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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
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
In the fictional worlds of film and TV, artificial intelligence has been depicted as so advanced that it is indistinguishable from humans. But what if we’re actually getting closer to a world where AI is capable of thinking and feeling?
Tech company UneeQ is embarking on that journey with its “digital humans.” These avatars act as visual interfaces for customer service chatbots, virtual assistants, and other applications. UneeQ’s digital humans appear lifelike not only in terms of language and tone of voice, but also because of facial movements: raised eyebrows, a tilt of the head, a smile, even a wink. They transform a transaction into an interaction: creepy yet astonishing, human, but not quite.
What lies beneath UneeQ’s digital humans? Their 3D faces are modeled on actual human features. Speech recognition enables the avatar to understand what a person is saying, and natural language processing is used to craft a response. Before the avatar utters a word, specific emotions and facial expressions are encoded within the response.
UneeQ may be part of a larger trend towards humanizing computing. ObEN’s digital avatars serve as virtual identities for celebrities, influencers, gaming characters, and other entities in the media and entertainment industry. Meanwhile, Soul Machines is taking a more biological approach, with a “digital brain” that simulates aspects of the human brain to modulate the emotions “felt” and “expressed” by its “digital people.” Amelia is employing a similar methodology in building its “digital employees.” It emulates parts of the brain involved with memory to respond to queries and, with each interaction, learns to deliver more engaging and personalized experiences.
Shiwali Mohan, an AI systems scientist at the Palo Alto Research Center, is skeptical of these digital beings. “They’re humanlike in their looks and the way they sound, but that in itself is not being human,” she says. “Being human is also how you think, how you approach problems, and how you break them down; and that takes a lot of algorithmic design. Designing for human-level intelligence is a different endeavor than designing graphics that behave like humans. If you think about the problems we’re trying to design these avatars for, we might not need something that looks like a human—it may not even be the right solution path.”
And even if these avatars appear near-human, they still evoke an uncanny valley feeling. “If something looks like a human, we have high expectations of them, but they might behave differently in ways that humans just instinctively know how other humans react. These differences give rise to the uncanny valley feeling,” says Mohan.
Yet the demand is there, with Amelia seeing high adoption of its digital employees across the financial, health care, and retail sectors. “We find that banks and insurance companies, which are so risk-averse, are leading the adoption of such disruptive technologies because they understand that the risk of non-adoption is much greater than the risk of early adoption,” says Chetan Dube, Amelia’s CEO. “Unless they innovate their business models and make them much more efficient digitally, they might be left behind.” Dube adds that the COVID-19 pandemic has accelerated adoption of digital employees in health care and retail as well.
Amelia, Soul Machines, and UneeQ are taking their digital beings a step further, enabling organizations to create avatars themselves using low-code or no-code platforms: Digital Employee Builder for Amelia, Creator for UneeQ, and Digital DNA Studio for Soul Machines. Unreal Engine, a game engine developed by Epic Games, is doing the same with MetaHuman Creator, a tool that allows anyone to create photorealistic digital humans. “The biggest motivation for Digital Employee Builder is to democratize AI,” Dube says.
Mohan is cautious about this approach. “AI has problems with bias creeping in from data sets and into the way it speaks. The AI community is still trying to figure out how to measure and counter that bias,” she says. “[Companies] have to have an AI expert on board that can recommend the right things to build for.”
Despite being wary of the technology, Mohan supports the purpose behind these virtual beings and is optimistic about where they’re headed. “We do need these tools that support humans in different kinds of things. I think the vision is the pro, and I’m behind that vision,” she says. “As we develop more sophisticated AI technology, we would then have to implement novel ways of interacting with that technology. Hopefully, all of that is designed to support humans in their goals.” Continue reading
Thanks to neural implants, mind reading is no longer science fiction.
As I’m writing this sentence, a tiny chip with arrays of electrodes could sit on my brain, listening in on the crackling of my neurons firing as my hands dance across the keyboard. Sophisticated algorithms could then decode these electrical signals in real time. My brain’s inner language to plan and move my fingers could then be used to guide a robotic hand to do the same. Mind-to-machine control, voilà!
Yet as the name implies, even the most advanced neural implant has a problem: it’s an implant. For electrodes to reliably read the brain’s electrical chatter, they need to pierce through the its protective membrane and into brain tissue. Danger of infection aside, over time, damage accumulates around the electrodes, distorting their signals or even rendering them unusable.
Now, researchers from Caltech have paved a way to read the brain without any physical contact. Key to their device is a relatively new superstar in neuroscience: functional ultrasound, which uses sound waves to capture activity in the brain.
In monkeys, the technology could reliably predict their eye movement and hand gestures after just a single trial—without the usual lengthy training process needed to decode a movement. If adopted by humans, the new mind-reading tech represents a triple triumph: it requires minimal surgery and minimal learning, but yields maximal resolution for brain decoding. For people who are paralyzed, it could be a paradigm shift in how they control their prosthetics.
“We pushed the limits of ultrasound neuroimaging and were thrilled that it could predict movement,” said study author Dr. Sumner Norman.
To Dr. Krishna Shenoy at Stanford, who was not involved, the study will finally put ultrasound “on the map as a brain-machine interface technique. Adding to this toolkit is spectacular,” he said.
Breaking the Sound Barrier
Using sound to decode brain activity might seem preposterous, but ultrasound has had quite the run in medicine. You’ve probably heard of its most common use: taking photos of a fetus in pregnancy. The technique uses a transducer, which emits ultrasound pulses into the body and finds boundaries in tissue structure by analyzing the sound waves that bounce back.
Roughly a decade ago, neuroscientists realized they could adapt the tech for brain scanning. Rather than directly measuring the brain’s electrical chatter, it looks at a proxy—blood flow. When certain brain regions or circuits are active, the brain requires much more energy, which is provided by increased blood flow. In this way, functional ultrasound works similarly to functional MRI, but at a far higher resolution—roughly ten times, the authors said. Plus, people don’t have to lie very still in an expensive, claustrophobic magnet.
“A key question in this work was: If we have a technique like functional ultrasound that gives us high-resolution images of the brain’s blood flow dynamics in space and over time, is there enough information from that imaging to decode something useful about behavior?” said study author Dr. Mikhail Shapiro.
There’s plenty of reasons for doubt. As the new kid on the block, functional ultrasound has some known drawbacks. A major one: it gives a far less direct signal than electrodes. Previous studies show that, with multiple measurements, it can provide a rough picture of brain activity. But is that enough detail to guide a robotic prosthesis?
The new study put functional ultrasound to the ultimate test: could it reliably detect movement intention in monkeys? Because their brains are the most similar to ours, rhesus macaque monkeys are often the critical step before a brain-machine interface technology is adapted for humans.
The team first inserted small ultrasound transducers into the skulls of two rhesus monkeys. While it sounds intense, the surgery doesn’t penetrate the brain or its protective membrane; it’s only on the skull. Compared to electrodes, this means the brain itself isn’t physically harmed.
The device is linked to a computer, which controls the direction of sound waves and captures signals from the brain. For this study, the team aimed the pulses at the posterior parietal cortex, a part of the “motor” aspect of the brain, which plans movement. If right now you’re thinking about scrolling down this page, that’s the brain region already activated, before your fingers actually perform the movement.
Then came the tests. The first looked at eye movements—something pretty necessary before planning actual body movements without tripping all over the place. Here, the monkeys learned to focus on a central dot on a computer screen. A second dot, either left or right, then flashed. The monkeys’ task was to flicker their eyes to the most recent dot. It’s something that seems easy for us, but requires sophisticated brain computation.
The second task was more straightforward. Rather than just moving their eyes to the second target dot, the monkeys learned to grab and manipulate a joystick to move a cursor to that target.
Using brain imaging to decode the mind and control movement. Image Credit: S. Norman, Caltech
As the monkeys learned, so did the device. Ultrasound data capturing brain activity was fed into a sophisticated machine learning algorithm to guess the monkeys’ intentions. Here’s the kicker: once trained, using data from just a single trial, the algorithm was able to correctly predict the monkeys’ actual eye movement—whether left or right—with roughly 78 percent accuracy. The accuracy for correctly maneuvering the joystick was even higher, at nearly 90 percent.
That’s crazy accurate, and very much needed for a mind-controlled prosthetic. If you’re using a mind-controlled cursor or limb, the last thing you’d want is to have to imagine the movement multiple times before you actually click the web button, grab the door handle, or move your robotic leg.
Even more impressive is the resolution. Sound waves seem omnipresent, but with focused ultrasound, it’s possible to measure brain activity at a resolution of 100 microns—roughly 10 neurons in the brain.
A Cyborg Future?
Before you start worrying about scientists blasting your brain with sound waves to hack your mind, don’t worry. The new tech still requires skull surgery, meaning that a small chunk of skull needs to be removed. However, the brain itself is spared. This means that compared to electrodes, ultrasound could offer less damage and potentially a far longer mind reading than anything currently possible.
There are downsides. Focused ultrasound is far younger than any electrode-based neural implants, and can’t yet reliably decode 360-degree movement or fine finger movements. For now, the tech requires a wire to link the device to a computer, which is off-putting to many people and will prevent widespread adoption. Add to that the inherent downside of focused ultrasound, which lags behind electrical recordings by roughly two seconds.
All that aside, however, the tech is just tiptoeing into a future where minds and machines seamlessly connect. Ultrasound can penetrate the skull, though not yet at the resolution needed for imaging and decoding brain activity. The team is already working with human volunteers with traumatic brain injuries, who had to have a piece of their skulls removed, to see how well ultrasound works for reading their minds.
“What’s most exciting is that functional ultrasound is a young technique with huge potential. This is just our first step in bringing high performance, less invasive brain-machine interface to more people,” said Norman.
Image Credit: Free-Photos / Pixabay Continue reading