music industry workers are more than double that of the general population.
How many more hit songs would these artists have written if they were still alive? We’ll never know, but hopefully Lost Tapes of the 27 Club and projects like it will raise awareness of mental health issues, both in the music industry and in general, and help people in need find the right resources. Because no matter how good computers eventually get at creating music, writing, or other art, as Lost Tapes’ website pointedly says, “Even AI will never replace the real thing.”
Image Credit: Edward Xu on Unsplash Continue reading
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