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#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
#439081 Classify This Robot-Woven Sneaker With ...
For athletes trying to run fast, the right shoe can be essential to achieving peak performance. For athletes trying to run fast as humanly possible, a runner’s shoe can also become a work of individually customized engineering.
This is why Adidas has married 3D printing with robotic automation in a mass-market footwear project it’s called Futurecraft.Strung, expected to be available for purchase as soon as later this year. Using a customized, 3D-printed sole, a Futurecraft.Strung manufacturing robot can place some 2,000 threads from up to 10 different sneaker yarns in one upper section of the shoe.
Skylar Tibbits, founder and co-director of the Self-Assembly Lab and associate professor in MIT's Department of Architecture, says that because of its small scale, footwear has been an area of focus for 3D printing and additive manufacturing, which involves adding material bit by bit.
“There are really interesting complex geometry problems,” he says. “It’s pretty well suited.”
Photo: Adidas
Beginning with a 3D-printed sole, Adidas robots weave together some 2000 threads from up to 10 different sneaker yarns to make one Futurecraft.Strung shoe—expected on the marketplace later this year or sometime in 2022.
Adidas began working on the Futurecraft.Strung project in 2016. Then two years later, Adidas Futurecraft, the company’s innovation incubator, began collaborating with digital design studio Kram/Weisshaar. In less than a year the team built the software and hardware for the upper part of the shoe, called Strung uppers.
“Most 3D printing in the footwear space has been focused on the midsole or outsole, like the bottom of the shoe,” Tibbits explains. But now, he says, Adidas is bringing robotics and a threaded design to the upper part of the shoe. The company bases its Futurecraft.Strung design on high-resolution scans of how runners’ feet move as they travel.
This more flexible design can benefit athletes in multiple sports, according to an Adidas blog post. It will be able to use motion capture of an athlete’s foot and feedback from the athlete to make the design specific to the athlete’s specific gait. Adidas customizes the weaving of the shoe’s “fabric” (really more like an elaborate woven string figure, a cat’s cradle to fit the foot) to achieve a close and comfortable fit, the company says.
What they call their “4D sole” consists of a design combining 3D printing with materials that can change their shape and properties over time. In fact, Tibbits coined the term 4D printing to describe this process in 2013. The company takes customized data from the Adidas Athlete Intelligent Engine to make the shoe, according to Kram/Weisshaar’s website.
Photo: Adidas
Closeup of the weaving process behind a Futurecraft.Strung shoe
“With Strung for the first time, we can program single threads in any direction, where each thread has a different property or strength,” Fionn Corcoran-Tadd, an innovation designer at Adidas’ Futurecraft lab, said in a company video. Each thread serves a purpose, the video noted. “This is like customized string art for your feet,” Tibbits says.
Although the robotics technology the company uses has been around for many years, what Adidas’s robotic weavers can achieve with thread is a matter of elaborate geometry. “It’s more just like a really elegant way to build up material combining robotics and the fibers and yarns into these intricate and complex patterns,” he says.
Robots can of course create patterns with more precision than if someone wound it by hand, as well as rapidly and reliably changing the yarn and color of the fabric pattern. Adidas says it can make a single upper in 45 minutes and a pair of sneakers in 1 hour and 30 minutes. It plans to reduce this time down to minutes in the months ahead, the company said.
An Adidas spokesperson says sneakers incorporating the Futurecraft.Strung uppers design are a prototype, but the company plans to bring a Strung shoe to market in late 2021 or 2022. However, Adidas Futurecraft sneakers are currently available with a 3D-printed midsole.
Adidas plans to continue gathering data from athletes to customize the uppers of sneakers. “We’re building up a library of knowledge and it will get more interesting as we aggregate data of testing and from different athletes and sports,” the Adidas Futurecraft team writes in a blog post. “The more we understand about how data can become design code, the more we can take that and apply it to new Strung textiles. It’s a continuous evolution.” Continue reading
#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