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#439908 Why Facebook (Or Meta) Is Making Tactile ...
Facebook, or Meta as it's now calling itself for some reason that I don't entirely understand, is today announcing some new tactile sensing hardware for robots. Or, new-ish, at least—there's a ruggedized and ultra low-cost GelSight-style fingertip sensor, plus a nifty new kind of tactile sensing skin based on suspended magnetic particles and machine learning. It's cool stuff, but why?
Obviously, Facebook Meta cares about AI, because it uses AI to try and do a whole bunch of the things that it's unwilling or unable to devote the time of actual humans to. And to be fair, there are some things that AI may be better at (or at least more efficient at) than humans are. AI is of course much worse than humans at many, many, many things as well, but that debate goes well beyond Facebook Meta and certainly well beyond the scope of this article, which is about tactile sensing for robots. So why does Facebook Meta care even a little bit about making robots better at touching stuff? Yann LeCun, the Chief AI Scientist at Facebook Meta, takes a crack at explaining it:
Before I joined Facebook, I was chatting with Mark Zuckerberg and I asked him, “is there any area related to AI that you think we shouldn't be working on?” And he said, “I can't find any good reason for us to work on robotics.” And so, that was kind of the start of Facebook AI Research—we were not going to work on robotics.
After a few years, it became clear that a lot of interesting progress in AI was happening in the context of robotics, because robotics is the nexus of where people in AI research are trying to get the full loop of perception, reasoning, planning, and action, and getting feedback from the environment. Doing it in the real world is where the problems are concentrated, and you can't play games if you want robots to learn quickly.
It was clear that four or five years ago, there was no business reason to work on robotics, but the business reasons have kind of popped up. Robotics could be used for telepresence, for maintaining data centers more automatically, but the more important aspect of it is making progress towards intelligent agents, the kinds of things that could be used in the metaverse, in augmented reality, and in virtual reality. That's really one of the raison d'être of a research lab, to foresee the domains that will be important in the future. So that's the motivation.Well, okay, but none of that seems like a good justification for research into tactile sensing specifically. But according to LeCun, it's all about putting together the pieces required for some level of fundamental world understanding, a problem that robotic systems are still bad at and that machine learning has so far not been able to tackle:
How to get machines to learn that model of the world that allows them to predict in advance and plan what's going to happen as a consequence of their actions is really the crux of the problem here. And this is something you have to confront if you work on robotics. But it's also something you have to confront if you want to have an intelligent agent acting in a virtual environment that can interact with humans in a natural way. And one of the long-term visions of augmented reality, for example, is virtual agents that basically are with you all the time, living in your automatic reality glasses or your smartphone or your laptop or whatever, helping you in your daily life as a human assistant would do, but also can answer any question you have. And that system will have to have some degree of understanding of how the world works—some degree of common sense, and be smart enough to not be frustrating to talk to. And that is where all of this research leads in the long run, whether the environment is real or virtual.AI systems (robots included) are very very dumb in very very specific ways, quite often the ways in which humans are least understanding and forgiving of. This is such a well established thing that there's a name for it: Moravec's paradox. Humans are great at subconscious levels of world understanding that we've built up over years and years of experience being, you know, alive. AI systems have none of this, and there isn't necessarily a clear path to getting them there, but one potential approach is to start with the fundamentals in the same way that a shiny new human does and build from there, a process that must necessarily include touch.
The DIGIT touch sensor is based on the GelSight style of sensor, which was first conceptualized at MIT over a decade ago. The basic concept of these kinds of tactile sensors is that they're able to essentially convert a touch problem into a vision problem: an array of LEDs illuminate a squishy finger pad from the back, and when the squishy finger pad pushes against something with texture, that texture squishes through to the other side of the finger pad where it's illuminated from many different angles by the LEDs. A camera up inside of the finger takes video of this, resulting in a very rainbow but very detailed picture of whatever the finger pad is squishing against.
The DIGIT paper published last year summarizes the differences between this new sensor and previous versions of GelSight:
DIGIT improves over existing GelSight sensors in several ways: by providing a more compact form factor that can be used on multi-finger hands, improving the durability of the elastomer gel, and making design changes that facilitate large-scale, repeatable production of the sensor hardware to facilitate tactile sensing research.
DIGIT is open source, so you can make one on your own, but that's a hassle. The really big news here is that GelSight itself (an MIT spinoff which commercialized the original technology) will be commercially manufacturing DIGIT sensors, providing a standardized and low-cost option for tactile sensing. The bill of materials for each DIGIT sensor is about US $15 if you were to make a thousand of them, so we're expecting that the commercial version won't cost much more than that.
The other hardware announcement is ReSkin, a tactile sensing skin developed in collaboration with Carnegie Mellon. Like DIGIT, the idea is to make an open source, robust, and very low cost system that will allow researchers to focus on developing the software to help robots make sense of touch rather than having to waste time on their own hardware.
ReSkin operates on a fairly simple concept: it's a flexible sheet of 2mm thick silicone with magnetic particles carelessly mixed in. The sheet sits on top of a magnetometer, and whenever the sheet deforms (like if something touches it), the magnetic particles embedded in the sheet get squooshed and the magnetic signal changes, which is picked up by the magnetometer. For this to work, the sheet doesn't have to be directly connected to said magnetometer. This is key, because it makes the part of the ReSkin sensor that's most likely to get damaged super easy to replace—just peel it off and slap on another one and you're good to go.
I get that touch is an integral part of this humanish world understanding that Facebook Meta is working towards, but for most of us, the touch is much more nuanced than just tactile data collection, because we experience everything that we touch within the world understanding that we've built up through integration of all of our other senses as well. I asked Roberto Calandra, one of the authors of the paper on DIGIT, what he thought about this:
I believe that we certainly want to have multimodal sensing in the same way that humans do. Humans use cues from touch, cues from vision, and also cues from audio, and we are able to very smartly put these different sensor modalities together. And if I tell you, can you imagine how touching this object is going to feel for you, can sort of imagine that. You can also tell me the shape of something that you are touching, you are able to somehow recognize it. So there is very clearly a multisensorial representation that we are learning and using as humans, and it's very likely that this is also going to be very important for embodied agents that we want to develop and deploy.Calandra also noted that they still have plenty of work to do to get DIGIT closer in form factor and capability to a human finger, which is an aspiration that I often hear from roboticists. But I always wonder: why bother? Like, why constrain robots (which can do all kinds of things that humans cannot) to do things in a human-like way, when we can instead leverage creative sensing and actuation to potentially give them superhuman capabilities? Here's what Calandra thinks:
I don't necessarily believe that a human hand is the way to go. I do believe that the human hand is possibly the golden standard that we should compare against. Can we do at least as good as a human hand? Beyond that, I actually do believe that over the years, the decades, or maybe the centuries, robots will have the possibility of developing superhuman hardware, in the same way that we can put infrared sensors or laser scanners on a robot, why shouldn't we also have mechanical hardware which is superior?
I think there has been a lot of really cool work on soft robotics for example, on how to build tentacles that can imitate an octopus. So it's a very natural question—if we want to have a robot, why should it have hands and not tentacles? And the answer to this is, it depends on what the purpose is. Do we want robots that can perform the same functions of humans, or do we want robots which are specialized for doing particular tasks? We will see when we get there.So there you have it—the future of manipulation is 100% sometimes probably tentacles. Continue reading
#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
#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