Aurelia Butler

On the Moon, there are raw materials that humanity could one day mine and use. Various space agencies, such as the European Space Agency (ESA), are already planning missions to better explore Earth’s satellite and find minerals. This calls for appropriate exploration vehicles. Swiss researchers led by ETH Zurich are now pursuing the idea of sending not just one solitary rover on an exploration tour, but rather an entire team of vehicles and flying devices that complement each other.
The researchers equipped three ANYmal — a type of legged robot developed at ETH — with a range of measuring and analysis instruments that would potentially make them suitable exploration devices in the future. They tested these robots on various terrains in Switzerland and at the European Space Resources Innovation Centre (ESRIC) in Luxembourg, where, a few months ago, the Swiss team won a European competition for lunar exploration robots together with colleagues from Germany. The competition involved finding and identifying minerals on a test site modelled after the surface of the Moon. In the latest issue of the journal Science Robotics, the scientists describe how they go about exploring an unknown terrain using a team of robots.
Insurance against failure
“Using multiple robots has two advantages,” explains Philip Arm, a doctoral student in the group led by ETH Professor Marco Hutter. “The individual robots can take on specialised tasks and perform them simultaneously. Moreover, thanks to its redundancy, a robot team is able to compensate for a teammate’s failure.” Redundancy in this case means that important measuring equipment is installed on several robots. In other words, redundancy and specialisation are opposing goals. “Getting the benefits of both is a matter of finding the right balance,” Arm says.
The researchers at ETH Zurich and the Universities of Basel, Bern and Zurich solved this problem by equipping two of the legged robots as specialists. One robot was programmed to be particularly good at mapping the terrain and classifying the geology. It used a laser scanner and several cameras — some of them capable of spectral analysis — to gather initial clues about the mineral composition of the rock. The other specialist robot was taught to precisely identify rocks using a Raman spectrometer and a microscopy camera.
The third robot was a generalist: it was able to both map the terrain and identify rocks, which meant that it had a broader range of tasks than the specialists. However, its equipment meant that it could perform these tasks with less precision. “This makes it possible to complete the mission should any one of the robots malfunction,” Arm says.
Combination is key
At the ESRIC and ESA Space Resources Challenge, the jury was particularly impressed that the researchers had built redundancy into their exploration system to make it resilient to potential failures. As a prize, the Swiss scientists and their colleagues from the FZI Research Center for Information Technology in Karlsruhe, were awarded a one-year research contract to further develop this technology. In addition to legged robots, this work will also involve robots with wheels, building on the FZI researchers’ experience with such robots.
“Legged robots like our ANYmal cope well in rocky and steep terrain, for example when it comes to climbing down into a crater,” explains Hendrik Kolvenbach, a senior scientist in Professor Hutter’s group. Robots with wheels are at a disadvantage in these kinds of conditions, but they can move faster on less challenging terrain. For a future mission, it would therefore make sense to combine robots that differ in terms of their mode of locomotion. Flying robots could also be added to the team.
The researchers also plan to make the robots more autonomous. Presently, all data from the robots flows into a control centre, where an operator assigns tasks to the individual robots. In the future, semi-autonomous robots could directly assign certain tasks to each other, with control and intervention options for the operator.
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The same AI technology used to mimic human art can now synthesize artificial scientific data, advancing efforts toward fully automated data analysis.
Researchers at the University of Illinois Urbana-Champaign have developed an AI that generates artificial data from microscopy experiments commonly used to characterize atomic-level material structures. Drawing from the technology underlying art generators, the AI allows the researchers to incorporate background noise and experimental imperfections into the generated data, allowing material features to be detected much faster and more efficiently than before.
“Generative AIs take information and generate new things that haven’t existed before in the world, and now we’ve leveraged that for the goal of automated data analysis,” said Pinshane Huang, a U. of I. professor of materials science and engineering and a project co-lead. “What is used to make paintings of llamas in the style of Monet on the internet can now make scientific data so good it fools me and my colleagues.”
Other forms of AI and machine learning are routinely used in materials science to assist with data analysis, but they require frequent, time-consuming human intervention. Making these analysis routines more efficient requires a large set of labeled data to show the program what to look for. Moreover, the data set needs to account for a wide range of background noise and experimental imperfections to be effective, effects that are difficult to model.
Since collecting and labeling such a vast data set using a real microscope is infeasible, Huang worked with U. of I. physics professor Bryan Clark to develop a generative AI that could create a large set of artificial training data from a comparatively small set of real, labeled data. To achieve this, the researchers used a cycle generative adversarial network, or CycleGAN.
“You can think of a CycleGAN as a competition between two entities,” Clark said. “There’s a ‘generator’ whose job is to imitate a provided data set, and there’s a ‘discriminator’ whose job is to spot the differences between the generator and the real data. They take turns trying to foil each other, improving themselves based on what the other was able to do. Ultimately, the generator can produce artificial data that is virtually indistinguishable from the real data.”
By providing the CycleGAN with a small sample of real microscopy images, the AI learned to generate images that were used to train the analysis routine. It is now capable of recognizing a wide range of structural features despite the background noise and systematic imperfections.
“The remarkable part of this is that we never had to tell the AI what things like background noise and imperfections like aberration in the microscope are,” Clark said. “That means even if there’s something that we hadn’t thought about, the CycleGAN can learn it and run with it.”
Huang’s research group has incorporated the CycleGAN into their experiments to detect defects in two-dimensional semiconductors, a class of materials that is promising for applications in electronics and optics but is difficult to characterize without the aid of AI. However, she observed that the method has a much broader reach.
“The dream is to one day have a ‘self-driving’ microscope, and the biggest barrier was understanding how to process the data,” she said. “Our work fills in this gap. We show how you can teach a microscope how to find interesting things without having to know what you’re looking for.” More

Nothing exists in a vacuum, but physicists often wish this weren’t the case. If the systems that scientists study could be completely isolated from the outside world, things would be a lot easier.
Take quantum computing. It’s a field that’s already drawing billions of dollars in support from tech investors and industry heavyweights including IBM, Google and Microsoft. But if the tiniest vibrations creep in from the outside world, they can cause a quantum system to lose information.
For instance, even light can cause information leaks if it has enough energy to jiggle the atoms within a quantum processor chip.
“Everyone is really excited about building quantum computers to answer really hard and important questions,” said Joe Kitzman, a doctoral student at Michigan State University. “But vibrational excitations can really mess up a quantum processor.”
But, with new research published in the journal Nature Communications, Kitzman and his colleagues are showing that these vibrations need not be a hindrance. In fact, they could benefit quantum technology.
“If we can understand how the vibrations couple with our system, we can use that as a resource and a tool for creating and stabilizing some types of quantum states,” Kitzman said.

What that means is that researchers can use these results to help mitigate information lost by quantum bits, or qubits (pronounced “q bits”).
Conventional computers rely on a clear-cut binary logic. Bits encode information by taking on one of two distinct possible states, often denoted as zero or one. Qubits, however, are more flexible and can exist in states that are simultaneously both zero and one.
Although that may sound like cheating, it’s well within the rules of quantum mechanics. Still, this feature should give quantum computers valuable advantages over conventional computers for certain problems in a variety of areas, including science, finance and cybersecurity.
Beyond its implications for quantum technology, the MSU-led team’s report also helps set the stage for future experiments to better explore quantum systems in general.
“Ideally, you want to separate your system from the environment, but the environment is always there,” said Johannes Pollanen, the Jerry Cowen Endowed Chair of Physics in the MSU Department of Physics and Astronomy. “It’s almost like junk you don’t want to deal with, but you can learn all kinds of cool stuff about the quantum world when you do.”
Pollanen also leads the Laboratory for Hybrid Quantum Systems, of which Kitzman is a member, in the College of Natural Science. For the experiments led by Pollanen and Kitzman, the team built a system consisting of a superconducting qubit and what are known as surface acoustic wave resonators.

These qubits are one of the most popular varieties among companies developing quantum computers. Mechanical resonators are used in many modern communications devices, including cellphones and garage door openers, and now, groups like Pollanen’s are putting them to work in emerging quantum technology.
The team’s resonators allowed the researchers to tune the vibrations experienced by qubits and understand how the mechanical interaction between the two influenced the fidelity of quantum information.
“We’re creating a paradigm system to understand how this information is scrambled,” said Pollanen. “We have control over the environment, in this case, the mechanical vibrations in the resonator, as well as the qubit.”
“If you can understand how these environmental losses affect the system, you can use that to your advantage,” Kitzman said. “The first step in solving a problem is understanding it.”
MSU is one of only a few places equipped and staffed to perform experiments on these coupled qubit-mechanical resonator devices, Pollanen said, and the researchers are excited to use their system for further exploration. The team also included scientists from the Massachusetts Institute of Technology and the Washington University in St. Louis. More

Researchers at the Department of Energy’s SLAC National Accelerator Laboratory and Stanford University say they’ve found a way to make thin films of an exciting new nickel oxide superconductor that are free of extended defects.
Not only does this improve the material’s ability to conduct electricity with no loss, they said, but it also allows them to discover its true nature and properties, both in and out of the superconducting state, for the first time.
Their first look at a superconducting nickel oxide, or nickelate, that does not have defects revealed that it is more like the cuprates – which hold the world’s high-temperature record for unconventional superconductivity at normal pressures — than previously thought. For instance, when the nickelate is tweaked to optimize its superconductivity and then heated above its superconducting temperature, its resistance to the flow of electric current increases in a linear fashion, just as in cuprates.
Those striking similarities, they said, may mean these two very different materials achieve superconductivity in much the same way.
It’s the latest step in a 35-year quest to develop superconductors that can operate at close to room temperature, which would revolutionize electronics, transportation, power transmission and other technologies by allowing them to operate without energy-wasting electrical resistance.
The research team, led by Harold Hwang, director of the Stanford Institute for Materials and Energy Sciences (SIMES) at SLAC, described their work today in the journal Nature.

“Nickelate films are really unstable, and until now our efforts to stabilize them on top of other materials have produced defects that are like speed bumps for electrons,” said Kyuho Lee, a SIMES postdoctoral researcher who contributed to the discovery of superconductivity in nickelates four years ago and has been working on them ever since.
“These quality issues have led to many debates and open questions about nickelate properties, with research groups reporting widely varying results,” Lee said. “So eliminating the defects is a significant breakthrough. It means we can finally address the underlying physics behind these materials and behind unconventional superconductivity in general.”
Jenga chemistry and a just-right fit
The defects, which are a bit like misaligned zipper teeth, arise from the same innovative process that allowed Hwang’s team to create and stabilize a nickelate film in the first place.
They started by making a common material known as perovskite. They “doped” it to change its electrical conductivity, then exposed it to a chemical that deftly removed layers of oxygen atoms from its molecular structure, much like removing a stick from a tower of Jenga blocks. With the oxygen layers gone, the film settled into a new structure — known as an infinite-layer nickelate -that can host superconductivity.

The atomic latticework of this new structure occupied a slightly bigger surface area than the original. With this in mind, they had built the film on a foundation, or substrate, that would be a good fit for the finished, spread-out product, Lee said.
But it didn’t match the atomic lattice of the starting material, which developed defects as it tried to fit comfortably onto the substrate — and those imperfections carried through to the finished nickelate.
Hwang said it’s as if two friends of different sizes had to share a coat. If the coat fit the smaller friend perfectly, the larger one would have a hard time zipping it up. If it fit the larger friend perfectly, it would hang like a tent on the smaller one and let the cold in. An in-between size might not be the best fit for either of them, but it’s close enough to keep them both warm and happy.
That’s the solution Lee and his colleagues pursued.
In a series of meticulous experiments, they used a substrate that was like the in-between coat. The atomic structure of its surface was a close enough fit for both the starting and ending materials that the finished nickelate came out defect-free. Lee said the team is already starting to see some interesting physics in the nickelate now that the system is much cleaner.
“What this means,” Hwang said, “is that we are getting closer and closer to measuring the intrinsic properties of these materials. And by sharing the details of how to make defect-free nickelates, we hope to benefit the field as a whole.”
Researchers from Cornell University contributed to this work, which was funded by the DOE Office of Science and the Gordon and Betty Moore Foundation’s Emergent Phenomena in Quantum Systems Initiative. More

A research group from Nagoya University has accurately simulated air turbulence occurring on clear days around Tokyo using Japan’s fastest supercomputer. They then compared their findings with flight data to create a more accurate predictive model. The research was reported in the journal Geophysical Research Letters.
Although air turbulence is usually associated with bad weather, an airplane cabin can shake violently even on a sunny and cloudless day. Known as clear air turbulence (CAT), these turbulent air movements can occur in the absence of any visible clouds or other atmospheric disturbances. Although the exact mechanisms that cause CAT are not fully understood, it is believed to be primarily driven by wind shear and atmospheric instability.
CAT poses a high risk to aviation safety. The sudden turbulence on an otherwise calm day can lead to passenger and crew member injuries, aircraft damage, and disruptions to flight operations. Pilots rely on reports from other aircraft, weather radar, and atmospheric models to anticipate and avoid areas of potential turbulence. However, since CAT shows no visible indicators, such as clouds or storms, it is particularly challenging to detect and forecast.
As winds swirl and circulate creating sudden changes in airflow, eddies are created that can shake an aircraft. Therefore, to better understand CAT, scientists model it using large-eddy simulation (LES), a computational fluid dynamics technique used to simulate these turbulent flows. However, despite its importance to research on air turbulence, one of the greatest challenges of LES is the computational cost. Simulating the complex interactions involved in LES requires high levels of computing power.
To elaborately simulate the process of turbulence generation using high-resolution LES, the research group from Nagoya University turned to an exascale computer called the Fugaku supercomputer. It is a high-performance computing system, currently ranked as the world’s second fastest supercomputer.
Using Fugaku’s immense computational power, Dr. Ryoichi Yoshimura of Nagoya University in collaboration with Dr. Junshi Ito and others at Tohoku University, performed an ultra-high-resolution simulation of the CAT above Tokyo’s Haneda airport in winter caused by low pressure and a nearby mountain range.
They found that the wind speed disturbance was caused by the collapse of the Kelvin-Helmholtz instability wave, a specific type of instability that occurs the interface between two layers of air with different velocities. As one layer has higher velocity than the other, it creates a wave-like effect as it pulls at the lower velocity layer. As the atmospheric waves grow from the west and collapse in the east, this phenomenon creates several fine vortices, creating turbulence.
After making their computations, the group needed to confirm whether their simulated vortices were consistent with real-world data. “Around Tokyo, there is a lot of observational data available to validate our results,” said Yoshimura. “There are many airplanes flying over the airports, which results in many reports of turbulence and the intensity of shaking. Atmospheric observations by a balloon near Tokyo were also used. The shaking data recorded at that time was used to show that the calculations were valid.”
“The results of this research should lead to a deeper understanding of the principle and mechanism of turbulence generation by high-resolution simulation and allow us to investigate the effects of turbulence on airplanes in more detail,” said Yoshimura. “Since significant turbulence has been shown to occur in the limited 3D region, routing without flying in the region is possible by adjusting flight levels if the presence of active turbulence is known in advance. LES would provide a smart way of flying by providing more accurate turbulence forecasts and real-time prediction.” More

The hottest drink of the summer may be the SEAS-colada. Here’s what you need to make it: gin, pineapple juice, coconut milk and a dielectric elastomer actuator-based soft peristaltic pump. Unfortunately, the last component can only be found in the lab of Robert Wood, the Harry Lewis and Marlyn McGrath Professor of Engineering and Applied Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences.
At least, for now.
Wood and his team designed the pump to solve a major challenge in soft robotics — how to replace traditionally bulky and rigid power components with soft alternatives.
Over the past several years, Wood’s Microrobotics Lab at SEAS has been developing soft analogues of traditionally rigid robotic components, including valves and sensors. In fluid-driven robotic systems, pumps control the pressure or flow of the liquid that powers the robot’s movement. Most pumps available today for soft robotics are either too large and rigid to fit onboard, not powerful enough for actuation or only work with specific fluids.
Wood’s team developed a compact, soft pump with adjustable pressure flow versatile enough to pump a variety of fluids with varying viscosity, including gin, juice, and coconut milk, and powerful enough to power soft haptic devices and a soft robotic finger.
The pump’s size, power and versatility opens up a range of possibilities for soft robots in a variety of applications, including food handling, manufacturing, and biomedical therapeutics.

The research was published recently in Science Robotics.
Peristaltic pumps are widely used in industry. These simple machines use motors to compress a flexible tube, creating a pressure differential that forces liquid through the tube. These types of pumps are especially useful in biomedical applications because the fluid doesn’t touch any component of the pump itself.
“Peristaltic pumps can deliver liquids with a wide range of viscosities, particle-liquid suspensions, or fluids such as blood, which are challenging for other types of pumps,” said first author Siyi Xu, a former graduate student at SEAS and current postdoctoral fellow in Wood’s lab.
Building off previous research, Xu and the team designed electrically powered dielectric elastomer actuators (DEAs) to act as the pump’s motor and rollers. These soft actuators have ultra-high power density, are lightweight, and can run for hundreds of thousands of cycles.
The team designed an array of DEAs that coordinate with each other, compressing a millimeter-sized channel in a programmed sequence to produce pressure waves.

The result is a centimeter-sized pump small enough to fit on board a small soft robot and powerful enough to actuate movement, with controllable pressure, flow rate, and flow direction.
“We also demonstrated that we could actively tune the output from continuous flow to droplets by varying the input voltages and the outlet resistance, in our case the diameter of the blunt needle,” said Xu. “This capability may allow the pump to be useful not only for robotics but also for microfluidic applications.”
“The majority of soft robots contain rigid components somewhere along their drivetrain,” said Wood. “This topic started as an effort to swap out one of those key pieces, the pump, with a soft alternative. But along the way we realized that compact soft pumps may have far greater utility, for example in biomedical settings for drug delivery or implantable therapeutic devices.”
The research was co-authored by Cara M. Nunez and Mohammad Souri. It was supported by the National Science Foundation under grant CMMI-1830291.
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Mars rovers have teams of human experts on Earth telling them what to do. But robots on lander missions to moons orbiting Saturn or Jupiter are too far away to receive timely commands from Earth. Researchers in the Departments of Aerospace Engineering and Computer Science at the University of Illinois Urbana-Champaign developed a novel learning-based method so robots on extraterrestrial bodies can make decisions on their own about where and how to scoop up terrain samples.
“Rather than simulating how to scoop every possible type of rock or granular material, we created a new way for autonomous landers to learn how to learn to scoop quickly on a new material it encounters,” said Pranay Thangeda, a Ph.D. student in the Department of Aerospace Engineering.
“It also learns how to adapt to changing landscapes and their properties, such as the topology and the composition of the materials,” he said.
Using this method, Thangeda said a robot can learn how to scoop a new material with very few attempts. “If it makes several bad attempts, it learns it shouldn’t scoop in that area and it will try somewhere else.”
The proposed deep Gaussian process model is trained on the offline database with deep meta-learning with controlled deployment gaps, which repeatedly splits the training set into mean-training and kernel-training and learns kernel parameters to minimize the residuals from the mean models. In deployment, the decision-maker uses the trained model and adapts it to the data acquired online.
One of the challenges for this research is the lack of knowledge about ocean worlds like Europa.

“Before we sent the recent rovers to Mars, orbiters gave us pretty good information about the terrain features,” Thangeda said. “But the best image we have of Europa has a resolution of 256 to 340 meters per pixel, which is not clear enough to ascertain features.”
Thangeda’s adviser Melkior Ornik said, “All we know is that Europa’s surface is ice, but it could be big blocks of ice or much finer like snow. We also don’t know what’s underneath the ice.”
For some trials, the team hid material under a layer of something else. The robot only sees the top material and thinks it might be good to scoop. “When it actually scoops and hits the bottom layer, it learns it is unscoopable and moves to a different area,” Thangeda said.
NASA wants to send battery-powered rovers rather than nuclear to Europa because, among other mission-specific considerations, it is critical to minimize the risk of contaminating ocean worlds with potentially hazardous materials.
“Although nuclear power supplies have a lifespan of months, batteries have about a 20-day lifespan. We can’t afford to waste a few hours a day to send messages back and forth. This provides another reason why the robot’s autonomy to make decisions on its own is vital,” Thangeda said.

This method of learning to learn is also unique because it allows the robot to use vision and very little on-line experience to achieve high-quality scooping actions on unfamiliar terrains — significantly outperforming non-adaptive methods and other state-of-the-art meta-learning methods.
From these 12 materials and terrains made of a unique composition of one or more materials, a database of 6,700 was created.
The team used a robot in the Department of Computer Science at Illinois. It is modeled after the arm of a lander with sensors to collect scooping data on a variety of materials, from 1-millimeter grains of sand to 8-centimeter rocks, as well as different volume materials such as shredded cardboard and packing peanuts. The resulting database in the simulation contains 100 points of knowledge for each of 67 different terrains, or 6,700 total points.
“To our knowledge, we are the first to open source a large-scale dataset on granular media,” Thangeda said. “We also provided code to easily access the dataset so others can start using it in their applications.”
The model the team created will be deployed at NASA’s Jet Propulsion Laboratory’s Ocean World Lander Autonomy Testbed.
“We’re interested in developing autonomous robotic capabilities on extraterrestrial surfaces, and in particular challenging extraterrestrial surfaces,” Ornik said. “This unique method will help inform NASA’s continuing interest in exploring ocean worlds.
“The value of this work is in adaptability and transferability of knowledge or methods from Earth to an extraterrestrial body, because it is clear that we will not have a lot of information before the lander gets there. And because of the short battery lifespan, we won’t have a long time for the learning process. The lander might last for just a few days, then die, so learning and making decisions autonomously is extremely beneficial.”
The open-source dataset is available at: drillaway.github.io/scooping-dataset.html. More

An Aston University researcher has turned one of the basic rules of construction on its head.
For centuries a hanging chain has been used as an example to explain how masonry arches stand.
Structural engineers are familiar with seventeenth-century scientist Robert Hooke’s theory that a hanging chain will mirror the shape of an upstanding rigid arch.
However, research from Aston University’s College of Engineering and Physical Sciences, shows that this common-held belief is incorrect because, regardless of the similarities, the hanging chain and the arch are two incompatible mechanical systems.
Dr Haris Alexakis used the transition in science from Newtonian to Lagrangian mechanics, that led to the development of modern physics and mathematics, to prove this with mathematical rigour.
In his paper Vector analysis and the stationary potential energy for assessing equilibrium
of curved masonry structures he revisits the equilibrium of the hanging chain and the arch, explaining that the two systems operate in different spatial frameworks. One consequence of this is that the hanging chain requires only translational force to be in equilibrium whereas the inverted arch needs both translational and rotational. As a result, the solutions are always different.

Dr Alexakis’s analysis unearthed subtle inconsistencies in the way Hooke’s analogy has been interpreted and applied over the centuries for the design and safety assessment of arches, and highlights its practical limitations.
He said: “The analogy between inverted hanging chains and the optimal shape of masonry arches is a concept deeply rooted in our structural analysis practices.
“Curved structures have enabled masons, engineers, and architects to carry heavy loads and cover large spans with the use of low-tensile strength materials for centuries, while creating the marvels of the world’s architectural heritage.
“Despite the long history of these practices, finding optimal structural forms and assessing the stability and safety of curved structures remains as topical as ever. This is due to an increasing interest to preserve heritage structures and reduce material use in construction, while replacing steel and concrete with low-carbon natural materials.”
His paper, which is published in the journal Mathematics and Mechanics of Solids, suggests a new structural analysis method based on the principle of stationary potential energy which would be faster, more flexible and help calculate more complex geometries.
As a result, analysts won’t need to consider equilibrium of each individual block or describe geometrically the load path of thrust forces to obtain a rigorous solution.
Dr Alexakis added: “The analysis tools discussed in the paper will enable us to assess the condition and safety of heritage structures and build more sustainable curved structures, like vaults and shells.
“The major advantage of these structures, apart from having appealing aesthetics, is that they can have reduced volume, and can be made of economic, low-tensile-strength and low-carbon natural materials, contributing to net zero construction.” More