Tag Archives: shape
#439172 Origami based tires can change shape ...
A team of researchers affiliated with Seoul National University, Harvard University and Hankook Tire and Technology Co. Ltd., has developed a tire based on an origami design that allows for changing the shape of a tire while a vehicle is moving. In their paper published in the journal Science Robotics, the group describes their new tire design and how well it worked when tested. Continue reading
Posted in Human Robots
#439081 Classify This Robot-Woven Sneaker With ...
For athletes trying to run fast, the right shoe can be essential to achieving peak performance. For athletes trying to run fast as humanly possible, a runner’s shoe can also become a work of individually customized engineering.
This is why Adidas has married 3D printing with robotic automation in a mass-market footwear project it’s called Futurecraft.Strung, expected to be available for purchase as soon as later this year. Using a customized, 3D-printed sole, a Futurecraft.Strung manufacturing robot can place some 2,000 threads from up to 10 different sneaker yarns in one upper section of the shoe.
Skylar Tibbits, founder and co-director of the Self-Assembly Lab and associate professor in MIT's Department of Architecture, says that because of its small scale, footwear has been an area of focus for 3D printing and additive manufacturing, which involves adding material bit by bit.
“There are really interesting complex geometry problems,” he says. “It’s pretty well suited.”
Photo: Adidas
Beginning with a 3D-printed sole, Adidas robots weave together some 2000 threads from up to 10 different sneaker yarns to make one Futurecraft.Strung shoe—expected on the marketplace later this year or sometime in 2022.
Adidas began working on the Futurecraft.Strung project in 2016. Then two years later, Adidas Futurecraft, the company’s innovation incubator, began collaborating with digital design studio Kram/Weisshaar. In less than a year the team built the software and hardware for the upper part of the shoe, called Strung uppers.
“Most 3D printing in the footwear space has been focused on the midsole or outsole, like the bottom of the shoe,” Tibbits explains. But now, he says, Adidas is bringing robotics and a threaded design to the upper part of the shoe. The company bases its Futurecraft.Strung design on high-resolution scans of how runners’ feet move as they travel.
This more flexible design can benefit athletes in multiple sports, according to an Adidas blog post. It will be able to use motion capture of an athlete’s foot and feedback from the athlete to make the design specific to the athlete’s specific gait. Adidas customizes the weaving of the shoe’s “fabric” (really more like an elaborate woven string figure, a cat’s cradle to fit the foot) to achieve a close and comfortable fit, the company says.
What they call their “4D sole” consists of a design combining 3D printing with materials that can change their shape and properties over time. In fact, Tibbits coined the term 4D printing to describe this process in 2013. The company takes customized data from the Adidas Athlete Intelligent Engine to make the shoe, according to Kram/Weisshaar’s website.
Photo: Adidas
Closeup of the weaving process behind a Futurecraft.Strung shoe
“With Strung for the first time, we can program single threads in any direction, where each thread has a different property or strength,” Fionn Corcoran-Tadd, an innovation designer at Adidas’ Futurecraft lab, said in a company video. Each thread serves a purpose, the video noted. “This is like customized string art for your feet,” Tibbits says.
Although the robotics technology the company uses has been around for many years, what Adidas’s robotic weavers can achieve with thread is a matter of elaborate geometry. “It’s more just like a really elegant way to build up material combining robotics and the fibers and yarns into these intricate and complex patterns,” he says.
Robots can of course create patterns with more precision than if someone wound it by hand, as well as rapidly and reliably changing the yarn and color of the fabric pattern. Adidas says it can make a single upper in 45 minutes and a pair of sneakers in 1 hour and 30 minutes. It plans to reduce this time down to minutes in the months ahead, the company said.
An Adidas spokesperson says sneakers incorporating the Futurecraft.Strung uppers design are a prototype, but the company plans to bring a Strung shoe to market in late 2021 or 2022. However, Adidas Futurecraft sneakers are currently available with a 3D-printed midsole.
Adidas plans to continue gathering data from athletes to customize the uppers of sneakers. “We’re building up a library of knowledge and it will get more interesting as we aggregate data of testing and from different athletes and sports,” the Adidas Futurecraft team writes in a blog post. “The more we understand about how data can become design code, the more we can take that and apply it to new Strung textiles. It’s a continuous evolution.” Continue reading
Posted in Human Robots
#438779 Meet Catfish Charlie, the CIA’s ...
Photo: CIA Museum
CIA roboticists designed Catfish Charlie to take water samples undetected. Why they wanted a spy fish for such a purpose remains classified.
In 1961, Tom Rogers of the Leo Burnett Agency created Charlie the Tuna, a jive-talking cartoon mascot and spokesfish for the StarKist brand. The popular ad campaign ran for several decades, and its catchphrase “Sorry, Charlie” quickly hooked itself in the American lexicon.
When the CIA’s Office of Advanced Technologies and Programs started conducting some fish-focused research in the 1990s, Charlie must have seemed like the perfect code name. Except that the CIA’s Charlie was a catfish. And it was a robot.
More precisely, Charlie was an unmanned underwater vehicle (UUV) designed to surreptitiously collect water samples. Its handler controlled the fish via a line-of-sight radio handset. Not much has been revealed about the fish’s construction except that its body contained a pressure hull, ballast system, and communications system, while its tail housed the propulsion. At 61 centimeters long, Charlie wouldn’t set any biggest-fish records. (Some species of catfish can grow to 2 meters.) Whether Charlie reeled in any useful intel is unknown, as details of its missions are still classified.
For exploring watery environments, nothing beats a robot
The CIA was far from alone in its pursuit of UUVs nor was it the first agency to do so. In the United States, such research began in earnest in the 1950s, with the U.S. Navy’s funding of technology for deep-sea rescue and salvage operations. Other projects looked at sea drones for surveillance and scientific data collection.
Aaron Marburg, a principal electrical and computer engineer who works on UUVs at the University of Washington’s Applied Physics Laboratory, notes that the world’s oceans are largely off-limits to crewed vessels. “The nature of the oceans is that we can only go there with robots,” he told me in a recent Zoom call. To explore those uncharted regions, he said, “we are forced to solve the technical problems and make the robots work.”
Image: Thomas Wells/Applied Physics Laboratory/University of Washington
An oil painting commemorates SPURV, a series of underwater research robots built by the University of Washington’s Applied Physics Lab. In nearly 400 deployments, no SPURVs were lost.
One of the earliest UUVs happens to sit in the hall outside Marburg’s office: the Self-Propelled Underwater Research Vehicle, or SPURV, developed at the applied physics lab beginning in the late ’50s. SPURV’s original purpose was to gather data on the physical properties of the sea, in particular temperature and sound velocity. Unlike Charlie, with its fishy exterior, SPURV had a utilitarian torpedo shape that was more in line with its mission. Just over 3 meters long, it could dive to 3,600 meters, had a top speed of 2.5 m/s, and operated for 5.5 hours on a battery pack. Data was recorded to magnetic tape and later transferred to a photosensitive paper strip recorder or other computer-compatible media and then plotted using an IBM 1130.
Over time, SPURV’s instrumentation grew more capable, and the scope of the project expanded. In one study, for example, SPURV carried a fluorometer to measure the dispersion of dye in the water, to support wake studies. The project was so successful that additional SPURVs were developed, eventually completing nearly 400 missions by the time it ended in 1979.
Working on underwater robots, Marburg says, means balancing technical risks and mission objectives against constraints on funding and other resources. Support for purely speculative research in this area is rare. The goal, then, is to build UUVs that are simple, effective, and reliable. “No one wants to write a report to their funders saying, ‘Sorry, the batteries died, and we lost our million-dollar robot fish in a current,’ ” Marburg says.
A robot fish called SoFi
Since SPURV, there have been many other unmanned underwater vehicles, of various shapes and sizes and for various missions, developed in the United States and elsewhere. UUVs and their autonomous cousins, AUVs, are now routinely used for scientific research, education, and surveillance.
At least a few of these robots have been fish-inspired. In the mid-1990s, for instance, engineers at MIT worked on a RoboTuna, also nicknamed Charlie. Modeled loosely on a blue-fin tuna, it had a propulsion system that mimicked the tail fin of a real fish. This was a big departure from the screws or propellers used on UUVs like SPURV. But this Charlie never swam on its own; it was always tethered to a bank of instruments. The MIT group’s next effort, a RoboPike called Wanda, overcame this limitation and swam freely, but never learned to avoid running into the sides of its tank.
Fast-forward 25 years, and a team from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) unveiled SoFi, a decidedly more fishy robot designed to swim next to real fish without disturbing them. Controlled by a retrofitted Super Nintendo handset, SoFi could dive more than 15 meters, control its own buoyancy, and swim around for up to 40 minutes between battery charges. Noting that SoFi’s creators tested their robot fish in the gorgeous waters off Fiji, IEEE Spectrum’s Evan Ackerman noted, “Part of me is convinced that roboticists take on projects like these…because it’s a great way to justify a trip somewhere exotic.”
SoFi, Wanda, and both Charlies are all examples of biomimetics, a term coined in 1974 to describe the study of biological mechanisms, processes, structures, and substances. Biomimetics looks to nature to inspire design.
Sometimes, the resulting technology proves to be more efficient than its natural counterpart, as Richard James Clapham discovered while researching robotic fish for his Ph.D. at the University of Essex, in England. Under the supervision of robotics expert Huosheng Hu, Clapham studied the swimming motion of Cyprinus carpio, the common carp. He then developed four robots that incorporated carplike swimming, the most capable of which was iSplash-II. When tested under ideal conditions—that is, a tank 5 meters long, 2 meters wide, and 1.5 meters deep—iSpash-II obtained a maximum velocity of 11.6 body lengths per second (or about 3.7 m/s). That’s faster than a real carp, which averages a top velocity of 10 body lengths per second. But iSplash-II fell short of the peak performance of a fish darting quickly to avoid a predator.
Of course, swimming in a test pool or placid lake is one thing; surviving the rough and tumble of a breaking wave is another matter. The latter is something that roboticist Kathryn Daltorio has explored in depth.
Daltorio, an assistant professor at Case Western Reserve University and codirector of the Center for Biologically Inspired Robotics Research there, has studied the movements of cockroaches, earthworms, and crabs for clues on how to build better robots. After watching a crab navigate from the sandy beach to shallow water without being thrown off course by a wave, she was inspired to create an amphibious robot with tapered, curved feet that could dig into the sand. This design allowed her robot to withstand forces up to 138 percent of its body weight.
Photo: Nicole Graf
This robotic crab created by Case Western’s Kathryn Daltorio imitates how real crabs grab the sand to avoid being toppled by waves.
In her designs, Daltorio is following architect Louis Sullivan’s famous maxim: Form follows function. She isn’t trying to imitate the aesthetics of nature—her robot bears only a passing resemblance to a crab—but rather the best functionality. She looks at how animals interact with their environments and steals evolution’s best ideas.
And yet, Daltorio admits, there is also a place for realistic-looking robotic fish, because they can capture the imagination and spark interest in robotics as well as nature. And unlike a hyperrealistic humanoid, a robotic fish is unlikely to fall into the creepiness of the uncanny valley.
In writing this column, I was delighted to come across plenty of recent examples of such robotic fish. Ryomei Engineering, a subsidiary of Mitsubishi Heavy Industries, has developed several: a robo-coelacanth, a robotic gold koi, and a robotic carp. The coelacanth was designed as an educational tool for aquariums, to present a lifelike specimen of a rarely seen fish that is often only known by its fossil record. Meanwhile, engineers at the University of Kitakyushu in Japan created Tai-robot-kun, a credible-looking sea bream. And a team at Evologics, based in Berlin, came up with the BOSS manta ray.
Whatever their official purpose, these nature-inspired robocreatures can inspire us in return. UUVs that open up new and wondrous vistas on the world’s oceans can extend humankind’s ability to explore. We create them, and they enhance us, and that strikes me as a very fair and worthy exchange.
This article appears in the March 2021 print issue as “Catfish, Robot, Swimmer, Spy.”
About the Author
Allison Marsh is an associate professor of history at the University of South Carolina and codirector of the university’s Ann Johnson Institute for Science, Technology & Society. Continue reading
Posted in Human Robots
#438613 Video Friday: Digit Takes a Hike
Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here's what we have so far (send us your events!):
HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
Let us know if you have suggestions for next week, and enjoy today's videos.
It's winter in Oregon, so everything is damp, all the time. No problem for Digit!
Also the case for summer in Oregon.
[ Agility Robotics ]
While other organisms form collective flocks, schools, or swarms for such purposes as mating, predation, and protection, the Lumbriculus variegatus worms are unusual in their ability to braid themselves together to accomplish tasks that unconnected individuals cannot. A new study reported by researchers at the Georgia Institute of Technology describes how the worms self-organize to act as entangled “active matter,” creating surprising collective behaviors whose principles have been applied to help blobs of simple robots evolve their own locomotion.
No, this doesn't squick me out at all, why would it.
[ Georgia Tech ]
A few years ago, we wrote about Zhifeng Huang's jet-foot equipped bipedal robot, and he's been continuing to work on it to the point where it can now step over gaps that are an absolutely astonishing 147% of its leg length.
[ Paper ]
Thanks Zhifeng!
The Inception Drive is a novel, ultra-compact design for an Infinitely Variable Transmission (IVT) that uses nested-pulleys to adjust the gear ratio between input and output shafts. This video shows the first proof-of-concept prototype for a “Fully Balanced” design, where the spinning masses within the drive are completely balanced to reduce vibration, thereby allowing the drive to operate more efficiently and at higher speeds than achievable on an unbalanced design.
As shown in this video, the Inception Drive can change both the speed and direction of rotation of the output shaft while keeping the direction and speed of the input shaft constant. This ability to adjust speed and direction within such a compact package makes the Inception Drive a compelling choice for machine designers in a wide variety of fields, including robotics, automotive, and renewable-energy generation.
[ SRI ]
Robots with kinematic loops are known to have superior mechanical performance. However, due to these loops, their modeling and control is challenging, and prevents a more widespread use. In this paper, we describe a versatile Inverse Kinematics (IK) formulation for the retargeting of expressive motions onto mechanical systems with loops.
[ Disney Research ]
Watch Engineered Arts put together one of its Mesmer robots in a not at all uncanny way.
[ Engineered Arts ]
There's been a bunch of interesting research into vision-based tactile sensing recently; here's some from Van Ho at JAIST:
[ Paper ]
Thanks Van!
This is really more of an automated system than a robot, but these little levitating pucks are very very slick.
ACOPOS 6D is based on the principle of magnetic levitation: Shuttles with integrated permanent magnets float over the surface of electromagnetic motor segments. The modular motor segments are 240 x 240 millimeters in size and can be arranged freely in any shape. A variety of shuttle sizes carry payloads of 0.6 to 14 kilograms and reach speeds of up to 2 meters per second. They can move freely in two-dimensional space, rotate and tilt along three axes and offer precise control over the height of levitation. All together, that gives them six degrees of motion control freedom.
[ ACOPOS ]
Navigation and motion control of a robot to a destination are tasks that have historically been performed with the assumption that contact with the environment is harmful. This makes sense for rigid-bodied robots where obstacle collisions are fundamentally dangerous. However, because many soft robots have bodies that are low-inertia and compliant, obstacle contact is inherently safe. We find that a planner that takes into account and capitalizes on environmental contact produces paths that are more robust to uncertainty than a planner that avoids all obstacle contact.
[ CHARM Lab ]
The quadrotor experts at UZH have been really cranking it up recently.
Aerodynamic forces render accurate high-speed trajectory tracking with quadrotors extremely challenging. These complex aerodynamic effects become a significant disturbance at high speeds, introducing large positional tracking errors, and are extremely difficult to model. To fly at high speeds, feedback control must be able to account for these aerodynamic effects in real-time. This necessitates a modelling procedure that is both accurate and efficient to evaluate. Therefore, we present an approach to model aerodynamic effects using Gaussian Processes, which we incorporate into a Model Predictive Controller to achieve efficient and precise real-time feedback control, leading to up to 70% reduction in trajectory tracking error at high speeds. We verify our method by extensive comparison to a state-of-the-art linear drag model in synthetic and real-world experiments at speeds of up to 14m/s and accelerations beyond 4g.
[ Paper ]
I have not heard much from Harvest Automation over the last couple years and their website was last updated in 2016, but I guess they're selling robots in France, so that's good?
[ Harvest Automation ]
Last year, Clearpath Robotics introduced a ROS package for Spot which enables robotics developers to leverage ROS capabilities out-of-the-box. Here at OTTO Motors, we thought it would be a compelling test case to see just how easy it would be to integrate Spot into our test fleet of OTTO materials handling robots.
[ OTTO Motors ]
Video showcasing recent robotics activities at PRISMA Lab, coordinated by Prof. Bruno Siciliano, at Università di Napoli Federico II.
[ PRISMA Lab ]
Thanks Fan!
State estimation framework developed by the team CoSTAR for the DARPA Subterranean Challenge, where the team achieved 2nd and 1st places in the Tunnel and Urban circuits.
[ Paper ]
Highlights from the 2020 ROS Industrial conference.
[ ROS Industrial ]
Thanks Thilo!
Not robotics, but entertaining anyway. From the CHI 1995 Technical Video Program, “The Tablet Newspaper: a Vision for the Future.”
[ CHI 1995 ]
This week's GRASP on Robotics seminar comes from Allison Okamura at Stanford, on “Wearable Haptic Devices for Ubiquitous Communication.”
Haptic devices allow touch-based information transfer between humans and intelligent systems, enabling communication in a salient but private manner that frees other sensory channels. For such devices to become ubiquitous, their physical and computational aspects must be intuitive and unobtrusive. We explore the design of a wide array of haptic feedback mechanisms, ranging from devices that can be actively touched by the fingertips to multi-modal haptic actuation mounted on the arm. We demonstrate how these devices are effective in virtual reality, human-machine communication, and human-human communication.
[ UPenn ] Continue reading
Posted in Human Robots
#437978 How Mirroring the Architecture of the ...
While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess.
The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. And in the era of Big Data, that’s easier than ever, particularly for the large data-centric tech companies carrying out a lot of the cutting-edge AI research.
Today’s largest deep learning models, like OpenAI’s GPT-3 and Google’s BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.
It also means today’s AI is far less flexible than natural intelligence. While a human only needs to see a handful of examples of an animal, a tool, or some other category of object to be able pick it out again, most AI need to be trained on many examples of an object in order to be able to recognize it.
There is an active sub-discipline of AI research aimed at what is known as “one-shot” or “few-shot” learning, where algorithms are designed to be able to learn from very few examples. But these approaches are still largely experimental, and they can’t come close to matching the fastest learner we know—the human brain.
This prompted a pair of neuroscientists to see if they could design an AI that could learn from few data points by borrowing principles from how we think the brain solves this problem. In a paper in Frontiers in Computational Neuroscience, they explained that the approach significantly boosts AI’s ability to learn new visual concepts from few examples.
“Our model provides a biologically plausible way for artificial neural networks to learn new visual concepts from a small number of examples,” Maximilian Riesenhuber, from Georgetown University Medical Center, said in a press release. “We can get computers to learn much better from few examples by leveraging prior learning in a way that we think mirrors what the brain is doing.”
Several decades of neuroscience research suggest that the brain’s ability to learn so quickly depends on its ability to use prior knowledge to understand new concepts based on little data. When it comes to visual understanding, this can rely on similarities of shape, structure, or color, but the brain can also leverage abstract visual concepts thought to be encoded in a brain region called the anterior temporal lobe (ATL).
“It is like saying that a platypus looks a bit like a duck, a beaver, and a sea otter,” said paper co-author Joshua Rule, from the University of California Berkeley.
The researchers decided to try and recreate this capability by using similar high-level concepts learned by an AI to help it quickly learn previously unseen categories of images.
Deep learning algorithms work by getting layers of artificial neurons to learn increasingly complex features of an image or other data type, which are then used to categorize new data. For instance, early layers will look for simple features like edges, while later ones might look for more complex ones like noses, faces, or even more high-level characteristics.
First they trained the AI on 2.5 million images across 2,000 different categories from the popular ImageNet dataset. They then extracted features from various layers of the network, including the very last layer before the output layer. They refer to these as “conceptual features” because they are the highest-level features learned, and most similar to the abstract concepts that might be encoded in the ATL.
They then used these different sets of features to train the AI to learn new concepts based on 2, 4, 8, 16, 32, 64, and 128 examples. They found that the AI that used the conceptual features yielded much better performance than ones trained using lower-level features on lower numbers of examples, but the gap shrunk as they were fed more training examples.
While the researchers admit the challenge they set their AI was relatively simple and only covers one aspect of the complex process of visual reasoning, they said that using a biologically plausible approach to solving the few-shot problem opens up promising new avenues in both neuroscience and AI.
“Our findings not only suggest techniques that could help computers learn more quickly and efficiently, they can also lead to improved neuroscience experiments aimed at understanding how people learn so quickly, which is not yet well understood,” Riesenhuber said.
As the researchers note, the human visual system is still the gold standard when it comes to understanding the world around us. Borrowing from its design principles might turn out to be a profitable direction for future research.
Image Credit: Gerd Altmann from Pixabay Continue reading
Posted in Human Robots