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In the field of robotics, researchers are continually looking for the fastest, strongest, most efficient and lowest-cost ways to actuate, or enable, robots to make the movements needed to carry out their intended functions.
The quest for new and better actuation technologies and ‘soft’ robotics is often based on principles of biomimetics, in which machine components are designed to mimic the movement of human muscles — and ideally, to outperform them. Despite the performance of actuators like electric motors and hydraulic pistons, their rigid form limits how they can be deployed. As robots transition to more biological forms and as people ask for more biomimetic prostheses, actuators need to evolve.
Associate professor (and alum) Michael Shafer and professor Heidi Feigenbaum of Northern Arizona University’s Department of Mechanical Engineering, along with graduate student researcher Diego Higueras-Ruiz, published a paper in Science Robotics presenting a new, high-performance artificial muscle technology they developed in NAU’s Dynamic Active Systems Laboratory. The paper, titled “Cavatappi artificial muscles from drawing, twisting, and coiling polymer tubes,” details how the new technology enables more human-like motion due to its flexibility and adaptability, but outperforms human skeletal muscle in several metrics.
“We call these new linear actuators cavatappi artificial muscles based on their resemblance to the Italian pasta,” Shafer said.
Because of their coiled, or helical, structure, the actuators can generate more power, making them an ideal technology for bioengineering and robotics applications. In the team’s initial work, they demonstrated that cavatappi artificial muscles exhibit specific work and power metrics ten and five times higher than human skeletal muscles, respectively, and as they continue development, they expect to produce even higher levels of performance.
“The cavatappi artificial muscles are based on twisted polymer actuators (TPAs), which were pretty revolutionary when they first came out because they were powerful, lightweight and cheap. But they were very inefficient and slow to actuate because you had to heat and cool them. Additionally, their efficiency is only about two percent,” Shafer said. “For the cavatappi, we get around this by using pressurized fluid to actuate, so we think these devices are far more likely to be adopted. These devices respond about as fast as we can pump the fluid. The big advantage is their efficiency. We have demonstrated contractile efficiency of up to about 45 percent, which is a very high number in the field of soft actuation.”
The engineers think this technology could be used in soft robotics applications, conventional robotic actuators (for example, for walking robots), or even potentially in assistive technologies like exoskeletons or prostheses.
“We expect that future work will include the use of cavatappi artificial muscles in many applications due to their simplicity, low-cost, lightweight, flexibility, efficiency and strain energy recovery properties, among other benefits,” Shafer said.
Technology is available for licensing, partnering opportunities.
Working with the NAU Innovations team, the inventors have taken steps to protect their intellectual property. The technology has entered the protection and early commercialization stage and is available for licensing and partnering opportunities. For more information, please contact NAU Innovations.
Shafer joined NAU in 2013. His other research interests are related to energy harvesting, wildlife telemetry systems and unmanned aerial systems. Feigenbaum joined NAU in 2007, and her other research interest include ratcheting in metals and smart materials. The graduate student on this project, Diego Higueras-Ruiz, received his MS in Mechanical Engineering from NAU in 2018 and will be completing his PhD in Bioengineering in Fall 2021. This work has been supported through a grant from NAU’s Research and Development Preliminary Studies program. More

In a new series of experiments, artificial intelligence (A.I.) algorithms were able to influence people’s preferences for fictitious political candidates or potential romantic partners, depending on whether recommendations were explicit or covert. Ujué Agudo and Helena Matute of Universidad de Deusto in Bilbao, Spain, present these findings in the open-access journal PLOS ONE on April 21, 2021.
From Facebook to Google search results, many people encounter A.I. algorithms every day. Private companies are conducting extensive research on the data of their users, generating insights into human behavior that are not publicly available. Academic social science research lags behind private research, and public knowledge on how A.I. algorithms might shape people’s decisions is lacking.
To shed new light, Agudo and Matute conducted a series of experiments that tested the influence of A.I. algorithms in different contexts. They recruited participants to interact with algorithms that presented photos of fictitious political candidates or online dating candidates, and asked the participants to indicate whom they would vote for or message. The algorithms promoted some candidates over others, either explicitly (e.g., “90% compatibility”) or covertly, such as by showing their photos more often than others’.
Overall, the experiments showed that the algorithms had a significant influence on participants’ decisions of whom to vote for or message. For political decisions, explicit manipulation significantly influenced decisions, while covert manipulation was not effective. The opposite effect was seen for dating decisions.
The researchers speculate these results might reflect people’s preference for human explicit advice when it comes to subjective matters such as dating, while people might prefer algorithmic advice on rational political decisions.
In light of their findings, the authors express support for initiatives that seek to boost the trustworthiness of A.I., such as the European Commission’s Ethics Guidelines for Trustworthy AI and DARPA’s explainable AI (XAI) program. Still, they caution that more publicly available research is needed to understand human vulnerability to algorithms.
Meanwhile, the researchers call for efforts to educate the public on the risks of blind trust in recommendations from algorithms. They also highlight the need for discussions around ownership of the data that drives these algorithms.
The authors add: “If a fictitious and simplistic algorithm like ours can achieve such a level of persuasion without establishing actually customized profiles of the participants (and using the same photographs in all cases), a more sophisticated algorithm such as those with which people interact in their daily lives should certainly be able to exert a much stronger influence.”
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Ever wondered why your virtual home assistant doesn’t understand your questions? Or why your navigation app took you on the side street instead of the highway? In a study published April 21st in the journal iScience, Italian researchers designed a robot that “thinks out loud” so that users can hear its thought process and better understand the robot’s motivations and decisions.
“If you were able to hear what the robots are thinking, then the robot might be more trustworthy,” says co-author Antonio Chella, describing first author Arianna Pipitone’s idea that launched the study at the University of Palermo. “The robots will be easier to understand for laypeople, and you don’t need to be a technician or engineer. In a sense, we can communicate and collaborate with the robot better.”
Inner speech is common in people and can be used to gain clarity, seek moral guidance, and evaluate situations in order to make better decisions. To explore how inner speech might impact a robot’s actions, the researchers built a robot called Pepper that speaks to itself. They then asked people to set the dinner table with Pepper according to etiquette rules to study how Pepper’s self-dialogue skills influence human-robot interactions.
The scientists found that, with the help of inner speech, Pepper is better at solving dilemmas. In one experiment, the user asked Pepper to place the napkin at the wrong spot, contradicting the etiquette rule. Pepper started asking itself a series of self-directed questions and concluded that the user might be confused. To be sure, Pepper confirmed the user’s request, which led to further inner speech.
“Ehm, this situation upsets me. I would never break the rules, but I can’t upset him, so I’m doing what he wants,” Pepper said to itself, placing the napkin at the requested spot. Through Pepper’s inner voice, the user can trace its thoughts to learn that Pepper was facing a dilemma and solved it by prioritizing the human’s request. The researchers suggest that the transparency could help establish human-robot trust.
Comparing Pepper’s performance with and without inner speech, Pipitone and Chella discovered that the robot had a higher task-completion rate when engaging in self-dialogue. Thanks to inner speech, Pepper outperformed the international standard functional and moral requirements for collaborative robots — guidelines that machines, from humanoid AI to mechanic arms at the manufacturing line, follow.
“People were very surprised by the robot’s ability,” says Pipitone. “The approach makes the robot different from typical machines because it has the ability to reason, to think. Inner speech enables alternative solutions for the robots and humans to collaborate and get out of stalemate situations.”
Although hearing the inner voice of robots enriches the human-robot interaction, some people might find it inefficient because the robot spends more time completing tasks when it talks to itself. The robot’s inner speech is also limited to the knowledge that researchers gave it. Still, Pipitone and Chella say their work provides a framework to further explore how self-dialogue can help robots focus, plan, and learn.
“In some sense, we are creating a generational robot that likes to chat,” says Chella. The authors say that, from navigation apps and the camera on your phone to medical robots in the operation rooms, machines and computers alike can benefit from this chatty feature. “Inner speech could be useful in all the cases where we trust the computer or a robot for the evaluation of a situation,” Chella says.
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Augmented reality (AR) is a technology that superimposes virtual objects onto a live view of physical environments, helping users visualize how these objects fit into their physical world. Researchers from City University of Hong Kong and Singapore Management University published a new paper in the Journal of Marketing that identifies four broad uses of AR in retail settings and examines the impact of AR on retail sales.
The study, forthcoming in the Journal of Marketing, is titled “Augmented Reality in Retail and Its Impact on Sales” and is authored by Yong-Chin Tan, Sandeep Chandukala, and Srinivas Reddy. The researchers discuss the following uses of AR in retail settings:* To entertain customers. AR transforms static objects into interactive, animated three-dimensional objects, helping marketers create fresh experiences that captivate and entertain customers. Marketers can use AR-enabled experiences to drive traffic to their physical locations. For example, Walmart collaborated with DC Comics and Marvel to place special thematic displays with exclusive superhero-themed AR experiences in its stores. In addition to creating novel and engaging experiences for customers, the displays also encouraged customers to explore different areas in the stores. * To educate customers. Due to its interactive and immersive format, AR is also an effective medium to deliver content and information to customers. To help customers better appreciate their new car models, Toyota and Hyundai have utilized AR to demonstrate key features and innovative technologies in a vivid and visually appealing manner. AR can also be used to provide in-store wayfinding and product support. Walgreens and Lowe’s have developed in-store navigation apps that overlay directional signals onto a live view of the path in front of users to guide them to product locations and notify them if there are special promotions along the way. * To facilitate product evaluation. By retaining the physical environment as a backdrop for virtual elements, AR also helps users visualize how products would appear in their actual consumption contexts to assess product fit more accurately prior to purchase. For example, Ikea’s Place app uses AR to overlay true-to-scale, three-dimensional models of furniture onto a live view of customers’ rooms. Customers can easily determine if the products fit in a space without taking any measurements. Uniqlo and Topshop have also deployed the same technology in their physical stores, offering customers greater convenience by reducing the need to change in and out of different outfits. An added advantage of AR is its ability to accommodate a wide assortment of products. This capability is particularly useful for made-to-order or bulky products. BMW and Audi have used AR to provide customers with true-to-scale, three-dimensional visual representations of car models based on customized features such as paint color, wheel design, and interior aesthetics. * To enhance the post-purchase consumption experience. Lastly, AR can be used to enhance and redefine the way products are experienced or consumed after they have been purchased. For example, Lego recently launched several specially designed brick sets that combine physical and virtual gameplay. Through the companion AR app, animated Lego characters spring to life and interact with the physical Lego sets, creating a whole new playing experience. In a bid to address skepticism about the quality of its food ingredients, McDonald’s has also used AR to let customers discover the origins of ingredients in the food they purchased via story-telling and three-dimensional animations. The research also focuses on the promising application of AR to facilitate product evaluation prior to purchase and examine how it impacts sales in online retail. For example: * The availability and usage of AR has a positive impact on sales. The overall impact appears to be small, but certain products are more likely to benefit from the technology than others. * The impact of AR is stronger for products and brands that are less popular. Thus, retailers carrying wide product assortments can use AR to stimulate demand for niche products at the long tail of the sales distribution. AR may also help to level the playing field for less-popular brands. With the launch of AR-enabled display ads on advertising platforms such as Facebook and YouTube, less-established brands could consider investing in this new ad format because they stand to benefit most from this technology. * The impact of AR is also greater for products that are more expensive, indicating that AR could increase overall revenues for retailers. Retailers selling premium products may also leverage AR to improve decision comfort and reduce customers’ hesitation in the purchase process. * Customers who are new to the online channel or product category are more likely to purchase after using AR, suggesting that AR has the potential to promote online channel adoption and category expansion. As prior research has shown that multichannel customers are more profitable, omni-channel retailers can use AR to encourage their offline customers to adopt the online channel.Taken together, these findings provide converging evidence that AR is most effective when product-related uncertainty is high. Managers can thus use AR to reduce customer uncertainty and improve sales.
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Researchers at Linköping University, Sweden, have developed a stable high-conductivity polymer ink. The advance paves the way for innovative printed electronics with high energy efficiency. The results have been published in Nature Communications.
Electrically conducting polymers have made possible the development of flexible and lightweight electronic components such as organic biosensors, solar cells, light-emitting diodes, transistors, and batteries.
The electrical properties of the conducting polymers can be tuned using a method known as “doping.” In this method, various dopant molecules are added to the polymer to change its properties. Depending on the dopant, the doped polymer can conduct electricity by the motion of either negatively charged electrons (an “n-type” conductor), or positively charged holes (a “p-type” conductor). Today, the most commonly used conducting polymer is the p-type conductor PEDOT:PSS. PEDOT:PSS has several compelling features such as high electrical conductivity, excellent ambient stability, and most importantly, commercial availability as an aqueous dispersion. However, many electronic devices require a combination of p-types and n-types to function. At the moment, there is no n-type equivalent to PEDOT:PSS.
Researchers at Linköping University, together with colleagues in the US and South Korea, have now developed a conductive n-type polymer ink, stable in air and at high temperatures. This new polymer formulation is known as BBL:PEI.
“This is a major advance that makes the next generation of printed electronic devices possible. The lack of a suitable n-type polymer has been like walking on one leg when designing functional electronic devices. We can now provide the second leg,” says Simone Fabiano, senior lecturer in the Department of Science and Technology at Linköping University.
Chi-Yuan Yang is a postdoc at Linköping University and one of the principal authors of the article published in Nature Communications. He adds:
“Everything possible with PEDOT:PSS is also possible with our new polymer. The combination of PEDOT:PSS and BBL:PEI opens new possibilities for the development of stable and efficient electronic circuits,” says Chi-Yuan Yang.
The new n-type material comes in the form of ink with ethanol as the solvent. The ink can be deposited by simply spraying the solution onto a surface, making organic electronic devices easier and cheaper to manufacture. Also, the ink is more eco-friendly than many other n-type organic conductors currently under development, which instead contain harmful solvents. Simone Fabiano believes that the technology is ready for routine use.
“Large-scale production is already feasible, and we are thrilled to have come so far in a relatively short time. We expect BBL:PEI to have the same impact as PEDOT:PSS. At the same time, much remains to be done to adapt the ink to various technologies, and we need to learn more about the material,” says Simone Fabiano.
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A new mathematical model for the interaction of bacteria in the gut could help design new probiotics and specially tailored diets to prevent diseases. The research, from Chalmers University of Technology in Sweden, was recently published in the journal PNAS.
“Intestinal bacteria have an important role to play in health and the development of diseases, and our new mathematical model could be extremely helpful in these areas,” says Jens Nielsen, Professor of Systems Biology at Chalmers, who led the research.
The new paper describes how the mathematical model performed when making predictions relating to two earlier clinical studies, one involving Swedish infants, and the other adults in Finland with obesity.
The studies involved regular measurements of health indicators, which the researchers compared with the predictions made from their mathematical model — the model proved to be highly accurate in predicting multiple variables, including how a switch from liquid to solid food in the Swedish infants affected their intestinal bacterial composition.
They also measured how the obese adults’ intestinal bacteria changed after a move to a more restricted diet. Again, the model’s predictions proved to be reliably accurate.
“These are very encouraging results, which could enable computer-based design for a very complex system. Our model could therefore be used to for creating personalised healthy diets, with the possibility to predict how adding specific bacteria as novel probiotics could impact a patient’s health,” says Jens Nielsen. More

Engineered, autonomous machines combined with artificial intelligence have long been a staple of science fiction, and often in the role of villain like the Cylons in the “Battlestar Galactica” reboot, creatures composed of biological and engineered materials. But what if these autonomous soft machines were … helpful?
This is the vision of a team of Penn State and U.S. Air Force researchers, outlined in a recent paper in Nature Communications. These researchers produced a soft, mechanical metamaterial that can “think” about how forces are applied to it and respond via programmed reactions. This platform holds great potential for a variety of applications from medical treatments to improving the environment.
“We created soft, mechanical metamaterials with flexible, conductive polymer networks that can compute all digital logic computations,” said Ryan Harne, James F. Will Career Development Associate Professor, Penn State. “Our paper reports a way to create decision-making functionality in engineered materials in a way that could support future soft, autonomous engineered systems that are invested with the basic elements of lifeforms yet are programmed to perform helpful services for people. These could include helping maintain sustainable and robust infrastructure, monitoring of airborne and waterborne contaminants and pathogens, assisting with patient wound healing, and more.”
Human thought processes are based on logic, Harne notes, which is similar to Boolean logic from mathematics. This approach uses binary inputs to process binary control outputs — using only “on” and “off” sequences to represent all thought and cognition. The soft materials that the research team created “think” using the reconfiguration of the conductive polymer networks. Mechanical force, applied to the materials, connects, and reconnects the network.
Using a low voltage input into the materials, the research team created a way for the soft material to decide how to react according to the output voltage signal from the reconfigured conductive polymer network.
The type of logic that Harne and the team uses goes beyond pure mechanical logic, which is a way of using combinations of bistable switches — switches with two stable states — to represent the “0s” and “1s” of a binary number sequence. They found that when they used pure mechanical logic, the researchers ended up getting stuck because certain logical operations cannot be constructed. More

As artificial intelligence has attracted broad interest, researchers are focused on understanding how the brain accomplishes cognition so they can construct artificial systems with general intelligence comparable to humans’ intelligence.
Many have approached this challenge by using conventional silicon microelectronics in conjunction with light. However, the fabrication of silicon chips with electronic and photonic circuit elements is difficult for many physical and practical reasons related to the materials used for the components.
In Applied Physics Letters, by AIP Publishing, researchers at the National Institute of Standards and Technology propose an approach to large-scale artificial intelligence that focuses on integrating photonic components with superconducting electronics rather than semiconducting electronics.
“We argue that by operating at low temperature and using superconducting electronic circuits, single-photon detectors, and silicon light sources, we will open a path toward rich computational functionality and scalable fabrication,” said author Jeffrey Shainline.
Using light for communication in conjunction with complex electronic circuits for computation could enable artificial cognitive systems of scale and functionality beyond what can be achieved with either light or electronics alone.
“What surprised me most was that optoelectronic integration may be much easier when working at low temperatures and using superconductors than when working at room temperatures and using semiconductors,” said Shainline.
Superconducting photon detectors enable detection of a single photon, while semiconducting photon detectors require about 1,000 photons. So not only do silicon light sources work at 4 kelvins, but they also can be 1,000 times less bright than their room temperature counterparts and still communicate effectively.
Some applications, such as chips in cellphones, require working at room temperature, but the proposed technology would still have wide reaching applicability for advanced computing systems.
The researchers plan to explore more complex integration with other superconducting electronic circuits as well as demonstrate all the components that comprise artificial cognitive systems, including synapses and neurons.
Showing that the hardware can be manufactured in a scalable manner, so large systems can be realized at a reasonable cost, will also be important. Superconducting optoelectronic integration could also help create scalable quantum technologies based on superconducting or photonic qubits. Such quantum-neural hybrid systems may also lead to new ways of leveraging the strengths of quantum entanglement with spiking neurons.
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