A critique published in Nature Wednesday calls the basic technology behind Microsoft’s “breakthrough” quantum computing chip the Majorana 1 into question. Microsoft unveiled the chip in February 2025 and said it featured a brand-new technology known as a topological qubit. Topological qubits, they said, would be the “building blocks” for their future quantum computer. Microsoft announced the next generation chip Majorana 2 at Build earlier this month.
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Robots have gotten very good at moving fast, repeating steps and doing jobs that would wear you and me out. But ask a robot to pick up something delicate, oddly shaped or slightly different from the last item it handled, and things can get a little complicated quickly.
That is where a new collaboration between ABB Robotics and PSYONIC comes in. ABB Robotics is working with PSYONIC, a California bionics company, to explore whether real-world touch and motion data from human prosthetic use can help train robotic arms.
In other words, the same kind of bionic hand that helps a person grip a tool, pick up a fragile object or adjust pressure in real time could help teach robots how to do those tasks better.
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SOFT ROBOTIC ARMBAND GIVES PROSTHETIC HAND USERS NATURAL CONTROL
The PSYONIC Ability Hand can capture touch, motion and grip-force data from real human prosthetic use. (ABB Robotics)
How a bionic hand could teach a robot
The collaboration centers on PSYONIC’s Ability Hand and ABB’s GoFa cobot. The Ability Hand was originally developed for prosthetic use. It has multi-articulating fingers, pressure sensors, vibration feedback and flexible mechanics that help it conform to irregular objects. That combination is important because human grip isn’tt one fixed action. You hold a coffee cup differently than a screwdriver. You handle an egg differently than a phone. Most of us do that without thinking about it.
For robots, that instinctive adjustment is hard. ABB and PSYONIC want to explore how movement, contact and grip-force data from the Ability Hand can help train robots to handle objects that are fragile, uneven or unpredictable. ABB’s GoFa cobot brings the industrial side of the equation, offering the accuracy and repeatability needed to test those movements in a controlled way. The result could be a robot arm that learns from real human handling data, then applies that information to factory and warehouse tasks.
Why robot grip is such a hard problem
Industrial robots can already lift, move, weld, sort and assemble with impressive speed. However, many still struggle when a task involves subtle touch. Think about a robot picking up a soft package, a medical component or a part that shifts slightly on a conveyor belt. Too much pressure can damage the item. Too little pressure can make the robot drop it. A tiny change in angle can throw off the whole process.
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That is why gripping and dexterity remain major challenges in automation. ABB calls this a key part of Autonomous Versatile Robotics, or AVR, its vision for robots that can sense, reason, move and handle objects with precision in changing environments.
Marc Segura, president of ABB Robotics, put it this way: Human dexterity remains “one of the most difficult things to replicate in industrial-grade robotics.” He said the collaboration with PSYONIC could help “close the long-standing gap” between human and robot dexterity. That gap is where this technology could make a real difference.
What makes the PSYONIC Ability Hand different
The PSYONIC Ability Hand was built to help people. It uses myoelectric control, touch sensing and compliant mechanics in a lightweight design. Its sensors can detect pressure during a grip, while vibration feedback can help communicate touch back to the person using it. That same sensing ability could be valuable for robots.
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PSYONIC says the Ability Hand can capture detailed data about movement, contact and grip force. When that hand is used by people in real-world situations, it can generate a more natural dataset than a lab-only robot demonstration.
ABB’s GoFa cobot is being used to test how bionic hand data could help robots handle delicate and irregular objects. (ABB Robotics)
Dr. Aadeel Akhtar, founder and CEO of PSYONIC, called dexterous manipulation “a data challenge as much as a hardware challenge.” That line really gets to the heart of this. Better robot hands are important. Yet the training data behind those hands may be what decides how useful they become in real workplaces.
Where bionic hand data could show up first
ABB and PSYONIC say this work could apply across automotive, aerospace, packaging, logistics and life sciences. That makes sense. These are industries where robots already play a major role, but where delicate or variable handling can still slow things down. A robot that can better adjust its grip could help with fragile components, oddly shaped products, soft packaging or repetitive tasks that are tough on the body.
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The International Federation of Robotics has also pointed to advanced gripping and digital integration as a way to reduce engineering time by up to 30%. That’s important for companies because automation often gets delayed by setup, tuning and custom engineering. If touch-enabled robotic hands can reduce some of that work, companies could deploy robots faster and use them in more flexible ways.
How touch-trained robots could change factory work
There is a hopeful side to this. Robots that handle repetitive or ergonomically challenging work could reduce strain on people. That could mean fewer workers stuck doing the same painful motion all day. However, there is also a bigger labor question here. More capable robots could take on tasks that once seemed too variable to automate. That may affect how companies hire, train and assign work in the future.
The most useful version of this technology would support people instead of simply replacing them. For example, robots could handle the repetitive gripping while workers focus on oversight, quality checks, machine setup and higher-skill work.
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Kurt’s key takeaways
ABB Robotics and PSYONIC are taking a different approach to one of robotics’ hardest problems: touch. Instead of training robots only in a lab, they want to use real movement and grip data from a bionic hand that people already use. That could help robots become better at delicate, variable tasks that have traditionally been hard to automate. It could also push industrial robots closer to working safely and effectively around humans in more settings. But the human side should not get lost in the excitement. If robots are going to learn from human touch, companies need to be clear about data use, workplace impact and safety testing.
The collaboration could help robots become more useful in factories, warehouses and other workplaces where precise grip matters. (ABB Robotics)
Would you feel comfortable knowing a robot at work was trained using real human touch data? Let us know by writing to us at CyberGuy.com.
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Amazon’s Prime Day is now in its second day, and whether you’re looking for a new pair of wireless earbuds or a smartwatch, there’s a good chance you’ll find a discount. The Apple Watch Series 11 has already dropped to a new low price, while the AirPods Pro 3 are discounted to $179. With Tim Cook warning that price hikes are coming, now may be the moment if you’ve been eyeing one of the company’s devices.
Below are the best Apple deals currently available. Some are exclusive to Prime Day, while others are simply great discounts we think are worth highlighting. We’ll continue updating this guide throughout Prime Day, highlighting more deals as they become available.
Earbud and headphone deals
Update, June 24th: Adjusted prices and availability, and added deals for Apple’s MagSafe Charger as well as the Apple Magic Keyboard.
But in a peer-reviewed article, Henry Legg, a physicist at the University of St Andrews, reanalyzed Microsoft’s data on their device and argued that the company’s researchers did not conclusively demonstrate a working topological qubit in the first place.
Theory predicts that the electrons in this wire behave in a collective pattern known as a Majorana particle, for which the chip is named.
Proponents of quantum computing predict that the technology’s computational abilities will advance new medicine discovery, encryption, and machine learning. Companies like Google and IBM have already demonstrated more advanced machines than Majorana 1 or 2, although presently, no one has conclusively gotten any quantum computer to perform anything useful. But Microsoft claimed that Majorana 1, and subsequently Majorana 2, paved their path toward a practical quantum computer.
Microsoft’s design, unique among quantum computing companies, involves a tiny wire, thinner than a human hair, made of the semiconductor indium arsenide stuck to a superconductor. Theory predicts that the electrons in this wire behave in a collective pattern known as a Majorana particle, for which the chip is named. Microsoft wants to encode information in the properties of the Majorana particle. (A topological qubit is to a Majorana particle as a transistor is to silicon.)
Proponents of the Majorana particle think it is promising qubit material because theory predicts that when formed into topological qubits, the Majorana should compute with fewer errors than competing materials, such as superconducting circuits pursued by IBM. This suggests that ultimately, fewer topological qubits are needed to scale up to a useful quantum computer.
That is, if Microsoft has actually made a Majorana particle. “They haven’t convincingly shown that they have Majoranas,” Legg told The Verge. “You can’t make a qubit if you don’t have the Majoranas.”
In Legg’s critique, he writes that what Microsoft claims as a signature of the Majorana particle could actually be from the formation of quantum dots, which are electron-containing structures, in the device. Quantum dots would not be useful for building the quantum computer. He also writes that Microsoft cherry-picked their data.
“You can’t make a qubit if you don’t have the Majoranas.”
Microsoft’s team published a rebuttal in Nature disputing Legg’s interpretation of their data. Legg’s critique “does not constitute a substantial scientific challenge to our findings,” the Microsoft team wrote. Legg has not “proposed an alternative model that fits all of our data,” Chetan Nayak, a physicist leading Microsoft’s quantum team, told The Verge.
Legg first posted his critique on the online physics repository arXiv on February 26, 2025, within a week of Microsoft’s Majorana 1 announcement. It took a year for Nature to conduct a peer review and publish his article.
Meanwhile, on June 2, Microsoft announced a new chip, the Majorana 2, featuring what they claimed was the next generation of their topological qubits. The company says they can build a “scalable quantum computer” by 2029. “We 100% stand behind our results,” Nayak told The Verge. “We stand by our roadmap. We stand behind our long-standing commitment to scientific rigor and dialogue.”
Legg says the company’s characterization of Majorana 2, which Microsoft wrote in a non-peer reviewed manuscript, suffers from similar problems he pointed out a year ago. “Nothing in this [manuscript] resolves the fundamental issues that so many scientists have with this company’s previous claims,” Legg told The Verge.
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