2025-11-20 23:00:02
A few years ago, Matthew Carey lost a friend in a freak car accident, after the friend’s car struck some small debris on a highway and spun out of control. Ordinarily, the car’s sensors would have detected the debris in plenty of time, but it was operating under conditions that render all of today’s car-mounted sensors useless: fog and bright early-morning sunshine. Radar can’t see small objects well, lidar is limited by fog, and cameras are blinded by glare. Carey and his cofounders decided to create a sensor that could have done the job—a terahertz imager.
Historically, terahertz frequencies have been the least utilized portion of the electromagnetic spectrum. People have struggled to send them even short distances through the air. But thanks to some intense engineering and improvements in silicon transistor frequency, beaming terahertz radiation over hundreds of meters is now possible. Teradar, the Boston-based startup Carey cofounded, has managed to make a sensor that can meet the auto industry’s 300-meter distance requirements.
The company came out of stealth last week with chips it says can deliver 20 times the resolution of automotive radar while seeing through all kinds of weather and costing less than lidar. The tech provides “a superset of lidar and radar combined,” Carey says. The technology is in tests with carmakers for a slot in vehicles to be produced in 2028, he says. It would be the first such sensor to make it to market.
“Every time you unlock a chunk of the electromagnetic spectrum, you unlock a brand-new way to view the world,” Carey says.
Teradar’s system is a new architecture, says Carey, that has elements of traditional radar and a camera. The terahertz transmitters are arrays of elements that generate electronically steerable beams, while the sensors are like imaging chips in a camera. The beams scan the area, and the sensor measures the time it takes for the signals to return as well as where they return from.
Teradar’s system can steer beams of terahertz radiation with no moving parts.Teradar
From these signals, the system generates a point cloud, similar to what a lidar produces. But unlike lidar, it does not use any moving parts. Those moving parts add significantly to the cost of lidar and subject it to wear and tear from the road.
“It’s a sensor that [has] the simplicity of radar and the resolution of lidar,” says Carey. Whether it replaces either technology or becomes an add-on is up to carmakers, he adds. The company is currently working with five of them.
That Teradar has gotten this far is partly down to progress in silicon transistor technology—in particular, the steady increase in the maximum frequency of devices that modern foundries can supply, says Carey.
Ruonan Han, a professor of electrical engineering at MIT who specializes in terahertz electronics, agrees. These improvements have led to boosts in the efficiency of terahertz circuits, their output power, and the sensitivity of receivers. Additionally, chip packaging, which is key to efficiently transmitting the radiation, has improved. Combined with research into the design of circuits and systems, engineers can now apply terahertz radiation in a variety of applications, including autonomous driving and safety.
Nevertheless, “it’s pretty challenging to deliver the performance needed for real and safe self-driving—especially the distance,” says Han. His lab at MIT has worked on terahertz radar and other circuits for several years. At the moment it’s focused on developing lightweight, low-power terahertz sensors for robots and drones. His lab has also spun out an imaging startup, Cambridge Terahertz, targeted at using the frequency band’s advantages in security scanners, where it can see through clothes to spot hidden weapons.
Teradar, too, will explore applications outside the automotive sector. Carey points out that while terahertz frequencies do not penetrate skin, melanomas show up as a different color at those wavelengths compared to normal skin.
But for now Carey’s company is focused on cars. And in that area, there’s one question I had to ask: Could Teradar’s tech have saved Kit Kat, the feline regrettably run down by a Waymo self-driving car in San Francisco last month?
“It probably would have saved the cat,” says Carey.
2025-11-20 04:56:25
This article is crossposted from IEEE Spectrum’s careers newsletter. Sign up now to get insider tips, expert advice, and practical strategies, written in partnership with tech career development company Taro and delivered to your inbox for free!
The most productive engineer I worked with at Meta joined the company as a staff engineer. This is already a relatively senior position, but he then proceeded to earn two promotions within three years, becoming one of the most senior engineers in the entire company.
Interestingly, what made him so productive was also frequently a source of annoyance for many of his colleagues. Productivity comes from prioritization, and that meant he often said no to ideas and opportunities that he didn’t think were important.
He frequently rejected projects that didn’t align with his priorities. He was laser-focused every day on the top project that the organization needed to deliver. He would skip status meetings, tech debt initiatives, and team bonding events. When he was in focus mode, he was difficult to get in touch with.
Compared to his relentless focus, I realized that most of what I spent my time on didn’t actually matter. I thought that having a to-do list of 10 items meant I was being productive. He ended up accomplishing a lot more than me with a list of two items, even if that meant he may have occasionally been a painful collaborator.
This is what the vast majority of engineers misunderstand about productivity. The biggest productivity “hack” is to simply work on the right things.
Figure out what’s important and strip away everything else from your day so that you can make methodical progress on that. In many workplaces, this is surprisingly difficult, and you’ll find your calendar filled with team lunches, maintenance requests, and leadership reviews. Do an audit of your day and examine how you spend your time. As an engineer, if the majority of your day is spent in emails and coordinating across teams, you’re clearly not being as productive as you could be.
My colleague got promoted so quickly because of his prodigious output. That output comes from whittling down the number of priorities rather than expanding them. It’s far better to deliver fully on the key priority, rather than getting pulled in every direction and subsequently failing to deliver anything of value.
—Rahul
Carlotta Berry is an electrical and computer engineering professor focused on bringing low-cost mobile robots to the public so that anyone can learn about robotics. She demonstrates open-source robots of her own design at schools, libraries, museums, and other community venues. Learn how her work earned her an Undergraduate Teaching Award from the IEEE Robotics and Automation Society.
We should not resign ourselves to the story of AI making experiences worse, say Bruce Schneier and Nathan E. Sanders at Harvard University. Rather, scientists and engineers should recognize the ways in which AI can be used for good. They suggest reforming AI under ethical guidelines, documenting negative applications of AI, using AI responsibly, and preparing institutions for the impacts of AI.
2025-11-20 03:00:02
Genya Crossman is a lifelong learner passionate about helping people understand and use quantum computing to solve the world’s most complex problems.
So, she is excited that quantum computing is in the spotlight this year. UNESCO declared 2025 the International Year of Quantum Science and Technology. It’s also the 100th anniversary of physicist Werner Heisenberg’s “On the Quantum-Theoretical Reinterpretation of Kinematic and Mechanical Relationships,” the first published paper on quantum mechanics.
Crossman, an IEEE member, is a quantum strategy consultant at IBM in Germany. As a full-time staff member, she coordinates and manages five working groups focused on developing quantum-based solutions for near-term problems in health care and life sciences, materials science, high-energy physics, optimization, and sustainability.
Employer
IBM in Germany
Job title
Quantum strategy consultant
Member grade
Member
Alma maters
University of Massachusetts, Amherst; Delft University of Technology and the Technische Universität Berlin
She attended the sixth annual IEEE Quantum Week, held from 31 August to 5 September in Albuquerque. This year’s event, also known as the IEEE International Conference on Quantum Computing and Engineering, marked the first time that the IBM- and community-created working groups’ experts and collaborators publicly presented their research together.
“We got great feedback and information about identifying common features across groups,” Crossman says. “The audience got to hear real-life examples to understand how quantum computing applies to different scenarios and how it works.”
Crossman understands the importance of sharing research more than most because she works at the intersection of quantum computing research and practical application. The quantum field might seem intimidating, she says, but you don’t need to understand it to use a quantum computer.
“Anyone can use one,” she says. “And if you know programming languages like Python, you can code a quantum computer.”
IBM has a long-standing history with quantum computing. IEEE Member Charles H. Bennett, an IBM Fellow, is called the father of quantum information theory because he wrote the first notes on the subject in 1970. In May 1981, IBM and MIT held the first Physics of Computation Conference.
“Quantum computing is often used to describe all quantum work,” including quantum science and quantum technology, Crossman says. The field involves a variety of technologies, including sensors, meteorology, and communications.
Classical computers use bits, and quantum computers use quantum bits, called qubits. Qubits can exist in more than one state simultaneously (both one and zero), known as the ability to exist in “superposition.”
Computers using qubits can store and process highly complex information and data faster and more efficiently, possibly using significantly less energy than classical computers.
With so much power and processing ability, quantum computers are complex and still not fully understood. Engineers are working to make quantum computing more accessible to everyone, so more people can understand how to work with the technology, Crossman says.
Growing up in the North Shore of Boston, Crossman spent many summer mornings poring over the latest issues of IEEE Spectrum and Scientific American with her older sister. Her father, Antony Crossman, is an electrical and electronics engineer and an IEEE life member. He often discussed science and engineering concepts with his daughters.
Looking back, Crossman says, she sees reading Spectrum as her first introduction to how research is presented.
“I loved reading about new research and what could be done with it,” she says. “It helped point me toward engineering as a career.”
When she enrolled at McGill University in Montreal in 2011 to pursue a bachelor’s degree in physics, her father gifted her an IEEE student membership.
“Montreal is a beautiful, creative city that’s also relatively easy to travel to from Boston within a day,” she says. “Plus, the school was known for its physics program.”
After two years, she dropped out and moved to Paris, where she worked in a café. A year later, in 2014, she enrolled in the physics degree program at the University of Massachusetts, Amherst.
In the summer of 2016, Crossman’s undergraduate advisor, Professor Stéphane Willocq, recommended her for a research project in the Microsystems Technology Laboratory within MIT’s electrical engineering department.
“Quantum computing is often used to describe all quantum work, including quantum computing, quantum science, and quantum technology.”
“I had been conducting research” with Willocq, she says, “and he knew I was considering going into electrical engineering, so he suggested I apply for this summer research opportunity.”
As a research assistant, she examined carrier transport in transistors and diodes made with two-dimensional materials.
After graduating with a bachelor’s degree in physics in 2017, she initially planned to go straight to graduate school, she says, but she wasn’t sure what she wanted to focus on. A friend and former classmate from an undergraduate quantum mechanics course referred her to a quantum computing job opening at Rigetti Computing in Berkeley, Calif.
She was hired as a junior quantum engineer. She started by creating the predecessor to, and then the schema for, the company’s first device database. She then designed, modeled, and simulated quantum devices such as circuits for superconducting quantum computers, including some used in the first deployed quantum systems. She also managed the Berkeley fabrication facility.
In that role, she learned a great deal about electrical and microwave engineering, she says, and that introduced her to computational modeling. It led her to better understand practical applications of quantum computing, she says. Her newfound knowledge made her “want to learn why and how people use quantum technology,” she says, which is how she became interested in the end users’ needs.
To further her career, she left Rigetti in 2020 and moved to Germany to pursue a dual master’s degree in computational and applied mathematics through a joint program between the Delft University of Technology and the Technische Universität Berlin. When she first began her master’s program, IBM recruiters offered her two jobs, she says, but she declined because she wanted to finish her degree.
During her studies, she worked with her mentor Eliska Greplova, an associate professor at TU Delft, who invited Crossman to join her quantum matter and AI research group. Crossman learned about condensed matter, machine learning, and quantum learning, and she participated in discussions about the technologies’ implications.
Despite being a great experience, it ultimately led her to decide against pursuing a Ph.D., she says, because she enjoyed working in the industry and that’s where she wanted to be in the long run.
She had planned to focus her master’s thesis on quantum computing from the end user’s perspective, but she switched to writing about integrating topological properties onto superconducting hardware.
She graduated in 2022. In January 2023, she accepted a full-time position at IBM Research in Germany as a quantum strategy consultant, supporting enterprise clients. Since then, her job has changed to technical engagement lead, overseeing the five quantum working groups.
She is also part of the team that oversees the company’s responsible computing initiative. IBM defines responsible quantum computing as the type that’s “aware of its effects.” The company says it wants to ensure it develops and uses quantum computing in line with its principles.
Established in 2022 by IBM and researchers from other organizations, the working groups tackle near-term problems and look for quantum and interdisciplinary solutions in their area of focus, Crossman says.
The groups are community-driven, with researchers from both quantum and nonquantum backgrounds collaborating to identify key problems, decide what to pursue, and pool their expertise to fill gaps, allowing them to look at problems holistically, she says. The groups regularly publish papers and make them publicly available.
Crossman’s job is to support the researchers, locate resources, help them use the IBM ecosystem, and identify experts to answer niche questions. Her other focus is on the end users, the people who will employ the research emerging from the working groups. She says she seeks to understand their needs and how to best support them.
“I really enjoy quantum engineering and working with everyone because it’s such an interdisciplinary field,” she says. “It combines problem-solving with creativity. It’s really at an exciting stage of development.”
With so much momentum, Crossman says, she is eager to see where quantum technologies go next.
“When I started learning about quantum mechanics in undergrad, there wasn’t much information out there,” she says. “The beginning of my career was when the quantum computing industry was just getting started. I’m really grateful for that.”
Being an IEEE member allows Crossman to stay updated on research across multiple fields, she says, and that’s important because most of them “are becoming much more interdisciplinary, especially quantum computing.”
She says she is looking forward to collaborating more with IEEE members working on quantum computing.
“I’ve always found IEEE useful,” she says. “I can learn about new research in my and other fields, and I really enjoyed attending this year’s Quantum Week.”
2025-11-20 00:12:53
Tesla was first to patent a primitive axial-flux electric motor—Nikola Tesla, that is, way back in 1889. It would be 126 years before the concept found its way to a car, the 1,500-horsepower (1,103 kilowatt), US $1.9 million, Koenigsegg Regera hybrid, in 2015. Even today, nearly all the world’s EVs and hybrids rely on relatively inefficient, easy-to-manufacture radial-flux motors.
Yet the latest electrified revolution is underway, led by YASA. Founded in the U.K. by Tim Woolmer in 2009, a spin-off from his Oxford PhD project, the company’s pioneering axial-flux motors are powering hybrid supercars from a Who’s Who of makers: Ferrari, Lamborghini, McLaren and Koenigsegg. Those include the Ferrari 296 Speciale and Lamborghini Temerario that I recently drove in Italy.
Boosted by these power-dense electric machines, these racy Italians carved up roads in Emilia-Romagna like hunks of prosciutto di Parma. The Temerario’s gasoline V-8 revs to a stratospheric 10,000 rpm, higher than any production supercar. Still not enough: The Temerario also integrates three YASA motors. A pair on the front axle deliver all-wheel-drive traction and a peak 294 horsepower (216 kilowatts). A total of 907 hybrid horsepower (667 kilowatts) sends the Temerario to a blistering 343 kph (213 mph) top speed. The electric motors ably fill any gaps in gasoline acceleration and finesse the handling with torque-vectoring, the electrified front wheels helping to catapult the Lamborghini out of corners with ridiculous ease.
With their compact design and superior power-to-weight ratio, these motors are setting records on land, sea and air. The world’s fastest electric plane, the Rolls-Royce Spirit of Innovation, integrated three YASA motors for its propeller, sending it to a record 559.9 kph (345.4 mph) top speed. Applying tech from its Formula E racing program, Jaguar used YASA motors to set a maritime electric speed record of 142.6 kph (88 mph) in England’s Lake District in 2018 (that record has since been broken).
In August, YASA’s motors helped the Mercedes-AMG GT XX prototype set dozens of EV endurance records. Cruising around Italy’s Nardo circuit at a sustained 186 mph (300 kph), the roughly 1,000-kilowatt (1,360 horsepower) Mercedes EV drove about 5,300 kilometers per day. In 7.5 days, it traveled 40,075 kilometers (24,902 miles), the exact equivalent of the earth’s circumference. That time included stops for charging, at 850 kilowatts.
Mercedes F1 driver George Russell stands next to a Mercedes AMG GT XX during its record-setting endurance run this past August. Powered by three YASA axial-flux motors, the concept EV drove the equivalent of the earth’s circumference in 7.5 days, at a near-steady 300 kph. A production version of the car could be a competitor for the Porsche Taycan. Mercedes-Benz
Mercedes bought YASA outright in 2021. Daimler, Mercedes’ corporate parent, is retrofitting a factory in Berlin to build up to 100,000 YASA motors a year, for the next logical step: The motors will power mass-produced EVs for the first time, specifically from AMG, Mercedes’ formidable high-performance division.
The company recently unveiled its latest motor, and its stats are eye-opening: The axial-flux prototype generates a peak 750 kilowatts, or 1,005 horsepower, as tested on a dynamometer. The motor can output a continuous 350-400 kilowatts (469-536 horsepower). Yet the unit weighs just 12.7 kilograms (27.9 pounds). Woolmer says the resulting power density of 59 kilowatts per kilogram is an unofficial world record for an electric motor, and about three times that of leading radial-flux designs, including Tesla’s.
“And this isn’t a concept on a screen — it’s running, right now, on the dynos,” Woolmer says. “We’ve built an electric motor that’s significantly more power-dense than anything before it, all with scalable materials and processes.”
Simon Odling, YASA’s chief of new technology, walks me through the tech. Conventional, radial-flux motors are shaped like a sausage roll. A spinning rotor is housed within a stationary stator. The lines of magnetic flux are oriented radially, perpendicular to the motor’s central shaft. These flux lines represent the interacting magnetic fields of the permanent magnets in the rotor and electromagnets in the stator. It is that interaction that provides torque.
An axial flux design is more like a pancake. In YASA’s configuration, a pair of much-larger rotors are positioned on either side of the stator, and, notably, all three have roughly the same diameter. Magnetic flux is oriented axially, parallel to the shaft. Because torque is proportional to the rotor diameter squared, an axial-flux design can generate substantially more torque than a comparable radial-flux unit. The dual permanent-magnetic rotors double the key torque-generating components, and ensure a short magnetic path, which enhances efficiency by reducing losses in the magnetic field.
YASA R&D Engineer Eddie Martin holds a 12.7 kg axial-flux motor that cranks out 750 kilowatts/1,005 horsepower.YASA
Odling says the company’s motors are about one-third the mass and length of a comparable radial-flux machine, with intriguing upsides for vehicle packaging and weight savings. “The motor sits between an engine and gearbox really nicely in a hybrid application, or it makes for a very compact drive unit in an EV,” Odling says. The configuration is also ideal for in-wheel motors, because the flat shape fits easily within the width of car and even motorcycle wheels.
YASA also touts the weight savings. Cascading gains in vehicle architecture could eliminate at least 200 kilograms from today’s EVs, the company says, about half from the motors themselves, the rest from smaller batteries, brakes, and lighter-weight supporting structures.
The company’s name offers another clue to its technical edge: YASA stands for “Yokeless and Segmented Architecture.” The motors remove a heavy iron or steel yoke, the structural and magnetic backbone for the copper coils in a conventional stator. Instead, they use a Soft Magnetic Composite (SMC)—a material that has very high magnetic permeability. That characteristic means the material is a very effective conductor of magnetic flux, so it can be used to concentrate and direct the field in the motor. In a typical application, the stator’s coils are wrapped around structures made of SMC.
Woolmer began studying SMCs in the mid 2000s before there were potential paying customers for his nascent motor designs: The first Tesla Roadster didn’t hit the road until 2008, and suppliers and tooling for these motors didn’t exist then. Woolmer’s early axial-flux designs finally made their way into the Jaguar C-X75 in 2010, a concept that was cancelled prior to production. By 2019, Ferrari was integrating one of Woolmer’s motors in its first hybrid, the SF90.
SMC became a key innovation, because axial-flux motors couldn’t be manufactured with the stacked-steel laminations of radial-flux machines. Woolmer segmented the stator into individual “pole pieces” made from SMC, which can be formed under pressure into a huge variety of 3D shapes. That flexibility greatly reduces weight and eddy-current losses, and lessens the cooling burden. Where a conventional motor might have 30 kilograms of iron, a comparable YASA design would need only 5 kilograms to generate the same power and torque.
YASA’s stators also integrate flat copper windings with direct oil cooling, Odling says, with no “buried copper” that the oil can’t reach. That greatly improves thermal performance and recovery in stressful conditions, a potential boon for high-performance EVs.
YASA designs and develops its motors at its Oxford Innovation Center. In May, it opened a new axial-motor “super factory” in nearby Yarnton, with capacity for more than 25,000 motors each year. The company also credits the British Advanced Propulsion Center (APC) as a linchpin of its expansion. The collaboration between the U.K. government, industry and academia looks to accelerate homegrown development of zero-emissions transportation to meet Net Zero targets.
YASA intends to release more specifics on its latest prototype motor in December. But company executives say the motor is ready for customers, with no exotic materials or manufacturing techniques required.
2025-11-20 00:00:16
As artificial intelligence reshapes every industry, universities face a critical choice: lead the transformation or risk falling behind. The institutions that integrate AI across disciplines, invest in computing infrastructure, and conduct groundbreaking research will become destinations for top students, faculty, and research funding.
This industry brief provides a practical roadmap for building a comprehensive AI strategy that drives enrollment, attracts research dollars, and delivers career-ready graduates.
2025-11-20 00:00:02
It seems like every week there’s a new video of a robot folding clothes. We’ve had some fantastic demonstrations, like this semi-autonomous video from Weave Robotics on X.
It’s awesome stuff, but Weave is far from the only company producing these kinds of videos. Figure 02 is folding clothes. Figure 03 is folding clothes. Physical Intelligence launched their flagship vision-language-action model, pi0, with an amazing video of a robot folding clothes after unloading a laundry machine. You can see robots folding clothes live at robotics expos. Even before all this, Google showed clothes folding in their work, ALOHA unleashed. 7X Tech is even planning to sell robots to fold clothes!
And besides folding actual clothes, there are other clothes-folding-like tasks, like Dyna’s napkin folding— which leads to what is probably my top robot video of the year, demonstrating 18 hours of continuous napkin folding. So why are all of these robotic manipulation companies suddenly into folding?
There’s work going back over a decade that shows some amount of robotic clothes folding. But these demonstrations were extremely brittle, extremely slow, and not even remotely production ready. Previous solutions existed (even learning based solutions!) but they relied on precise camera calibration, or on carefully hand-designed features, meaning that these clothes folding demos generally worked only on one robot, in one environment, and may have only ever worked a single time—just enough for the recording of a demo video or paper submission.
With a little bit of help from a creatively patterned shirt, PR2 was folding things back in 2014.Bosch/IEEE
Take a look at this example of UC Berkeley’s PR2 folding laundry from 2014. This robot is, in fact, using a neural network policy. But that policy is very small and brittle; it picks and places objects against the same green background, moves very slowly, and can’t handle a wide range of shirts. Making this work in practice would require larger models, pretrained on web-scale data, and better, more general techniques for imitation learning.
And so 10 years later, with the appropriate demonstration data, many different startups and research labs have been able to implement clothes-folding demos; it’s something we have seen from numerous hobbyists and startups, using broadly similar tools (like LeRobot from HuggingFace), without intense specialization.
Many of us who work in robotics have this ‘north star’ of a robot butler which can do all the chores we don’t want to do. Mention clothes folding, and many, many people will chime in about how they don’t ever want to fold clothes again and are ready to part with basically any amount of money to make that happen.
This is important for the companies involved as well. Companies like Figure and 1x have been raising large amounts of money predicated on the idea that they will be able to automate many different jobs, but increasingly these companies seem to want to start in the home.
Dyna Robotics can fold an indefinite number of napkins indefinitely.Dyna Robotics
And that’s part of the magic of these demos. While they’re slow, and imperfect, everyone can start to envision how this technology becomes the thing that we all want: a robot that can exist in our house and mitigate all those everyday annoyances that take up our time.
These robot behaviors are produced by models trained via imitation learning. Modern imitation learning methods like Diffusion Policy use techniques inspired by generative AI to produce complex, dexterous robot trajectories, based on examples of expert human behavior that’s been provided to them—and they often need many, many trajectories. The work ALOHA Unleashed by Google is a great example, needing about 6,000 demonstrations to learn how to, for example, tie a pair of shoelaces. For each of these demonstrations, a human piloted a pair of robot arms while performing the task; all of this data was then used to train a policy.
We need to keep in mind what’s hard about these demonstrations. Human demonstrations are never perfect, nor are they perfectly consistent; for example, two human demonstrators will never grab the exact same part of an object with sub-millimeter precision. That’s potentially a problem if you want to screw a cover in place on top of a machine you’re building, but it’s not a problem at all for folding clothes, which is fairly forgiving. This has two knock-on effects:
For similar reasons, it’s great that with cloth folding, you can fix your cameras in just the right position. When learning a new skill, you need training examples with “coverage” of the space of environments you expect to see at deployment time. So the more control you have, the more efficient the learning process will be—the less data you’ll need, and the easier it will be to get a flashy demo. Keep this in mind when you see a robot folding things on a plain tabletop or with an extremely clean background; that’s not just nice framing, it helps the robot out a lot!
And since we’ve committed to collecting a ton of data—dozens of hours—to get this task working well, mistakes will be made. It’s very useful, then, if it’s easy to reset the task, i.e. restore it to a state from which you can try the task again. If something goes wrong folding clothes, it’s fine. Simply pick the cloth up, drop it, and it’s ready for you to start over. This wouldn’t work if, say, you were stacking glasses to put away in a cupboard, since if you knock over the stack or drop one on the floor, you’re in trouble.
Clothes folding also avoids making forceful contact with the environment. Once you’re exerting a lot of pressure, things can break, the task can become non-resettable, and demonstrations are often much harder to collect because forces aren’t as easily observable to the policy. And every piece of variation (like the amount of force you’re exerting) will end up requiring more data so the model has “coverage” of the space it’s expected to operate in.
While we’re seeing a lot of clothes-folding demos now, I still feel, broadly, quite impressed with many of them. As mentioned above, Dyna was one of my favorite demos this year, mostly because longer-running robot policies have been so rare until now. But they were able to demonstrate zero-shot folding (meaning folding without additional training data) at a couple of different conferences, including Actuate in San Francisco and the Conference on Robot Learning (CoRL) in Seoul. This is impressive and actually very rare in robotics, even now.
In the future, we should hope to see robots that can handle more challenging and dynamic interactions with their environments: moving more quickly, moving heavier objects, and climbing or otherwise handling adverse terrain while performing manipulation tasks.
But for now, remember that modern learning methods will come with their own strengths and weaknesses. It seems that, while not easy, clothes folding is the kind of task that’s just really well suited for what our models can do right now. So expect to see a lot more of it.