Computers Math

When astronauts begin to build a permanent base on the moon, as NASA plans to do in the coming years, they’ll need help. Robots could potentially do the heavy lifting by laying cables, deploying solar panels, erecting communications towers, and building habitats. But if each robot is designed for a specific action or task, a moon base could become overrun by a zoo of machines, each with its own unique parts and protocols.
To avoid a bottleneck of bots, a team of MIT engineers is designing a kit of universal robotic parts that an astronaut could easily mix and match to rapidly configure different robot “species” to fit various missions on the moon. Once a mission is completed, a robot can be disassembled and its parts used to configure a new robot to meet a different task.
The team calls the system WORMS, for the Walking Oligomeric Robotic Mobility System. The system’s parts include worm-inspired robotic limbs that an astronaut can easily snap onto a base, and that work together as a walking robot. Depending on the mission, parts can be configured to build, for instance, large “pack” bots capable of carrying heavy solar panels up a hill. The same parts could be reconfigured into six-legged spider bots that can be lowered into a lava tube to drill for frozen water.
“You could imagine a shed on the moon with shelves of worms,” says team leader George Lordos, a PhD candidate and graduate instructor in MIT’s Department of Aeronautics and Astronautics (AeroAstro), in reference to the independent, articulated robots that carry their own motors, sensors, computer, and battery. “Astronauts could go into the shed, pick the worms they need, along with the right shoes, body, sensors and tools, and they could snap everything together, then disassemble it to make a new one. The design is flexible, sustainable, and cost-effective.”
Lordos’ team has built and demonstrated a six-legged WORMS robot. Last week, they presented their results at IEEE’s Aerospace Conference, where they also received the conference’s Best Paper Award.
MIT team members include Michael J. Brown, Kir Latyshev, Aileen Liao, Sharmi Shah, Cesar Meza, Brooke Bensche, Cynthia Cao, Yang Chen, Alex S. Miller, Aditya Mehrotra, Jacob Rodriguez, Anna Mokkapati, Tomas Cantu, Katherina Sapozhnikov, Jessica Rutledge, David Trumper, Sangbae Kim, Olivier de Weck, Jeffrey Hoffman, along with Aleks Siemenn, Cormac O’Neill, Diego Rivero, Fiona Lin, Hanfei Cui, Isabella Golemme, John Zhang, Jolie Bercow, Prajwal Mahesh, Stephanie Howe, and Zeyad Al Awwad, as well as Chiara Rissola of Carnegie Mellon University and Wendell Chun of the University of Denver.

Animal instincts
WORMS was conceived in 2022 as an answer to NASA’s Breakthrough, Innovative and Game-changing (BIG) Idea Challenge — an annual competition for university students to design, develop, and demonstrate a game-changing idea. In 2022, NASA challenged students to develop robotic systems that can move across extreme terrain, without the use of wheels.
A team from MIT’s Space Resources Workshop took up the challenge, aiming specifically for a lunar robot design that could navigate the extreme terrain of the moon’s South Pole — a landscape that is marked by thick, fluffy dust; steep, rocky slopes; and deep lava tubes. The environment also hosts “permanently shadowed” regions that could contain frozen water, which, if accessible, would be essential for sustaining astronauts.
As they mulled over ways to navigate the moon’s polar terrain, the students took inspiration from animals. In their initial brainstorming, they noted certain animals could conceptually be suited to certain missions: A spider could drop down and explore a lava tube, a line of elephants could carry heavy equipment while supporting each other down a steep slope, and a goat, tethered to an ox, could help lead the larger animal up the side of a hill as it transports an array of solar panels.
“As we were thinking of these animal inspirations, we realized that one of the simplest animals, the worm, makes similar movements as an arm, or a leg, or a backbone, or a tail,” says deputy team leader and AeroAstro graduate student Michael Brown. “And then the lightbulb went off: We could build all these animal-inspired robots using worm-like appendages.'”
Snap on, snap off

Lordos, who is of Greek descent, helped coin WORMS, and chose the letter “O” to stand for “oligomeric,” which in Greek signifies “a few parts.”
“Our idea was that, with just a few parts, combined in different ways, you could mix and match and get all these different robots,” says AeroAstro undergraduate Brooke Bensche.
The system’s main parts include the appendage, or worm, which can be attached to a body, or chassis, via a “universal interface block” that snaps the two parts together through a twist-and-lock mechanism. The parts can be disconnected with a small tool that releases the block’s spring-loaded pins.
Appendages and bodies can also snap into accessories such as a “shoe,” which the team engineered in the shape of a wok, and a LiDAR system that can map the surroundings to help a robot navigate.
“In future iterations we hope to add more snap-on sensors and tools, such as winches, balance sensors, and drills,” says AeroAstro undergraduate Jacob Rodriguez.
The team developed software that can be tailored to coordinate multiple appendages. As a proof of concept, the team built a six-legged robot about the size of a go-cart. In the lab, they showed that once assembled, the robot’s independent limbs worked to walk over level ground. The team also showed that they could quickly assemble and disassemble the robot in the field, on a desert site in California.
In its first generation, each WORMS appendage measures about 1 meter long and weighs about 20 pounds. In the moon’s gravity, which is about one-sixth that of Earth’s, each limb would weigh about 3 pounds, which an astronaut could easily handle to build or disassemble a robot in the field. The team has planned out the specs for a larger generation with longer and slightly heavier appendages. These bigger parts could be snapped together to build “pack” bots, capable of transporting heavy payloads.
“There are many buzz words that are used to describe effective systems for future space exploration: modular, reconfigurable, adaptable, flexible, cross-cutting, et cetera,” says Kevin Kempton, an engineer at NASA’s Langley Research Center, who served as a judge for the 2022 BIG Idea Challenge. “The MIT WORMS concept incorporates all these qualities and more.”
This research was supported, in part, by NASA, MIT, the Massachusetts Space Grant, the National Science Foundation, and the Fannie and John Hertz Foundation.
Video: https://youtu.be/U72lmSXEVkM More

Robots can be useful as mental wellbeing coaches in the workplace — but perception of their effectiveness depends in large part on what the robot looks like.
Researchers from the University of Cambridge carried out a study in a tech consultancy firm using two different robot wellbeing coaches, where 26 employees participated in weekly robot-led wellbeing sessions for four weeks. Although the robots had identical voices, facial expressions, and scripts for the sessions, the robots’ physical appearance affected how participants interacted with it.
Participants who did their wellbeing exercises with a toy-like robot said that they felt more of a connection with their ‘coach’ than participants who worked with a humanoid-like robot. The researchers say that perception of robots is affected by popular culture, where the only limit on what robots can do is the imagination. When faced with a robot in the real world however, it often does not live up to expectations.
Since the toy-like robot looks simpler, participants may have had lower expectations and ended up finding the robot easier to talk connect with. Participants who worked with the humanoid robot found that their expectations didn’t match reality, since the robot was not capable of having interactive conversations.
Despite the differences between expectations and reality, the researchers say that their study shows that robots can be a useful tool to promote mental wellbeing in the workplace. The results will be reported today (15 March) at the ACM/IEEE International Conference on Human-Robot Interaction in Stockholm.
The World Health Organization recommends that employers take action to promote and protect mental wellbeing at work, but the implementation of wellbeing practices is often limited by a lack of resources and personnel. Robots have shown some early promise for helping address this gap, but most studies on robots and wellbeing have been conducted in a laboratory setting.

“We wanted to take the robots out of the lab and study how they might be useful in the real world,” said Dr Micol Spitale, the paper’s first author.
The researchers collaborated with local technology company Cambridge Consultants to design and implement a workplace wellbeing programme using robots. Over the course of four weeks, employees were guided through four different wellbeing exercises by one of two robots: either the QTRobot (QT) or the Misty II robot (Misty).
The QT is a childlike humanoid robot and roughly 90cm tall, while Misty is a 36cm tall toy-like robot. Both robots have screen faces that can be programmed with different facial expressions.
“We interviewed different wellbeing coaches and then we programmed our robots to have a coach-like personality, with high openness and conscientiousness,” said co-author Minja Axelsson. “The robots were programmed to have the same personality, the same facial expressions and the same voice, so the only difference between them was the physical robot form.”
Participants in the experiment were guided through different positive psychology exercises by a robot in an office meeting room. Each session started with the robot asking participants to recall a positive experience or describe something in their lives they were grateful for, and the robot would ask follow-up questions. After the sessions, participants were asked to assess the robot with a questionnaire and an interview. Participants did one session per week for four weeks, and worked with the same robot for each session.

Participants who worked with the toy-like Misty robot reported that they had a better working connection with the robot than participants who worked with the child-like QT robot. Participants also had a more positive perception of Misty overall.
“It could be that since the Misty robot is more toy-like, it matched their expectations,” said Spitale. “But since QT is more humanoid, they expected it to behave like a human, which may be why participants who worked with QT were slightly underwhelmed.”
“The most common response we had from participants was that their expectations of the robot didn’t match with reality,” said Professor Hatice Gunes from Cambridge’s Department of Computer Science and Technology, who led the research. “We programmed the robots with a script, but participants were hoping there would be more interactivity. It’s incredibly difficult to create a robot that’s capable of natural conversation. New developments in large language models could really be beneficial in this respect.”
“Our perceptions of how robots should look or behave might be holding back the uptake of robotics in areas where they can be useful,” said Axelsson.
Although the robots used in the experiment are not as advanced as C-3PO or other fictional robots, participants still said they found the wellbeing exercises helpful, and that they were open to the idea of talking to a robot in future.
“The robot can serve as a physical reminder to commit to the practice of wellbeing exercises,” said Gunes. “And just saying things out loud, even to a robot, can be helpful when you’re trying to improve mental wellbeing.”
The team is now working to enhance the robot coaches’ responsiveness during the coaching practices and interactions.
The research was supported by the Engineering and Physical Sciences Research Council (EPSRC), part of UK Research and Innovation (UKRI). Hatice Gunes is a Staff Fellow of Trinity Hall, Cambridge. More

Since the development of functional magnetic resonance imaging in the 1990s, the reliance on neuroimaging has skyrocketed as researchers investigate how fMRI data from the brain at rest, and anatomical brain structure itself, can be used to predict individual traits, such as depression, cognitive decline, and brain disorders.
Brain imaging has the potential to reveal the neural underpinnings of many traits, from disorders like depression and chronic widespread pain to why one person has a better memory than another, and why some people’s memories are resilient as they age. But how reliable brain imaging is for detecting traits has been a subject of wide debate.
Prior research on brain-wide associated studies (termed ‘BWAS’) has shown that links between brain function and structure and traits are so weak that thousands of participants are needed to detect replicable effects. Research of this scale requires millions of dollars in investment in each study, limiting which traits and brain disorders can be studied.
However, according to a new commentary published in Nature, stronger links between brain measures and traits can be obtained when state-of-the-art pattern recognition (or ‘machine learning’) algorithms are utilized, which can garner high-powered results from moderate sample sizes.
In their article, researchers from Dartmouth and University Medicine Essen provide a response to an earlier analysis of brain-wide association studies led by Scott Marek at Washington University School of Medicine in St. Louis, Brenden Tervo-Clemmens at Massachusetts General Hospital/Harvard Medical School, and colleagues. The earlier study found very weak associations across a range of traits in several large brain imaging studies, concluding that thousands of participants would be needed to detect these associations.
The new article explains that the very weak effects found in the earlier paper do not apply to all brain images and all traits, but rather are limited to specific cases. It outlines how fMRI data from hundreds of participants, as opposed to thousands, can be better leveraged to yield important diagnostic information about individuals.

One key to stronger associations between brain images and traits such as memory and intelligence is the use of state-of-the-art pattern recognition algorithms. “Given that there’s virtually no mental function performed entirely by one area of the brain, we recommend using pattern recognition to develop models of how multiple brain areas contribute to predicting traits, rather than testing brain areas individually,” says senior author Tor Wager, the Diana L. Taylor Distinguished Professor of Psychological and Brain Sciences and director of the Brain Imaging Center at Dartmouth.
“If models of multiple brain areas working together rather than in isolation are applied, this provides for a much more powerful approach in neuroimaging studies, yielding predictive effects that are four times larger than when testing brain areas in isolation,” says lead author Tamas Spisak, head of the Predictive Neuroimaging Lab at the Institute of Diagnostic and Interventional Radiology and Neuroradiology at University Medicine Essen.
However, not all pattern recognition algorithms are equal and finding the algorithms that work best for specific types of brain imaging data is an active area of research. The earlier paper by Marek, Tervo-Clemmens et al. also tested whether pattern recognition can be used to predict traits from brain images, but Spisak and colleagues found that the algorithm they used is suboptimal.
When the researchers applied a more powerful algorithm, the effects got even larger and reliable associations could be detected in much smaller samples. “When you do the power calculations on how many participants are needed to detect replicable effects, the number drops to below 500 people,” Spisak says.
“This opens the field to studies of many traits and clinical conditions for which obtaining thousands of patients is not possible, including rare brain disorders,” says co-author Ulrike Bingel at University Medicine Essen, who is the head of the University Centre for Pain Medicine. “Identifying markers, including those involving the central nervous system, are urgently needed, as they are critical to improve diagnostics and individually tailored treatment approaches. We need to move towards a personalized medicine approach grounded in neuroscience. The potential for multivariate BWAS to move us towards this goal should not be underestimated.”
The team explains that the weak associations found in the earlier analysis, particularly through brain images, were collected while people were simply resting in the scanner, rather than performing tasks. But fMRI can also capture brain activity linked to specific moment-by-moment thoughts and experiences.
Wager believes that linking brain patterns to these experiences may be a key to understanding and predicting differences among individuals. “One of the challenges associated with using brain imaging to predict traits is that many traits aren’t stable or reliable. If we use brain imaging to focus on studying mental states and experiences, such as pain, empathy, and drug craving, the effects can be much larger and more reliable,” says Wager. “The key is finding the right task to capture the state.”
“For example, showing images of drugs to people with substance use disorders can elicit drug cravings, according to an earlier study revealing a neuromarker for cravings,” says Wager.
“Identifying which approaches to understanding the brain and mind are most likely to succeed is important, as this affects how stakeholders view and ultimately fund translational research in neuroimaging,” says Bingel. “Finding the limitations and working together to overcome them is key to developing new ways of diagnosing and caring for patients with brain and mental health disorders.” More

To understand how driverless vehicles can navigate the complexities of the road, researchers often use game theory — mathematical models representing the way rational agents behave strategically to meet their goals.
Dejan Milutinovic, professor of electrical and computer engineering at UC Santa Cruz, has long worked with colleagues on the complex subset of game theory called differential games, which have to do with game players in motion. One of these games is called the wall pursuit game, a relatively simple model for a situation in which a faster pursuer has the goal to catch a slower evader who is confined to moving along a wall.
Since this game was first described nearly 60 years ago, there has been a dilemma within the game — a set of positions where it was thought that no game optimal solution existed. But now, Milutinovic and his colleagues have proved in a new paper published in the journal IEEE Transactions on Automatic Control that this long-standing dilemma does not actually exist, and introduced a new method of analysis that proves there is always a deterministic solution to the wall pursuit game. This discovery opens the door to resolving other similar challenges that exist within the field of differential games, and enables better reasoning about autonomous systems such as driverless vehicles.
Game theory is used to reason about behavior across a wide range of fields, such as economics, political science, computer science and engineering. Within game theory, the Nash equilibrium is one of the most commonly recognized concepts. The concept was introduced by mathematician John Nash and it defines game optimal strategies for all players in the game to finish the game with the least regret. Any player who chooses not to play their game optimal strategy will end up with more regret, therefore, rational players are all motivated to play their equilibrium strategy.
This concept applies to the wall pursuit game — a classical Nash equilibrium strategy pair for the two players, the pursuer and evader, that describes their best strategy in almost all of their positions. However, there are a set of positions between the pursuer and evader for which the classical analysis fails to yield the game optimal strategies and concludes with the existence of the dilemma. This set of positions are known as a singular surface — and for years, the research community has accepted the dilemma as fact.
But Milutinovic and his co-authors were unwilling to accept this.

“This bothered us because we thought, if the evader knows there is a singular surface, there is a threat that the evader can go to the singular surface and misuse it,” Milutinovic said. “The evader can force you to go to the singular surface where you don’t know how to act optimally — and then we just don’t know what the implication of that would be in much more complicated games.”
So Milutinovic and his coauthors came up with a new way to approach the problem, using a mathematical concept that was not in existence when the wall pursuit game was originally conceived. By using the viscosity solution of the Hamilton-Jacobi-Isaacs equation and introducing a rate of loss analysis for solving the singular surface they were able to find that a game optimal solution can be determined in all circumstances of the game and resolve the dilemma.
The viscosity solution of partial differential equations is a mathematical concept that was non-existent until the 1980s and offers a unique line of reasoning about the solution of the Hamilton-Jacobi-Isaacs equation. It is now well known that the concept is relevant for reasoning about optimal control and game theory problems.
Using viscosity solutions, which are functions, to solve game theory problems involves using calculus to find the derivatives of these functions. It is relatively easy to find game optimal solutions when the viscosity solution associated with a game has well-defined derivatives. This is not the case for the wall-pursuit game, and this lack of well-defined derivatives creates the dilemma.
Typically when a dilemma exists, a practical approach is that players randomly choose one of possible actions and accept losses resulting from these decisions. But here lies the catch: if there is a loss, each rational player will want to minimize it.

So to find how players might minimize their losses, the authors analyzed the viscosity solution of the Hamilton-Jacobi-Isaacs equation around the singular surface where the derivatives are not well-defined. Then, they introduced a rate of loss analysis across these singular surface states of the equation. They found that when each actor minimizes its rate of losses, there are well-defined game strategies for their actions on the singular surface.
The authors found that not only does this rate of loss minimization define the game optimal actions for the singular surface, but it is also in agreement with the game optimal actions in every possible state where the classical analysis is also able to find these actions.
“When we take the rate of loss analysis and apply it elsewhere, the game optimal actions from the classical analysis are not impacted ,” Milutinovic said. “We take the classical theory and we augment it with the rate of loss analysis, so a solution exists everywhere. This is an important result showing that the augmentation is not just a fix to find a solution on the singular surface, but a fundamental contribution to game theory.
Milutinovic and his coauthors are interested in exploring other game theory problems with singular surfaces where their new method could be applied. The paper is also an open call to the research community to similarly examine other dilemmas.
“Now the question is, what kind of other dilemmas can we solve?” Milutinovic said. More

Practical carbon capture technologies are still in the early stages of development, with the most promising involving a class of compounds called amines that can chemically bind with carbon dioxide. In AVS Quantum Science, researchers deploy an algorithm to study amine reactions through quantum computing. An existing quantum computer cab run the algorithm to find useful amine compounds for carbon capture more quickly, analyzing larger molecules and more complex reactions than a traditional computer can.
The amount of carbon dioxide in the atmosphere increases daily with no sign of stopping or slowing. Too much of civilization depends on the burning of fossil fuels, and even if we can develop a replacement energy source, much of the damage has already been done. Without removal, the carbon dioxide already in the atmosphere will continue to wreak havoc for centuries.
Atmospheric carbon capture is a potential remedy to this problem. It would pull carbon dioxide out of the air and store it permanently to reverse the effects of climate change. Practical carbon capture technologies are still in the early stages of development, with the most promising involving a class of compounds called amines that can chemically bind with carbon dioxide. Efficiency is paramount in these designs, and identifying even slightly better compounds could lead to the capture of billions of tons of additional carbon dioxide.
In AVS Quantum Science, by AIP Publishing, researchers from the National Energy Technology Laboratory and the University of Kentucky deployed an algorithm to study amine reactions through quantum computing. The algorithm can be run on an existing quantum computer to find useful amine compounds for carbon capture more quickly.
“We are not satisfied with the current amine molecules that we use for this [carbon capture] process,” said author Qing Shao. “We can try to find a new molecule to do it, but if we want to test it using classical computing resources, it will be a very expensive calculation. Our hope is to have a fast algorithm that can screen thousands of new molecules and structures.”
Any computer algorithm that simulates a chemical reaction needs to account for the interactions between every pair of atoms involved. Even a simple three-atom molecule like carbon dioxide bonding with the simplest amine, ammonia, which has four atoms, results in hundreds of atomic interactions. This problem vexes traditional computers but is exactly the sort of question at which quantum computers excel.
However, quantum computers are still a developing technology and are not powerful enough to handle these kinds of simulations directly. This is where the group’s algorithm comes in: It allows existing quantum computers to analyze larger molecules and more complex reactions, which is vital for practical applications in fields like carbon capture.
“We are trying to use the current quantum computing technology to solve a practical environmental problem,” said author Yuhua Duan. More

In a world experiencing growing inequality and intolerance, tools borrowed from science and mathematics could be the key to understanding and preventing prejudice.
In Chaos, by AIP Publishing, Luis A. Martinez-Vaquero of the Polytechnic University of Madrid applied evolutionary game theory, which combines techniques from economics and biology, and complex system analysis to investigate the relationship between inequality and intolerance. He found that inequality boosts intolerance and that redistribution of wealth can prevent its infectious spread.
Martinez-Vaquero studied cooperation using an indirect reciprocity model. Unlike direct reciprocity, where individuals base their decisions only on the past encounters with the specific individuals they are interacting with, indirect reciprocity relies on a third party. In this case, individuals assign other individuals reputations based on the actions they witness. They base future encounters on that reputation.
“Thus, strategies under reputation-based indirect reciprocity consist of two parts: moral assessments, which dictate how individuals judge the interactions of others and attribute reputations, and action rules, which indicate how one should interact with others based on what these reputations are,” said Martinez-Vaquero.
In this model, a population is divided between a high-income group and low-income group. Individuals randomly interact in pairs as a donor or a recipient. The donor chooses if they want to contribute funds to the recipient based on both of their reputations.
The population witnesses the interaction and judges the donor’s action as “good” or “bad,” updating the donor’s reputation accordingly.
Tolerant individuals do not judge based on income when deciding to donate, while intolerant individuals will assign those from the opposite income group “bad” reputations. Intolerant individuals will also judge individuals from their group as “bad” for cooperating with the opposite group.
“Inequality clearly enhances the emergence of intolerance, escalating it even without the presence of new individuals who bring these behaviors. Once intolerance begins to act, it is almost unstoppable in the presence of inequality,” said Martinez-Vaquero.
In some scenarios, results also show that economically disfavored individuals from minority groups prioritized helping wealthy individuals over members of their group, even when wealthy individuals discriminated against them.
Still, redistribution of wealth early during the spread of intolerance proved an effective intervention.
This simplified model is a foundation for future investigation into preventing intolerance by intercepting wealth inequality. It demonstrates the power of complex systems, which are used across many fields including climate science, thermodynamics, chaos theory, and neural network computing. More

A team of dermatologists has developed an artificial intelligence (AI) model that empowers atopic dermatitis (AD) patients to detect complications from bacterial or viral infections and distinguish between eczema and skin lesions caused by a type of blood cancer.
The AI model is detailed in a paper published in the Journal of Dermatological Science on January 11, 2023.
AD is a chronic disease that affects around 12 percent of people and often begins in childhood. Patients with AD typically have a suppressed skin immune barrier, which reduces their protection against microbial pathogens, leading to complications of the eczema from bacterial or viral infections. This may include herpes simplex, impetigo, and Kaposi varicelliform eruption (eczema herpeticum).
Recognizing whether AD has led to any of these complications can be challenging for patients as the symptoms’ appearance on the skin is very similar to AD itself. Moreover, mycosis fungoides, a type of blood cancer that causes skin lesions, can also exhibit similar symptoms to AD and may co-exist with AD. Some medications for AD can even worsen infections or mycosis fungoides.
Proper and early diagnosis of complications and malignant diseases is critical for appropriate treatment and better outcomes. However, patients cannot always recognize any abnormal symptoms and visit a doctor as soon as possible due to the similarity of symptoms.
To address this issue, the team trained their convolutional neural network (CNN) model on non-standard images of AD, impetigo, mycosis fungoides, herpes simplex, and Kaposi varicelliform eruption. They then compared the AI’s diagnosis accuracy to a set of non-standard images manually cropped and diagnostically annotated by dermatologists. They found that their system achieved a diagnostic accuracy almost equal to the manually assessed image set.
The team is currently developing an AI-powered smartphone app to translate their system, enabling patients to manage their skin diseases remotely with just their phone’s camera. They are also experimenting with AD patients to improve the app’s usability.
Yuta Yanagisawa, a researcher with the Tohoku University School of Medicine and co-author of the paper, said, “A dermatologist would of course be able to spot the difference, but it is incredibly impractical for an AD patient to visit a dermatologist every day. If only there were some handy, low-cost mechanism that replicated that dermatologist’s knowledge and could be used during a patient’s daily regimen of checking their skin.”
The team believes that this technology will help patients with skin diseases to manage their conditions effectively and efficiently, resulting in better health outcomes. More

Most animals can quickly transition from walking to jumping to crawling to swimming if needed without reconfiguring or making major adjustments.
Most robots cannot. But researchers at Carnegie Mellon University have created soft robots that can seamlessly shift from walking to swimming, for example, or crawling to rolling.
“We were inspired by nature to develop a robot that can perform different tasks and adapt to its environment without adding actuators or complexity,” said Dinesh K. Patel, a post-doctoral fellow in the Morphing Matter Lab in the School of Computer Science’s Human-Computer Interaction Institute. “Our bistable actuator is simple, stable and durable, and lays the foundation for future work on dynamic, reconfigurable soft robotics.”
The bistable actuator is made of 3D-printed soft rubber containing shape-memory alloy springs that react to electrical currents by contracting, which causes the actuator to bend. The team used this bistable motion to change the actuator or robot’s shape. Once the robot changes shape, it is stable until another electrical charge morphs it back to its previous configuration.
“Matching how animals transition from walking to swimming to crawling to jumping is a grand challenge for bio-inspired and soft robotics,” said Carmel Majidi, a professor in the Mechanical Engineering Department in CMU’s College of Engineering.
For example, one robot the team created has four curved actuators attached to the corners of a cellphone-sized body made of two bistable actuators. On land, the curved actuators act as legs, allowing the robot to walk. In the water, the bistable actuators change the robot’s shape, putting the curved actuators in an ideal position to act as propellers so it can swim.
“You need to have legs to walk on land, and you need to have a propeller to swim in the water. Building a robot with separate systems designed for each environment adds complexity and weight,” said Xiaonan Huang, an assistant professor of robotics at the University of Michigan and Majidi’s former Ph.D. student. “We use the same system for both environments to create an efficient robot.”
The team created two other robots: one that can crawl and jump, and one inspired by caterpillars and pill bugs that can crawl and roll.
The actuators require only a hundred millisecond of electrical charge to change their shape, and they are durable. The team had a person ride a bicycle over one of the actuators a few times and changed their robots’ shapes hundreds of times to demonstrate durability.
In the future, the robots could be used in rescue situations or to interact with sea animals or coral. Using heat-activated springs in the actuators could open up applications in environmental monitoring, haptics, and reconfigurable electronics and communication.
“There are many interesting and exciting scenarios where energy-efficient and versatile robots like this could be useful,” said Lining Yao, the Cooper-Siegel Assistant Professor in HCII and head of the Morphing Matter Lab.
The team’s research, “Highly Dynamic Bistable Soft Actuator for Reconfigurable Multimodal Soft Robots,” was featured on the cover of the January 2023 issue of Advanced Materials Technologies. The research team included co-first authors Patel and Huang; Yao; Majidi; Yichi Luo, a mechanical engineering master’s student at CMU; and Mrunmayi Mungekar and M. Khalid Jawed, both from the Department of Mechanical and Aerospace Engineering at the University of California, Los Angeles. More