Tag Archives: legs
#435579 RoMeLa’s Newest Robot Is a ...
A few years ago, we wrote about NABiRoS, a bipedal robot from Dennis Hong’s Robotics & Mechanisms Laboratory (RoMeLa) at UCLA. Unlike pretty much any other biped we’d ever seen, NABiRoS had a unique kinematic configuration that had it using its two legs to walk sideways, which offered some surprising advantages.
As it turns out, bipeds aren’t the only robots that can potentially benefit from a bit of a kinematic rethink. RoMeLa has redesigned quadrupedal robots too—rather than model them after a quadrupedal animal like a dog or a horse, RoMeLa’s ALPHRED robots use four legs arranged symmetrically around the body of the robot, allowing it to walk, run, hop, and jump, as well as manipulate and carry objects, karate chop through boards, and even roller skate on its butt. This robot can do it all.
Impressive, right? This is ALPHRED 2, and its predecessor, the original ALPHRED, was introduced at IROS 2018. Both ALPHREDs are axisymmetric about the vertical axis, meaning that they don’t have a front or a back and are perfectly happy to walk in any direction you like. Traditional quadrupeds like Spot or Laikago can also move sideways and backwards, but their leg arrangement makes them more efficient at moving in one particular direction, and also results in some curious compromises like a preference for going down stairs backwards. ANYmal is a bit more flexible in that it can reverse its knees, but it’s still got that traditional quadrupedal two-by-two configuration.
ALPHRED 2’s four symmetrical limbs can be used for a whole bunch of stuff. It can do quadrupedal walking and running, and it’s able to reach stable speeds of up to 1.5 m/s. If you want bipedal walking, it can do that NABiRoS-style, although it’s still a bit fragile at the moment. Using two legs for walking leaves two legs free, and those legs can turn into arms. A tripedal compromise configuration, with three legs and one arm, is more stable and allows the robot to do things like push buttons, open doors, and destroy property. And thanks to passive wheels under its body, ALPHRED 2 can use its limbs to quickly and efficiently skate around:
The impressive performance of the robot comes courtesy of a custom actuator that RoMeLa designed specifically for dynamic legged locomotion. They call it BEAR, or Back-Drivable Electromechanical Actuator for Robots. These are optionally liquid-cooled motors capable of proprioceptive sensing, consisting of a DC motor, a single stage 10:1 planetary gearbox, and channels through the back of the housing that coolant can be pumped through. The actuators have a peak torque of 32 Nm, and a continuous torque of about 8 Nm with passive air cooling. With liquid cooling, the continuous torque jumps to about 21 Nm. And in the videos above, ALPHRED 2 isn’t even running the liquid cooling system, suggesting that it’s capable of much higher sustained performance.
Photo: RoMeLa
Using two legs for walking leaves two legs free, and those legs can turn into arms.
RoMeLa has produced a bunch of very creative robots, and we appreciate that they also seem to produce a bunch of very creative demos showing why their unusual approaches are in fact (at least in some specific cases) somewhat practical. With the recent interest in highly dynamic robots that can be reliably useful in environments infested with humans, we can’t wait to see what kinds of exciting tricks the next (presumably liquid-cooled) version will be able to do.
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#434592 Caltech Building Agile Humanoid Robot by ...
Leonardo augments humanoid legs with thrusters to help it run and jump Continue reading
#433785 DeepMind’s Eerie Reimagination of the ...
If a recent project using Google’s DeepMind were a recipe, you would take a pair of AI systems, images of animals, and a whole lot of computing power. Mix it all together, and you’d get a series of imagined animals dreamed up by one of the AIs. A look through the research paper about the project—or this open Google Folder of images it produced—will likely lead you to agree that the results are a mix of impressive and downright eerie.
But the eerie factor doesn’t mean the project shouldn’t be considered a success and a step forward for future uses of AI.
From GAN To BigGAN
The team behind the project consists of Andrew Brock, a PhD student at Edinburgh Center for Robotics, and DeepMind intern and researcher Jeff Donahue and Karen Simonyan.
They used a so-called Generative Adversarial Network (GAN) to generate the images. In a GAN, two AI systems collaborate in a game-like manner. One AI produces images of an object or creature. The human equivalent would be drawing pictures of, for example, a dog—without necessarily knowing what a dog exactly looks like. Those images are then shown to the second AI, which has already been fed images of dogs. The second AI then tells the first one how far off its efforts were. The first one uses this information to improve its images. The two go back and forth in an iterative process, and the goal is for the first AI to become so good at creating images of dogs that the second can’t tell the difference between its creations and actual pictures of dogs.
The team was able to draw on Google’s vast vaults of computational power to create images of a quality and life-like nature that were beyond almost anything seen before. In part, this was achieved by feeding the GAN with more images than is usually the case. According to IFLScience, the standard is to feed about 64 images per subject into the GAN. In this case, the research team fed about 2,000 images per subject into the system, leading to it being nicknamed BigGAN.
Their results showed that feeding the system with more images and using masses of raw computer power markedly increased the GAN’s precision and ability to create life-like renditions of the subjects it was trained to reproduce.
“The main thing these models need is not algorithmic improvements, but computational ones. […] When you increase model capacity and you increase the number of images you show at every step, you get this twofold combined effect,” Andrew Brock told Fast Company.
The Power Drain
The team used 512 of Google’s AI-focused Tensor Processing Units (TPU) to generate 512-pixel images. Each experiment took between 24 and 48 hours to run.
That kind of computing power needs a lot of electricity. As artist and Innovator-In-Residence at the Library of Congress Jer Thorp tongue-in-cheek put it on Twitter: “The good news is that AI can now give you a more believable image of a plate of spaghetti. The bad news is that it used roughly enough energy to power Cleveland for the afternoon.”
Thorp added that a back-of-the-envelope calculation showed that the computations to produce the images would require about 27,000 square feet of solar panels to have adequate power.
BigGAN’s images have been hailed by researchers, with Oriol Vinyals, research scientist at DeepMind, rhetorically asking if these were the ‘Best GAN samples yet?’
However, they are still not perfect. The number of legs on a given creature is one example of where the BigGAN seemed to struggle. The system was good at recognizing that something like a spider has a lot of legs, but seemed unable to settle on how many ‘a lot’ was supposed to be. The same applied to dogs, especially if the images were supposed to show said dogs in motion.
Those eerie images are contrasted by other renditions that show such lifelike qualities that a human mind has a hard time identifying them as fake. Spaniels with lolling tongues, ocean scenery, and butterflies were all rendered with what looks like perfection. The same goes for an image of a hamburger that was good enough to make me stop writing because I suddenly needed lunch.
The Future Use Cases
GAN networks were first introduced in 2014, and given their relative youth, researchers and companies are still busy trying out possible use cases.
One possible use is image correction—making pixillated images clearer. Not only does this help your future holiday snaps, but it could be applied in industries such as space exploration. A team from the University of Michigan and the Max Planck Institute have developed a method for GAN networks to create images from text descriptions. At Berkeley, a research group has used GAN to create an interface that lets users change the shape, size, and design of objects, including a handbag.
For anyone who has seen a film like Wag the Dog or read 1984, the possibilities are also starkly alarming. GANs could, in other words, make fake news look more real than ever before.
For now, it seems that while not all GANs require the computational and electrical power of the BigGAN, there is still some way to reach these potential use cases. However, if there’s one lesson from Moore’s Law and exponential technology, it is that today’s technical roadblock quickly becomes tomorrow’s minor issue as technology progresses.
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