Aurelia Butler

Before a robot can grab dishes off a shelf to set the table, it must ensure its gripper and arm won’t crash into anything and potentially shatter the fine china. As part of its motion planning process, a robot typically runs “safety check” algorithms that verify its trajectory is collision-free.
However, sometimes these algorithms generate false positives, claiming a trajectory is safe when the robot would actually collide with something. Other methods that can avoid false positives are typically too slow for robots in the real world.
Now, MIT researchers have developed a safety check technique which can prove with 100 percent accuracy that a robot’s trajectory will remain collision-free (assuming the model of the robot and environment is itself accurate). Their method, which is so precise it can discriminate between trajectories that differ by only millimeters, provides proof in only a few seconds.
But a user doesn’t need to take the researchers’ word for it — the mathematical proof generated by this technique can be checked quickly with relatively simple math.
The researchers accomplished this using a special algorithmic technique, called sum-of-squares programming, and adapted it to effectively solve the safety check problem. Using sum-of-squares programming enables their method to generalize to a wide range of complex motions.
This technique could be especially useful for robots that must move rapidly avoid collisions in spaces crowded with objects, such as food preparation robots in a commercial kitchen. It is also well-suited for situations where robot collisions could cause injuries, like home health robots that care for frail patients.
“With this work, we have shown that you can solve some challenging problems with conceptually simple tools. Sum-of-squares programming is a powerful algorithmic idea, and while it doesn’t solve every problem, if you are careful in how you apply it, you can solve some pretty nontrivial problems,” says Alexandre Amice, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this technique.

Amice is joined on the paper fellow EECS graduate student Peter Werner and senior author Russ Tedrake, the Toyota Professor of EECS, Aeronautics and Astronautics, and Mechanical Engineering, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL). The work will be presented at the International Conference on Robots and Automation.
Certifying safety
Many existing methods that check whether a robot’s planned motion is collision-free do so by simulating the trajectory and checking every few seconds to see whether the robot hits anything. But these static safety checks can’t tell if the robot will collide with something in the intermediate seconds.
This might not be a problem for a robot wandering around an open space with few obstacles, but for robots performing intricate tasks in small spaces, a few seconds of motion can make an enormous difference.
Conceptually, one way to prove that a robot is not headed for a collision would be to hold up a piece of paper that separates the robot from any obstacles in the environment. Mathematically, this piece of paper is called a hyperplane. Many safety check algorithms work by generating this hyperplane at a single point in time. However, each time the robot moves, a new hyperplane needs to be recomputed to perform the safety check.
Instead, this new technique generates a hyperplane function that moves with the robot, so it can prove that an entire trajectory is collision-free rather than working one hyperplane at a time.

The researchers used sum-of-squares programming, an algorithmic toolbox that can effectively turn a static problem into a function. This function is an equation that describes where the hyperplane needs to be at each point in the planned trajectory so it remains collision-free.
Sum-of-squares can generalize the optimization program to find a family of collision-free hyperplanes. Often, sum-of-squares is considered a heavy optimization that is only suitable for offline use, but the researchers have shown that for this problem it is extremely efficient and accurate.
“The key here was figuring out how to apply sum-of-squares to our particular problem. The biggest challenge was coming up with the initial formulation. If I don’t want my robot to run into anything, what does that mean mathematically, and can the computer give me an answer?” Amice says.
In the end, like the name suggests, sum-of-squares produces a function that is the sum of several squared values. The function is always positive, since the square of any number is always a positive value.
Trust but verify
By double-checking that the hyperplane function contains squared values, a human can easily verify that the function is positive, which means the trajectory is collision-free, Amice explains.
While the method certifies with perfect accuracy, this assumes the user has an accurate model of the robot and environment; the mathematical certifier is only as good as the model.
“One really nice thing about this approach is that the proofs are really easy to interpret, so you don’t have to trust me that I coded it right because you can check it yourself,” he adds.
They tested their technique in simulation by certifying that complex motion plans for robots with one and two arms were collision-free. At its slowest, their method took just a few hundred milliseconds to generate a proof, making it much faster than some alternate techniques.
While their approach is fast enough to be used as a final safety check in some real-world situations, it is still too slow to be implemented directly in a robot motion planning loop, where decisions need to be made in microseconds, Amice says.
The researchers plan to accelerate their process by ignoring situations that don’t require safety checks, like when the robot is far away from any objects it might collide with. They also want to experiment with specialized optimization solvers that could run faster.
This work was supported, in part, by Amazon and the U.S. Air Force Research Laboratory. More

Solving arithmetic problems, even simple subtractions, involves mental representations whose influence remains to be clarified. Visualizing these representations would enable us to better understand our reasoning and adapt our teaching methods. A team from the University of Geneva (UNIGE), in collaboration with CY Cergy Paris University (CYU) and University of Burgundy (uB), analyzed drawings made by children and adults when solving simple problems. The scientists found that, whatever the age of the participant, the most effective calculation strategies were associated with certain drawing typologies. These results, published in the journal Memory & Cognition, open up new perspectives for the teaching of mathematics.
Learning mathematics often involves small problems, linked to concrete everyday situations. For example, pupils have to add up quantities of flour to make a recipe or subtract sums of money to find out what’s left in their wallets after shopping. They are thus led to translate statements into algorithmic procedures to find the solution. This translation of words into solving strategies involves a stage of mental representation of mathematical information, such as numbers or the arithmetic operation to be performed, and non-mathematical information, such as the context of the problem.
The cardinal or ordinal dimensions of problems
Having a clearer idea of these mental representations would enable a better understanding of the choice of calculation strategies. Scientists from UNIGE, CYU and uB conducted a study with 10-year-old children and adults, asking them to solve simple problems with the instruction to use as few calculation steps as possible. The participants were then asked to produce a drawing or diagram explaining their problem-solving strategy for each statement. The contexts of some problems called on the cardinal properties of numbers — the quantity of elements in a set — others on their ordinal properties — their position in an ordered list.
The former involved marbles, fishes, or books, for example: ”Paul has 8 red marbles. He also has blue marbles. In total, Paul has 11 marbles. Jolene has as many blue marbles as Paul, and some green marbles. She has 2 green marbles less than Paul has red marbles. In total, how many marbles does Jolene have?”. The latter involved lengths or durations, for example: ”Sofia traveled for 8 hours. Her trip started during the day. Sofia arrived at 11. Fred leaves at the same time as Sofia. Fred’s trip lasted 2 hours less than Sofia’s. What time was it when Fred arrived?”
Both of the above problems share the same mathematical structure, and both can be solved by a long strategy in 3 steps: 11 — 8 = 3; 8 — 2 = 6; 6 + 3 = 9, but also in a single calculation: 11 — 2 = 9, using a simple subtraction. However, the mental representations of these problems are very different, and the researchers wanted to determine whether the type of representations could predict the calculation strategy, in 1 or 3 steps, of those who solve them.
”Our hypothesis was that cardinal problems — such as the one involving marbles — would inspire cardinal drawings, i.e. diagrams with identical individual elements, such as crosses or circles, or with overlaps of elements in sets or subsets. Similarly, we assumed that ordinal problems — such as the one mentioning travel times — would lead to ordinal representations, i.e. diagrams with axes, graduations or intervals — and that these ordinal drawings would reflect participants’ representations and indicate that they would be more successful in identifying the one-step solution strategy,” explains Hippolyte Gros, former post-doctoral fellow at UNIGE’s Faculty of Psychology and Educational Sciences, associate professor at CYU, and first author of the study.

Identifying mental representations through drawings
These hypotheses were validated by analyzing the drawings of 52 adults and 59 children. ”We have shown that, irrespective of their experience — since the same results were obtained in both children and adults — the use of strategies by the participants depends on their representation of the problem, and that this is influenced by the non-mathematical information contained in the problem statement, as revealed by their drawings,” says Emmanuel Sander, full professor at the UNIGE’s Faculty of Psychology and Educational Sciences. ”Our study also shows that, even after years of experience in solving addition and subtraction, the difference between cardinal and ordinal problems remains very marked. The majority of participants were only able to solve problems of the second type in a single step”.
Improving mathematical learning through drawing analysis
The team also noted that drawings showing ordinal representations were more frequently associated with a one-step solution, even if the problem was cardinal. In other words, drawing with a scale or an axis is linked to the choice of the fastest calculation. “From a pedagogical point of view, this suggests that the presence of specific features in a student’s drawing may or may not indicate that his or her representation of the problem is the most efficient one for meeting the instructions — in this case, solving with the fewest calculations possible,” observes Jean-Pierre Thibaut, full professor at the uB Laboratory for Research on Learning and Development.
”Thus, when it comes to subtracting individual elements, a representation via an axis — rather than via subsets — is more effective in finding the fastest method. Analysis of students’ drawings in arithmetic can therefore enable targeted intervention to help them translate problems into more optimal representations. One way of doing this is to work on the graphical representation of statements in class, to help students understand the most direct strategies,” concludes Hippolyte Gros. More

Two physicists at the University of Konstanz are developing a method that could enable the stable exchange of information in quantum computers. In the leading role: photons that make quantum bits “fly.”
Quantum computers are considered the next big evolutionary step in information technology. They are expected to solve computing problems that today’s computers simply cannot solve — or would take ages to do so. Research groups around the world are working on making the quantum computer a reality. This is anything but easy, because the basic components of such a computer, the quantum bits or qubits, are extremely fragile. One type of qubits consists of the intrinsic angular momentum (spin) of a single electron, i.e. they are at the scale of an atom. It is hard enough to keep such a fragile system intact. It is even more difficult to interconnect two or more of these qubits. So how can a stable exchange of information between qubits be achieved?
Flying qubits
The two Konstanz physicists Benedikt Tissot and Guido Burkard have now developed a theoretical model of how the information exchange between qubits could succeed by using photons as a “means of transport” for quantum information. The general idea: The information content (electron spin state) of the material qubit is converted into a “flying qubit,” namely a photon. Photons are “light quanta” that constitute the basic building blocks making up the electromagnetic radiation field. The special feature of the new model: stimulated Raman emissions are used for converting the qubit into a photon. This procedure allows more control over the photons. “We are proposing a paradigm shift from optimizing the control during the generation of the photon to directly optimizing the temporal shape of the light pulse in the flying qubit,” explains Guido Burkard.
Benedikt Tissot compares the basic procedure with the Internet: “In a classic computer, we have our bits, which are encoded on a chip in the form of electrons. If we want to send information over long distances, the information content of the bits is converted into a light signal that is transmitted through optical fibers.” The principle of information exchange between qubits in a quantum computer is very similar: “Here, too, we have to convert the information into states that can be easily transmitted — and photons are ideal for this,” explains Tissot.
A three-level system for controlling the photon
“We need to consider several aspects,” says Tissot: “We want to control the direction in which the information flows — as well as when, how quickly and where it flows to. That’s why we need a system that allows for a high level of control.” The researchers’ method makes this control possible by means of resonator-enhanced, stimulated Raman emissions. Behind this term is a three-level system, which leads to a multi-stage procedure. These stages offer the physicists control over the photon that is created. “We have ‘more buttons’ here that we can operate to control the photon,” Tissot illustrates.
Stimulated Raman emission are an established method in physics. However, using them to send qubit states directly is unusual. The new method might make it possible to balance the consequences of environmental perturbations and unwanted side effects of rapid changes in the temporal shape of the light pulse, so that information transport can be implemented more accurately. The detailed procedure was published in the journal Physical Review Research in February 2024. More

Cornell University researchers have developed a semiconductor chip that will enable ever-smaller devices to operate at the higher frequencies needed for future 6G communication technology.
The next generation of wireless communication not only requires greater bandwidth at higher frequencies — it also needs a little extra time. The new chip adds a necessary time delay so signals sent across multiple arrays can align at a single point in space — without disintegrating.
The team’s paper, “Ultra-Compact Quasi-True-Time-Delay for Boosting Wireless Channel-Capacity,” published March 6 in Nature. The lead author is Bal Govind, a doctoral student in electrical and computer engineering.
The majority of current wireless communications, such as 5G phones, operate at frequencies below 6 gigahertz (GHz). Technology companies have been aiming to develop a new wave of 6G cellular communications that use frequencies above 20 GHz, where there is more available bandwidth, which means more data can flow and at a faster rate. 6G is expected to be 100 times faster than 5G.
However, since data loss through the environment is greater at higher frequencies, one crucial factor is how the data is relayed. Instead of relying on a single transmitter and a single receiver, most 5G and 6G technologies use a more energy-efficient method: a series of phased arrays of transmitters and receivers.
“Every frequency in the communication band goes through different time delays,” Govind said. “The problem we’re addressing is decades old — that of transmitting high-bandwidth data in an economical manner so signals of all frequencies line up at the right place and time.”
“It’s not just building something with enough delay, it’s building something with enough delay where you still have a signal at the end,” said senior author Alyssa Apsel,professor of engineering. “The trick is that we were able to do it without enormous loss.”
Govind worked with postdoctoral researcher and co-author Thomas Tapen to design a complementary metal-oxide-semiconductor (CMOS) that could tune a time delay over an ultra-broad bandwidth of 14 GHz, with as high as 1 degree of phase resolution

“Since the aim of our design was to pack as many of these delay elements as possible,” Govind said, “we imagined what it would be like to wind the path of the signal in three-dimensional waveguides and bounce signals off of them to cause delay, instead of laterally spreading wavelength-long wires across the chip.”
The team engineered a series of these 3D reflectors strung together to form a “tunable transmission line.”
The resulting integrated circuit occupies a 0.13-square-millimeter footprint that is smaller than phase shifters yet nearly doubles the channel-capacity — i.e., data rate — of conventional wireless arrays. And by boosting the projected data rate, the chip could provide faster service, getting more data to cellphone users.
“The big problem with phased arrays is this tradeoff between trying to make these things small enough to put on a chip and maintain efficiency,” Apsel said. “The answer that most of the industry has landed on is, ‘Well, we can’t do time delay, so we’re going to do phase delay.’ And that fundamentally limits how much information you can transmit and receive. They just sort of take that hit.
“I think one of our major innovations is really the question: Do you need to build it this way?” Apsel said. “If we can boost the channel capacity by a factor of 10 by changing one component, that is a pretty interesting game-changer for communications.” More

The National Institute of Standards and Technology (NIST) and its collaborators have delivered a small but mighty advancement in timing technology: compact chips that seamlessly convert light into microwaves. This chip could improve GPS, the quality of phone and internet connections, the accuracy of radar and sensing systems, and other technologies that rely on high-precision timing and communication.
This technology reduces something known as timing jitter, which is small, random changes in the timing of microwave signals. Similar to when a musician is trying to keep a steady beat in music, the timing of these signals can sometimes waver a bit. The researchers have reduced these timing wavers to a very small fraction of a second — 15 femtoseconds to be exact, a big improvement over traditional microwave sources — making the signals much more stable and precise in ways that could increase radar sensitivity, the accuracy of analog-to-digital converters and the clarity of astronomical images captured by groups of telescopes.
The team’s results were published in Nature.
Shining a Light on Microwaves
What sets this demonstration apart is the compact design of the components that produce these signals. For the first time, researchers have taken what was once a tabletop-size system and shrunken much of it into a compact chip, about the same size as a digital camera memory card. Reducing timing jitter on a small scale reduces power usage and makes it more usable in everyday devices.
Right now, several of the components for this technology are located outside of the chip, as researchers test their effectiveness. The ultimate goal of this project is to integrate all the different parts, such as lasers, modulators, detectors and optical amplifiers, onto a single chip.
By integrating all the components onto a single chip, the team could reduce both the size and power consumption of the system. This means it could be easily incorporated into small devices without requiring lots of energy and specialized training.

“The current technology takes several labs and many Ph.D.s to make microwave signals happen,” said Frank Quinlan, NIST physical scientist. “A lot of what this research is about is how we utilize the advantages of optical signals by shrinking the size of components and making everything more accessible.”
To accomplish this, researchers use a semiconductor laser, which acts as a very steady flashlight. They direct the light from the laser into a tiny mirror box called a reference cavity, which is like a miniature room where light bounces around. Inside this cavity, some light frequencies are matched to the size of the cavity so that the peaks and valleys of the light waves fit perfectly between the walls. This causes the light to build up power in those frequencies, which is used to keep the laser’s frequency stable. The stable light is then converted into microwaves using a device called a frequency comb, which changes high-frequency light into lower-pitched microwave signals. These precise microwaves are crucial for technologies like navigation systems, communication networks and radar because they provide accurate timing and synchronization.
“The goal is to make all these parts work together effectively on a single platform, which would greatly reduce the loss of signals and remove the need for extra technology,” said Quinlan. “Phase one of this project was to show that all these individual pieces work together. Phase two is putting them together on the chip.”
In navigation systems such as GPS, the precise timing of signals is essential for determining location. In communication networks, such as mobile phone and internet systems, accurate timing and synchronization of multiple signals ensure that data is transmitted and received correctly.
For example, synchronizing signals is important for busy cell networks to handle multiple phone calls. This precise alignment of signals in time enables the cell network to organize and manage the transmission and reception of data from multiple devices, like your cellphone. This ensures that multiple phone calls can be carried over the network simultaneously without experiencing significant delays or drops.
In radar, which is used for detecting objects like airplanes and weather patterns, precise timing is crucial for accurately measuring how long it takes for signals to bounce back.

“There are all sorts of applications for this technology. For instance, astronomers who are imaging distant astronomical objects, like black holes, need really low-noise signals and clock synchronization,” said Quinlan. “And this project helps get those low noise signals out of the lab, and into the hands of radar technicians, of astronomers, of environmental scientists, of all these different fields, to increase their sensitivity and ability to measure new things.”
Working Together Toward a Shared Goal
Creating this type of technological advancement is not done alone. Researchers from the University of Colorado Boulder, the NASA Jet Propulsion Laboratory, California Institute of Technology, the University of California Santa Barbara, the University of Virginia, and Yale University came together to accomplish this shared goal: to revolutionize how we harness light and microwaves for practical applications.
“I like to compare our research to a construction project. There’s a lot of moving parts, and you need to make sure everyone is coordinated so the plumber and electrician are showing up at the right time in the project,” said Quinlan. “We all work together really well to keep things moving forward.”
This collaborative effort underscores the importance of interdisciplinary research in driving technological progress, Quinlan said. More

Artificial intelligence helped clinicians to accelerate the design of diabetes prevention software, a new study finds.
Publishing online March 6 in the Journal of Medical Internet Research, the study examined the capabilities of a form of artificial intelligence (AI) called generative AI or GenAI, which predicts likely options for the next word in any sentence based on how billions of people used words in context on the internet. A side effect of this next-word prediction is that the generative AI “chatbots” like chatGPT can generate replies to questions in realistic language, and produce clear summaries of complex texts.
Led by researchers at NYU Langone Health, the current paper explores the application of ChatGPT to the design of a software program that uses text messages to counter diabetes by encouraging patients to eat healthier and get exercise. The team tested whether AI-enabled interchanges between doctors and software engineers could hasten the development of such a personalized automatic messaging system (PAMS).
In the current study, eleven evaluators in fields ranging from medicine to computer science successfully used ChatGPT to produce a version of the diabetes tool over 40 hours, where an original, non-AI-enabled effort had required more than 200 programmer hours.
“We found that ChatGPT improves communications between technical and non-technical team members to hasten the design of computational solutions to medical problems,” says study corresponding author Danissa Rodriguez, PhD, assistant professor in the Department of Population Health at NYU Langone, and member of its Healthcare Innovation Bridging Research, Informatics and Design (HiBRID) Lab. “The chatbot drove rapid progress throughout the software development life cycle, from capturing original ideas, to deciding which features to include, to generating the computer code. If this proves to be effective at scale it could revolutionize healthcare software design.”
AI as Translator
Generative AI tools are sensitive, say the study authors, and asking a question of the tool in two subtly different ways may yield divergent answers. The skill required to frame the questions asked of chatbots in a way that elicits the desired response, called prompt engineering, combines intuition and experimentation. Physicians and nurses, with their understanding of nuanced medical contexts, are well positioned to engineer strategic prompts that improve communications with engineers, and without learning to write computer code.
These design efforts, however, where care providers, the would-be users of a new software, seek to advise engineers about what it must include can be compromised by attempts to converse using “different” technical languages. In the current study, the clinical members of the team were able to type their ideas in plain English, enter them into chatGPT, and ask the tool to convert their input into the kind of language required to guide coding work by the team’s software engineers. AI could take software design only so far before human software developers were needed for final code generation, but the overall process was greatly accelerated, say the authors.
“Our study found that chatGPT can democratize the design of healthcare software by enabling doctors and nurses to drive its creation,” says senior study author Devin Mann, MD, director of the HiBRID Lab, and strategic director of Digital Health Innovation within NYU Langone Medical Center Information Technology (MCIT).”GenAI-assisted development promises to deliver computational tools that are usable, reliable, and in-line with the highest coding standards.”
Along with Rodriguez and Mann, study authors from the Department of Population Health at NYU Langone were Katharine Lawrence, MD, Beatrix Brandfield-Harvey, Lynn Xu, Sumaiya Tasneem, and Defne Levine. Javier Gonzalez,technical lead in the HIBRID Lab, was also a study author. This work was supported by the National Institute of Diabetes and Digestive and Kidney Diseases grant 1R18DK118545-01A1. More

If you recently had trouble figuring out if an image of a person is real or generated through artificial intelligence (AI), you’re not alone.
A new study from University of Waterloo researchers found that people had more difficulty than was expected distinguishing who is a real person and who is artificially generated.
The Waterloo study saw 260 participants provided with 20 unlabeled pictures: 10 of which were of real people obtained from Google searches, and the other 10 generated by Stable Diffusion or DALL-E, two commonly used AI programs that generate images.
Participants were asked to label each image as real or AI-generated and explain why they made their decision. Only 61 per cent of participants could tell the difference between AI-generated people and real ones, far below the 85 per cent threshold that researchers expected.
“People are not as adept at making the distinction as they think they are,” said Andreea Pocol, a PhD candidate in Computer Science at the University of Waterloo and the study’s lead author.
Participants paid attention to details such as fingers, teeth, and eyes as possible indicators when looking for AI-generated content — but their assessments weren’t always correct.
Pocol noted that the nature of the study allowed participants to scrutinize photos at length, whereas most internet users look at images in passing.

“People who are just doomscrolling or don’t have time won’t pick up on these cues,” Pocol said.
Pocol added that the extremely rapid rate at which AI technology is developing makes it particularly difficult to understand the potential for malicious or nefarious action posed by AI-generated images. The pace of academic research and legislation isn’t often able to keep up: AI-generated images have become even more realistic since the study began in late 2022.
These AI-generated images are particularly threatening as a political and cultural tool, which could see any user create fake images of public figures in embarrassing or compromising situations.
“Disinformation isn’t new, but the tools of disinformation have been constantly shifting and evolving,” Pocol said. “It may get to a point where people, no matter how trained they will be, will still struggle to differentiate real images from fakes. That’s why we need to develop tools to identify and counter this. It’s like a new AI arms race.”
The study, “Seeing Is No Longer Believing: A Survey on the State of Deepfakes, AI-Generated Humans, and Other Nonveridical Media,” appears in the journal Advances in Computer Graphics. More