Artificial muscle makes soft robots stronger

A new type of artificial muscle allows soft robots to lift nearly 1,000 times their own weight.

Scientists from Harvard University and MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have created artificial muscles that allow soft robots to lift objects that are up to 1,000 times their own weight, a new study published in the Proceedings of the National Academy of Sciences reports.

Soft robotics has made large strides over the past decade. However, while recent advancements have enabled the machines to bend and flex in new ways, the softer materials typically come with reduced strength.

The new origami-inspired muscles in the study get around that obstacle and could one day lead to much more efficient machines.

“We were very surprised by how strong the actuators [aka, “muscles”] were,” said study co-author Daniela Rus, the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science at MIT, according to Phys.org. “We expected they’d have a higher maximum functional weight than ordinary soft robots, but we didn’t expect a thousand-fold increase. It’s like giving these robots superpowers.”

Making muscle-like actuators is one of the largest challenges in engineering. Now that it has been overcome, scientists can potentially build nearly any robot for almost any task.

Each artificial muscle consists of an inner “skeleton” made from materials like metal coil or a sheet of folded plastic surrounded by air or fluid and sealed inside a plastic or textile bag. A vacuum inside the bag causes the muscles to move by forcing the “skin” to collapse onto the skeleton. That tension drives the motion, and allows the device to work without any other external human input. 

In the study, the team created dozens of different muscles with materials ranging from metal springs to packing foam to sheets of plastic. They then experimented with different skeleton shapes to create muscles that can contract down to 10 percent of their original size, lift a flower off the ground, and twist into a coil.

Those experiments showed the muscles can move in many ways, and are able to operate with a high amount of resilience. Not only that, but the technology can generate roughly six times more force per unit area than mammalian skeletal muscle, and is both lightweight and easy to make. A single muscle can be constructed within ten minutes using materials that cost less than $1.

Another important property is that the actuators are highly scalable, meaning they can be constructed at different sizes. That is important because it greatly increases their potential applications. The team believes they could one day be used for a wide variety of tasks, including miniature surgical devices, wearable robotic exoskeletons, transformable architecture, deep-sea manipulators, and large deployable structures for space exploration.

“The possibilities really are limitless,” added Rus, in a statement. “But the very next thing I would like to build with these muscles is an elephant robot with a trunk that can manipulate the world in ways that are as flexible and powerful as you see in real elephants.”

Eagled-eyed machine learning algorithm outperforms human experts

Scientists just trained a machine learning algorithm to best human experts in the analysis and detection of microscopic radiation damage in materials.

University of Wisconsin-Madison and Oak Ridge National Laboratory researchers just trained artificial intelligence to consistently and quickly analyze and detect microscopic radiation damage in materials considered for nuclear reactors better than human experts.

“Machine learning has great potential to transform the current, human-involved approach of image analysis in microscopy,” said Wei Li, who participated in the research.

“In the future, I believe images from many instruments will pass through a machine learning algorithm for initial analysis before being considered by humans,” said engineering professor Dane Morgan, Li’s graduate school advisor.

The job in question is crucial for the development of safe nuclear materials and could make the time-consuming process more effective and efficient.

“Human detection and identification is error-prone, inconsistent and inefficient. Perhaps most importantly, it’s not scalable,” Morgan said. “Newer imaging technologies are outstripping human capabilities to analyze the data we can produce.”

After training the machine with 270 images, the neural network, in combination with a cascade object detector machine learning algorithm, was able to identify and classify about 86 percent of dislocation loops in a set of sample pictures. In comparison, human experts only found 80 percent of the defects.

“When we got the final result, everyone was surprised, not only by the accuracy of the approach, but the speed,” said Oak Ridge staff scientist Kevin Field. “We can now detect these loops like humans while doing it in a fraction of the time on a standard home computer.”

“This is just the beginning,” Morgan said. “Machine learning tools will help create a cyber infrastructure that scientists can utilize in ways we are just beginning to understand.”

Robots might hold key to social success for kids with autism

A new study shines a light on the potential of robots for teaching behavior to autistic children.

A new study suggests that autonomous “social” robots can be utilized in home-based therapy to model and encourage behaviors to children with autism spectrum disorder. The behaviors included paying attention and maintaining eye contact.

According to the mother of one boy who took part in the experiment, she observed tangible results in her son, who is typically “a little awkward at times” with people he is not close to.

“From watching the robot and interacting with the robot, that really engaged him, and it made him, I think, connect the dots. His interactions became more consistent,” she said. “His eye contact became more consistent.”

“It really just showed me how bright he is and how quick he is,” she continued. “And it gave us time together, to kind of learn about each other. He’s a lot of fun, and this really brought out really good qualities for him.”

“The robot acted in part as the game moderator, selecting appropriate difficulty levels, posing new challenges [and] advancing the narrative of the games,” said study leader Brian Scassellati, who is from Yale University.

Thomas Frazier, chief science officer for Autism Speaks, believes that the study is an “important advance.”

“The authors are careful to note several of the current limitations, including the relatively restricted context in which the robot is being used,” he said. “But, at the same time, this provides a nice advance toward the ultimate goal of personalized support throughout the day and in various settings.”

“The fact that engagement was so high, performance improved and caregivers felt that the child’s behavior improved is impressive,” he added.

The findings were published in Science Robotics.

Cannabis-like drug could help Alzheimer’s patients, study says

A new study sheds light on the promise of a cannabis-like drug called nabilone for treating agitation in Alzheimer’s patients.

A new study suggests that a cannabis-like drug called nabilone might treat agitation in patients with Alzheimer’s disease. The synthetic drug is typically used to control nausea in cancer patients.

Agitation is one of the most disturbing Alzheimer’s symptoms and it can be very tough to manage. In order to control the symptom, many doctors go against medical advice and prescribe antipsychotic drugs and sometimes even physically restrain patients.

“Agitation, aggression, sleep disturbances — all have a significant impact on both their quality of life and their carers’ quality of life,” said Heather Snyder, senior director of medical operations for the Alzheimer’s Association.

“Currently prescribed treatments for agitation in Alzheimer’s do not work in everybody, and when they do work the effect is small and they increase risk of harmful side effects, including increased risk of death,” said Krista Lanctôt of the University of Toronto, who led the research. “As a result, there is an urgent need for safer medication options.”

Lanctôt are her team tested nabilone for six weeks by giving the pill to 39 dementia patients. Afterwards, they gave them a placebo for an additional six weeks.

“Agitation improved significantly in those taking nabilone, compared to placebo,” the Alzheimer’s Association said in a summary of the research, which is being presented at its annual Chicago meeting.

The non-profit association also said that “nabilone also significantly improved overall behavioral symptoms, compared to placebo, as measured by the Neuropsychiatric Inventory” questionnaire.

The findings were published in The American Journal of Geriatric Psychiatry.

Researchers use underwater Pokeball to capture sea creatures

A team of researchers created an origami-inspired underwater Pokeball that captures sea creatures inside its folding polyhedral enclosure.

Researchers from Harvard University’s Wyss Institute, John A. Paulson School of Engineering and Applied Sciences (SEAS) created a new device comparable to an underwater Pokéball that traps sea creatures safely inside its folding polyhedral enclosure. And using its unique, origami-inspired design, it lets them go without harm.

“We approach these animals as if they are works of art: would we cut pieces out of the Mona Lisa to study it? No—we’d use the most innovative tools available,” said the study’s collaborating author David Gruber. “These deep-sea organisms, some being thousands of years old, deserve to be treated with a similar gentleness when we’re interacting with them.”

The idea for the device began back in 2014 when first author Zhi Ern Teoh took a class from Chuck Hoberman that covered the creation of folding mechanisms through computational means.

“I was building microrobots by hand in graduate school, which was very painstaking and tedious work, and I wondered if there was a way to fold a flat surface into a three-dimensional shape using a motor instead,” Teoh said.

“The RAD sampler design is perfect for the difficult environment of the deep ocean because its controls are very simple, so there are fewer elements that can break,” Teoh added. “It’s also modular, so if something does break, we can simply replace that part and send the sampler back down into the water. This folding design is also well-suited to be used in space, which is similar to the deep ocean in that it’s a low-gravity, inhospitable environment that makes operating any device challenging.”

The findings were published in Science Robotics.

NASA planning to award contract to test flying drones on Venus

NASA plans to award a contract to Black Swift Technologies that lets them test flying drones in Venus’ firey atmosphere.

NASA is planning to award a contract to study flying drones on Venus. The difficult challenge will be conducted in partnership with Black Swift Technologies, a company located in Boulder that specializes in unmanned aerial systems (UAS).

The mission will hinge on the creation of a drone that can survive in Venus’ upper atmosphere. It won’t be easy, but if all of Black Swift’s designs are good enough, NASA is going to award the company a contract for a Venus aerial drone.

“They’re looking for vehicles to explore just above the cloud layer,” said Jack Elston, who co-founded of Black Swift Technologies. “The pressure and temperatures are similar to what you’d find on Earth, so it could be a good environment for looking for evidence of life. The winds in the upper atmosphere of Venus are incredibly strong, which creates design challenge.”

Elston and his team are going to focus on creating a unique aircraft and an energy-harvesting method to help it survive Venus’ upper atmosphere.

“Our experience working on unmanned aircraft systems that interact with severe convective storms on Earth will hopefully provide a valuable contribution to the ongoing discussion for how best to explore this turbulent environment,” he said. “Additionally, the work we do will help inform better designs of our own aircraft and should lead to longer observation times and more robust aircraft to observe everything from volcanic plumes to hurricanes.”

The end-goal of this kind of research is to determine if Venus was once a more habitable planet, and if so, how it came to be the hellish environment that it is today.

Tesla enlists humans after Tesla Model 3 robotic production failures

‘Humans are underrated,’ says Tesla’s CEO Elon Musk, proceeding the company’s failed attempt to attain weekly production targets in first quarter of 2018.

CEO and product architect Elon Musk publicly explains one of the reasons towards Tesla’s unsuccessful first quarter of 2018. Musk states that automation has been hindering Tesla’s Model 3 production due to a stronger focus on robotics rather than humans, resulting in “excessive automation”.

During a tour around Tesla’s factory, CBS presented inquiries concerning a correlation of robots and slower production instead of heightening manufacturing speed. Musk replied, “Yes, they did. We had this crazy, complex network of conveyor belts and it was not working, so we got rid of that whole thing.”

Additionally, Musk explained, “Yes, excessive automation at Tesla was a mistake. To be precise, my mistake. Humans are underrated.”

As a result of production failure towards 2,500 Model 3 vehicles, in the first quarter of 2018, uncertainty escalates for Tesla’s ability to reach its 5,000-a-week target in three months time. In return, the shortfall has delayed crucial customer deliveries. Musk described his forced actions to take direct control of the production line at the beginning of April, with recourse to working through the night and sleeping at the factory.

In the meantime, Tesla is confronting adverse public relations involving a fatal accident of one of its Model X SUVs that was operating using the company’s Autopilot mode.

Soft robotics draws inspiration from Octopus

Often scientists joke that it is as if it was dropped on this planet by some advanced alien species.

The octopus is one of the most advanced species on the planet. Often scientists joke that it is as if it was dropped on this planet by some advanced alien species. With over eight survival traits such as ink clouds and camouflage far superior to that of chameleons, it is easy to see why.

However, there is so much more scientists can learn from the octopus. One of them is how its muscular system works. Suffice to have eight powerful limps each able to pull almost ten times its weight, and the octopus muscles are also highly flexible. It is able to push its whole body through a keyhole.

Scientists in soft robotics have decided to use the principle of the Octopus muscular system to make significant progress in soft robotics. The “octobot” is made from silicon and liquid-gas muscles. Its locomotion depends on chemical reactions that push into limps.

At the moment it’s movement is highly irregular but still a big step forward. Prior soft robots still had hardware and were operated remotely. But the new version is entirely self-sufficient. “Many of the previous embodiments required tethers to external controllers or power sources,” said PhD student Ryan Truby from Harvard University. “What we’ve tried to do is actually to replace these hardware components entirely and have a completely soft robotic system.”

The scientist explains that he hopes that one day soft robot can do tasks that are humanly impossible even with hard robotics. They foresee a time when such robots can carry out internal surgery without significant insertions.