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#439693 Agility Robotics’ Digit is Getting ...
Agility Robotics' Digit humanoid has been taking a bit of a break from work during the pandemic. Most of what we've seen from Agility and Digit over the past year and a half has been decidedly research-y. Don't get me wrong, Digit's been busy making humans look bad and not falling over when it really should have done, but remember that Agility's goal is to make Digit into a useful, practical robot. It's not a research platform—as Agility puts it, Digit is intended to “accelerate business productivity and people's pursuit of a more fulfilling life.” As far as I can make out, this is a fancier way of saying that Digit should really be spending its time doing dull repetitive tasks so that humans don't have to, and in a new video posted today, the robot shows how it can help out with boring warehouse tote shuffling.
The highlights here for me are really in the combination of legged mobility and object manipulation. Right at the beginning of the video, you see Digit squatting all the way down, grasping a tote bin, shuffling backwards to get the bin out from under the counter, and then standing again. There's an unfortunate cut there, but the sequence is shown again at 0:44, and you can see how Digit pulls the tote towards itself and then regrasps it before lifting. Clever. And at 1:20, the robot gives a tote that it just placed on a shelf a little nudge with one arm to make sure it's in the right spot.
These are all very small things, but I think of them as highlights because all of the big things seem to be more or less solved in this scenario. Digit has no problem lifting things, walking around, and not mowing over the occasional human, and once that stuff is all sorted, whether the robot is able to effectively work in an environment like this is to some extent reflected in all of these other little human-obvious things that often make the difference between success and failure.
The clear question, though, is why Digit (or, more broadly, any bipedal robot) is the right robot to be doing this kind of job. There are other robots out there already doing tasks like these in warehouses, and they generally have wheeled bases and manipulation systems specifically designed to move totes and do nothing else. If you were to use one of those robots instead of Digit, my guess is that you'd pay less for it, it would be somewhat safer, and it would likely do the job more efficiently. Fundamentally, Digit can't out box-move a box-moving robot. But the critical thing to consider here is that as soon as you run out of boxes to move, Digit can do all kinds of other things thanks to its versatile humanoid design, while your box-moving robot can only sit in the corner and be sad until more boxes show up.
“We did not set out to build a humanoid robot. We set out to solve mobility.”
—Agility CTO Jonathan Hurst
“Digit is very, very flexible automation,” Agility CTO Jonathan Hurst told us when we asked him about this. “The value of what we're doing is in generality, and having a robot that's going be able to work carrying totes for three or four hours, then go unload boxes from trailers for three or four hours, keep up with you if you change your workflow entirely. Many of these spaces are designed specifically around the human form factor, and it's possible for a robot like Digit to do all of these different boring, repetitive jobs. And then when things get complicated, humans are still doing it.”
The value of having a human-like robot in a human environment comes into play as soon as you start thinking about typical warehouse situations that would be trivial for a human to solve but that are impossible for wheeled robots. For example, Hurst says that Digit is capable of using a stool to reach objects on high shelves. You could, of course, design a wheeled robot with an extension system to allow it to reach high shelves, but you're now adding more cost and complexity, and the whole point of a generalist humanoid robot is that in human environments, you just don't have to worry about environmental challenges. Or that's the idea, anyway, but as Hurst explains, the fact that Digit ended up with a mostly humanoid form factor was more like a side effect of designing with specific capabilities in mind:
We did not set out to build a humanoid robot. We set out to solve mobility, and we've been on a methodical path towards understanding physical interaction in the world. Agility started with our robot Cassie, and one of the big problems with Cassie was that we didn't have enough inertia in the robot's body to counteract the leg swinging forward, which is why Digit has an upright torso. We wanted to give ourselves more control authority in the yaw direction with Cassie, so we experimented with putting a tail on the robot, and it turns out that the best tail is a pair of bilaterally symmetrical tails, one on either side.
Our goal was to design a machine that can go where people go while manipulating things in the world, and we ended up with this kind of form factor. It's a very different path for us to have gotten here than the vast majority of humanoid robots, and there's an awful lot of subtlety that is in our machine that is absent in most other machines.IEEE Spectrum: So are you saying that Digit's arms sort of started out as tails to help Cassie with yaw control?
Jonathan Hurst: There are many examples like this—we've been going down this path where we find a solution to a problem like yaw control, and it happens to look like it does with animals, but it's also a solution that's optimal in several different ways, like physical interaction and being able to catch the robot when it falls. It's not like it's a compromise between one thing and another thing, it's straight up the right solution for these three different performance design goals.
Looking back, we started by asking, should we put a reaction wheel or a gyro on Cassie for yaw control? Well, that's just wasted mass. We could use a tail, and there are a lot of nice robots with tails, but usually they're for controlling pitch. It's the same with animals; if you look at lizards, they use their tails for mid-air reorienting to land on their feet after they jump. Cassie doesn't need a tail for that, but we only have a couple of small feet on the ground to work with. And if you look at other bipedal animals, every one of them has some other way of getting that yaw authority. If you watch an ostrich run, when it turns, it sticks its wing out to get the control that it needs.
And so all of these things just fall into place, and a bilaterally symmetrical pair of tails is the best way to control yaw in a biped. When you see Digit walking and its arms are swinging, that's not something that we added to make the motion look right. It looks right because it literally is right—it's the physics of mobility. And that's a good sign for us that we're on the right path to getting the performance that we want.
“We're going for general purpose, but starting with some of the easiest use cases.”
—Agility CTO Jonathan Hurst
Spectrum: We've seen Digit demonstrating very impressive mobility skills. Why are we seeing a demo in a semi-constrained warehouse environment instead of somewhere that would more directly leverage Digit's unique advantages?
Jonathan Hurst: It's about finding the earliest, most appropriate, and most valuable use cases. There's a lot to this robot, and we're not going to be just a tote packing robot. We're not building a specialized robot for this one application, but we have a couple of pretty big logistics partners who are interested in the flexibility and the manipulation capabilities of this machine. And yeah, what you're seeing now is the robot on a flattish floor, but it's also not going to be tripped up by a curb, or a step, or, a wire cover, or other things on the ground. You don't have to worry about anything like that. So next, it's an easy transition next to unloading trailers, where it's going to have to be stepping over gaps and up and down things and around boxes on the floor and stuff like that. We're going for general purpose, but starting with some of the easiest use cases.
Damion Shelton, CEO: We're trying to prune down the industry space, to get to something where there's a clear value proposition with a partner and deploying there. We can respect the difficulty of the general purpose use case and work to deploy early and profitably, as opposed to continuing to push for the outdoor applications. The blessing and the curse of the Ford opportunity is that it's super interesting, but also super hard. And so it's very motivating, and it's clear to us that that's where one of the ultimate opportunities is, but it's also far enough away from a deployment timeline that it just doesn't map on to a viable business model.
This is a point that every robotics company runs into sooner or later, where aspirations have to succumb to the reality of selling robots in a long-term sustainable way. It's definitely not a bad thing, it just means that we may have to adjust our expectations accordingly. No matter what kind of flashy cutting-edge capabilities your robot has, if it can't cost effectively do dull or dirty or dangerous stuff, nobody's going to pay you money for it. And cost effective usefulness is, arguably, one of the biggest challenges in bipedal robotics right now. In the past, I've been impressed by Digit's weightlifting skills, or its ability to climb steep and muddy hills. I'll be just as impressed when it starts making money for Agility by doing boring repetitive tasks in warehouses, because that means that Agility will be able to keep working towards those more complex, more exciting things. “It's not general manipulation, and we're not solving the grand challenges of robotics,” says Hurst. “Yet. But we're on our way.” Continue reading
Posted in Human Robots
#439674 Cerebras Upgrades Trillion-Transistor ...
Much of the recent progress in AI has come from building ever-larger neural networks. A new chip powerful enough to handle “brain-scale” models could turbo-charge this approach.
Chip startup Cerebras leaped into the limelight in 2019 when it came out of stealth to reveal a 1.2-trillion-transistor chip. The size of a dinner plate, the chip is called the Wafer Scale Engine and was the world’s largest computer chip. Earlier this year Cerebras unveiled the Wafer Scale Engine 2 (WSE-2), which more than doubled the number of transistors to 2.6 trillion.
Now the company has outlined a series of innovations that mean its latest chip can train a neural network with up to 120 trillion parameters. For reference, OpenAI’s revolutionary GPT-3 language model contains 175 billion parameters. The largest neural network to date, which was trained by Google, had 1.6 trillion.
“Larger networks, such as GPT-3, have already transformed the natural language processing landscape, making possible what was previously unimaginable,” said Cerebras CEO and co-founder Andrew Feldman in a press release.
“The industry is moving past 1 trillion parameter models, and we are extending that boundary by two orders of magnitude, enabling brain-scale neural networks with 120 trillion parameters.”
The genius of Cerebras’ approach is that rather than taking a silicon wafer and splitting it up to make hundreds of smaller chips, it makes a single massive one. While your average GPU will have a few hundred cores, the WSE-2 has 850,000. Because they’re all on the same hunk of silicon, they can work together far more seamlessly.
This makes the chip ideal for tasks that require huge numbers of operations to happen in parallel, which includes both deep learning and various supercomputing applications. And earlier this week at the Hotchips conference, the company unveiled new technology that is pushing the WSE-2’s capabilities even further.
A major challenge for large neural networks is shuttling around all the data involved in their calculations. Most chips have a limited amount of memory on-chip, and every time data has to be shuffled in and out it creates a bottleneck, which limits the practical size of networks.
The WSE-2 already has an enormous 40 gigabytes of on-chip memory, which means it can hold even the largest of today’s networks. But the company has also built an external unit called MemoryX that provides up to 2.4 Petabytes of high-performance memory, which is so tightly integrated it behaves as if it were on-chip.
Cerebras has also revamped its approach to that data it shuffles around. Previously the guts of the neural network would be stored on the chip, and only the training data would be fed in. Now, though, the weights of the connections between the network’s neurons are kept in the MemoryX unit and streamed in during training.
By combining these two innovations, the company says, they can train networks two orders of magnitude larger than anything that exists today. Other advances announced at the same time include the ability to run extremely sparse (and therefore efficient) neural networks, and a new communication system dubbed SwarmX that makes it possible to link up to 192 chips to create a combined total of 163 million cores.
How much all this cutting-edge technology will cost and who is in a position to take advantage of it is unclear. “This is highly specialized stuff,” Mike Demler, a senior analyst with the Linley Group, told Wired. “It only makes sense for training the very largest models.”
While the size of AI models has been increasing rapidly, it’s likely to be years before anyone can push the WSE-2 to its limits. And despite the insinuations in Cerebras’ press material, just because the parameter count roughly matches the number of synapses in the brain, that doesn’t mean the new chip will be able to run models anywhere close to its complexity or performance.
There’s a major debate in AI circles today over whether we can achieve general artificial intelligence by simply building larger neural networks, or this will require new theoretical breakthroughs. So far, increasing parameter counts has led to pretty consistent jumps in performance. A two-order-of-magnitude improvement over today’s largest models would undoubtedly be significant.
It’s still far from clear whether that trend will hold out, but Cerebras’ new chip could get us considerably closer to an answer.
Image Credit: Cerebras Continue reading
Posted in Human Robots
#439628 How a Simple Crystal Could Help Pave the ...
Vaccine and drug development, artificial intelligence, transport and logistics, climate science—these are all areas that stand to be transformed by the development of a full-scale quantum computer. And there has been explosive growth in quantum computing investment over the past decade.
Yet current quantum processors are relatively small in scale, with fewer than 100 qubits— the basic building blocks of a quantum computer. Bits are the smallest unit of information in computing, and the term qubits stems from “quantum bits.”
While early quantum processors have been crucial for demonstrating the potential of quantum computing, realizing globally significant applications will likely require processors with upwards of a million qubits.
Our new research tackles a core problem at the heart of scaling up quantum computers: how do we go from controlling just a few qubits, to controlling millions? In research published today in Science Advances, we reveal a new technology that may offer a solution.
What Exactly Is a Quantum Computer?
Quantum computers use qubits to hold and process quantum information. Unlike the bits of information in classical computers, qubits make use of the quantum properties of nature, known as “superposition” and “entanglement,” to perform some calculations much faster than their classical counterparts.
Unlike a classical bit, which is represented by either 0 or 1, a qubit can exist in two states (that is, 0 and 1) at the same time. This is what we refer to as a superposition state.
Demonstrations by Google and others have shown even current, early-stage quantum computers can outperform the most powerful supercomputers on the planet for a highly specialized (albeit not particularly useful) task—reaching a milestone we call quantum supremacy.
Google’s quantum computer, built from superconducting electrical circuits, had just 53 qubits and was cooled to a temperature close to -273℃ in a high-tech refrigerator. This extreme temperature is needed to remove heat, which can introduce errors to the fragile qubits. While such demonstrations are important, the challenge now is to build quantum processors with many more qubits.
Major efforts are underway at UNSW Sydney to make quantum computers from the same material used in everyday computer chips: silicon. A conventional silicon chip is thumbnail-sized and packs in several billion bits, so the prospect of using this technology to build a quantum computer is compelling.
The Control Problem
In silicon quantum processors, information is stored in individual electrons, which are trapped beneath small electrodes at the chip’s surface. Specifically, the qubit is coded into the electron’s spin. It can be pictured as a small compass inside the electron. The needle of the compass can point north or south, which represents the 0 and 1 states.
To set a qubit in a superposition state (both 0 and 1), an operation that occurs in all quantum computations, a control signal must be directed to the desired qubit. For qubits in silicon, this control signal is in the form of a microwave field, much like the ones used to carry phone calls over a 5G network. The microwaves interact with the electron and cause its spin (compass needle) to rotate.
Currently, each qubit requires its own microwave control field. It is delivered to the quantum chip through a cable running from room temperature down to the bottom of the refrigerator at close to -273 degrees Celsius. Each cable brings heat with it, which must be removed before it reaches the quantum processor.
At around 50 qubits, which is state-of-the-art today, this is difficult but manageable. Current refrigerator technology can cope with the cable heat load. However, it represents a huge hurdle if we’re to use systems with a million qubits or more.
The Solution Is ‘Global’ Control
An elegant solution to the challenge of how to deliver control signals to millions of spin qubits was proposed in the late 1990s. The idea of “global control” was simple: broadcast a single microwave control field across the entire quantum processor.
Voltage pulses can be applied locally to qubit electrodes to make the individual qubits interact with the global field (and produce superposition states).
It’s much easier to generate such voltage pulses on-chip than it is to generate multiple microwave fields. The solution requires only a single control cable and removes obtrusive on-chip microwave control circuitry.
For more than two decades global control in quantum computers remained an idea. Researchers could not devise a suitable technology that could be integrated with a quantum chip and generate microwave fields at suitably low powers.
In our work we show that a component known as a dielectric resonator could finally allow this. The dielectric resonator is a small, transparent crystal which traps microwaves for a short period of time.
The trapping of microwaves, a phenomenon known as resonance, allows them to interact with the spin qubits longer and greatly reduces the power of microwaves needed to generate the control field. This was vital to operating the technology inside the refrigerator.
In our experiment, we used the dielectric resonator to generate a control field over an area that could contain up to four million qubits. The quantum chip used in this demonstration was a device with two qubits. We were able to show the microwaves produced by the crystal could flip the spin state of each one.
The Path to a Full-Scale Quantum Computer
There is still work to be done before this technology is up to the task of controlling a million qubits. For our study, we managed to flip the state of the qubits, but not yet produce arbitrary superposition states.
Experiments are ongoing to demonstrate this critical capability. We’ll also need to further study the impact of the dielectric resonator on other aspects of the quantum processor.
That said, we believe these engineering challenges will ultimately be surmountable— clearing one of the greatest hurdles to realizing a large-scale spin-based quantum computer.
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Image Credit: Serwan Asaad/UNSW, Author provided Continue reading
Posted in Human Robots
#439589 Tiny ‘maniac’ robots could ...
Would you let a tiny MANiAC travel around your nervous system to treat you with drugs? You may be inclined to say no, but in the future, “magnetically aligned nanorods in alginate capsules” (MANiACs) may be part of an advanced arsenal of drug delivery technologies at doctors' disposal. A recent study in Frontiers in Robotics and AI is the first to investigate how such tiny robots might perform as drug delivery vehicles in neural tissue. The study finds that when controlled using a magnetic field, the tiny tumbling soft robots can move against fluid flow, climb slopes and move about neural tissues, such as the spinal cord, and deposit substances at precise locations. Continue reading
Posted in Human Robots