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#435023 Inflatable Robot Astronauts and How to ...

The typical cultural image of a robot—as a steel, chrome, humanoid bucket of bolts—is often far from the reality of cutting-edge robotics research. There are difficulties, both social and technological, in realizing the image of a robot from science fiction—let alone one that can actually help around the house. Often, it’s simply the case that great expense in producing a humanoid robot that can perform dozens of tasks quite badly is less appropriate than producing some other design that’s optimized to a specific situation.

A team of scientists from Brigham Young University has received funding from NASA to investigate an inflatable robot called, improbably, King Louie. The robot was developed by Pneubotics, who have a long track record in the world of soft robotics.

In space, weight is at a premium. The world watched in awe and amusement when Commander Chris Hadfield sang “Space Oddity” from the International Space Station—but launching that guitar into space likely cost around $100,000. A good price for launching payload into outer space is on the order of $10,000 per pound ($22,000/kg).

For that price, it would cost a cool $1.7 million to launch Boston Dynamics’ famous ATLAS robot to the International Space Station, and its bulk would be inconvenient in the cramped living quarters available. By contrast, an inflatable robot like King Louie is substantially lighter and can simply be deflated and folded away when not in use. The robot can be manufactured from cheap, lightweight, and flexible materials, and minor damage is easy to repair.

Inflatable Robots Under Pressure
The concept of inflatable robots is not new: indeed, earlier prototypes of King Louie were exhibited back in 2013 at Google I/O’s After Hours, flailing away at each other in a boxing ring. Sparks might fly in fights between traditional robots, but the aim here was to demonstrate that the robots are passively safe: the soft, inflatable figures won’t accidentally smash delicate items when moving around.

Health and safety regulations form part of the reason why robots don’t work alongside humans more often, but soft robots would be far safer to use in healthcare or around children (whose first instinct, according to BYU’s promotional video, is either to hug or punch King Louie.) It’s also much harder to have nightmarish fantasies about robotic domination with these friendlier softbots: Terminator would’ve been a much shorter franchise if Skynet’s droids were inflatable.

Robotic exoskeletons are increasingly used for physical rehabilitation therapies, as well as for industrial purposes. As countries like Japan seek to care for their aging populations with robots and alleviate the burden on nurses, who suffer from some of the highest rates of back injuries of any profession, soft robots will become increasingly attractive for use in healthcare.

Precision and Proprioception
The main issue is one of control. Rigid, metallic robots may be more expensive and more dangerous, but the simple fact of their rigidity makes it easier to map out and control the precise motions of each of the robot’s limbs, digits, and actuators. Individual motors attached to these rigid robots can allow for a great many degrees of freedom—individual directions in which parts of the robot can move—and precision control.

For example, ATLAS has 28 degrees of freedom, while Shadow’s dexterous robot hand alone has 20. This is much harder to do with an inflatable robot, for precisely the same reasons that make it safer. Without hard and rigid bones, other methods of control must be used.

In the case of King Louie, the robot is made up of many expandable air chambers. An air-compressor changes the pressure levels in these air chambers, allowing them to expand and contract. This harks back to some of the earliest pneumatic automata. Pairs of chambers act antagonistically, like muscles, such that when one chamber “tenses,” another relaxes—allowing King Louie to have, for example, four degrees of freedom in each of its arms.

The robot is also surprisingly strong. Professor Killpack, who works at BYU on the project, estimates that its payload is comparable to other humanoid robots on the market, like Rethink Robotics’ Baxter (RIP).

Proprioception, that sixth sense that allows us to map out and control our own bodies and muscles in fine detail, is being enhanced for a wider range of soft, flexible robots with the use of machine learning algorithms connected to input from a whole host of sensors on the robot’s body.

Part of the reason this is so complicated with soft, flexible robots is that the shape and “map” of the robot’s body can change; that’s the whole point. But this means that every time King Louie is inflated, its body is a slightly different shape; when it becomes deformed, for example due to picking up objects, the shape changes again, and the complex ways in which the fabric can twist and bend are far more difficult to model and sense than the behavior of the rigid metal of King Louie’s hard counterparts. When you’re looking for precision, seemingly-small changes can be the difference between successfully holding an object or dropping it.

Learning to Move
Researchers at BYU are therefore spending a great deal of time on how to control the soft-bot enough to make it comparably useful. One method involves the commercial tracking technology used in the Vive VR system: by moving the game controller, which provides a constant feedback to the robot’s arm, you can control its position. Since the tracking software provides an estimate of the robot’s joint angles and continues to provide feedback until the arm is correctly aligned, this type of feedback method is likely to work regardless of small changes to the robot’s shape.

The other technologies the researchers are looking into for their softbot include arrays of flexible, tactile sensors to place on the softbot’s skin, and minimizing the complex cross-talk between these arrays to get coherent information about the robot’s environment. As with some of the new proprioception research, the project is looking into neural networks as a means of modeling the complicated dynamics—the motion and response to forces—of the softbot. This method relies on large amounts of observational data, mapping how the robot is inflated and how it moves, rather than explicitly understanding and solving the equations that govern its motion—which hopefully means the methods can work even as the robot changes.

There’s still a long way to go before soft and inflatable robots can be controlled sufficiently well to perform all the tasks they might be used for. Ultimately, no one robotic design is likely to be perfect for any situation.

Nevertheless, research like this gives us hope that one day, inflatable robots could be useful tools, or even companions, at which point the advertising slogans write themselves: Don’t let them down, and they won’t let you down!

Image Credit: Brigham Young University. Continue reading

Posted in Human Robots

#434643 Sensors and Machine Learning Are Giving ...

According to some scientists, humans really do have a sixth sense. There’s nothing supernatural about it: the sense of proprioception tells you about the relative positions of your limbs and the rest of your body. Close your eyes, block out all sound, and you can still use this internal “map” of your external body to locate your muscles and body parts – you have an innate sense of the distances between them, and the perception of how they’re moving, above and beyond your sense of touch.

This sense is invaluable for allowing us to coordinate our movements. In humans, the brain integrates senses including touch, heat, and the tension in muscle spindles to allow us to build up this map.

Replicating this complex sense has posed a great challenge for roboticists. We can imagine simulating the sense of sight with cameras, sound with microphones, or touch with pressure-pads. Robots with chemical sensors could be far more accurate than us in smell and taste, but building in proprioception, the robot’s sense of itself and its body, is far more difficult, and is a large part of why humanoid robots are so tricky to get right.

Simultaneous localization and mapping (SLAM) software allows robots to use their own senses to build up a picture of their surroundings and environment, but they’d need a keen sense of the position of their own bodies to interact with it. If something unexpected happens, or in dark environments where primary senses are not available, robots can struggle to keep track of their own position and orientation. For human-robot interaction, wearable robotics, and delicate applications like surgery, tiny differences can be extremely important.

Piecemeal Solutions
In the case of hard robotics, this is generally solved by using a series of strain and pressure sensors in each joint, which allow the robot to determine how its limbs are positioned. That works fine for rigid robots with a limited number of joints, but for softer, more flexible robots, this information is limited. Roboticists are faced with a dilemma: a vast, complex array of sensors for every degree of freedom in the robot’s movement, or limited skill in proprioception?

New techniques, often involving new arrays of sensory material and machine-learning algorithms to fill in the gaps, are starting to tackle this problem. Take the work of Thomas George Thuruthel and colleagues in Pisa and San Diego, who draw inspiration from the proprioception of humans. In a new paper in Science Robotics, they describe the use of soft sensors distributed through a robotic finger at random. This placement is much like the constant adaptation of sensors in humans and animals, rather than relying on feedback from a limited number of positions.

The sensors allow the soft robot to react to touch and pressure in many different locations, forming a map of itself as it contorts into complicated positions. The machine-learning algorithm serves to interpret the signals from the randomly-distributed sensors: as the finger moves around, it’s observed by a motion capture system. After training the robot’s neural network, it can associate the feedback from the sensors with the position of the finger detected in the motion-capture system, which can then be discarded. The robot observes its own motions to understand the shapes that its soft body can take, and translate them into the language of these soft sensors.

“The advantages of our approach are the ability to predict complex motions and forces that the soft robot experiences (which is difficult with traditional methods) and the fact that it can be applied to multiple types of actuators and sensors,” said Michael Tolley of the University of California San Diego. “Our method also includes redundant sensors, which improves the overall robustness of our predictions.”

The use of machine learning lets the roboticists come up with a reliable model for this complex, non-linear system of motions for the actuators, something difficult to do by directly calculating the expected motion of the soft-bot. It also resembles the human system of proprioception, built on redundant sensors that change and shift in position as we age.

In Search of a Perfect Arm
Another approach to training robots in using their bodies comes from Robert Kwiatkowski and Hod Lipson of Columbia University in New York. In their paper “Task-agnostic self-modeling machines,” also recently published in Science Robotics, they describe a new type of robotic arm.

Robotic arms and hands are getting increasingly dexterous, but training them to grasp a large array of objects and perform many different tasks can be an arduous process. It’s also an extremely valuable skill to get right: Amazon is highly interested in the perfect robot arm. Google hooked together an array of over a dozen robot arms so that they could share information about grasping new objects, in part to cut down on training time.

Individually training a robot arm to perform every individual task takes time and reduces the adaptability of your robot: either you need an ML algorithm with a huge dataset of experiences, or, even worse, you need to hard-code thousands of different motions. Kwiatkowski and Lipson attempt to overcome this by developing a robotic system that has a “strong sense of self”: a model of its own size, shape, and motions.

They do this using deep machine learning. The robot begins with no prior knowledge of its own shape or the underlying physics of its motion. It then repeats a series of a thousand random trajectories, recording the motion of its arm. Kwiatkowski and Lipson compare this to a baby in the first year of life observing the motions of its own hands and limbs, fascinated by picking up and manipulating objects.

Again, once the robot has trained itself to interpret these signals and build up a robust model of its own body, it’s ready for the next stage. Using that deep-learning algorithm, the researchers then ask the robot to design strategies to accomplish simple pick-up and place and handwriting tasks. Rather than laboriously and narrowly training itself for each individual task, limiting its abilities to a very narrow set of circumstances, the robot can now strategize how to use its arm for a much wider range of situations, with no additional task-specific training.

Damage Control
In a further experiment, the researchers replaced part of the arm with a “deformed” component, intended to simulate what might happen if the robot was damaged. The robot can then detect that something’s up and “reconfigure” itself, reconstructing its self-model by going through the training exercises once again; it was then able to perform the same tasks with only a small reduction in accuracy.

Machine learning techniques are opening up the field of robotics in ways we’ve never seen before. Combining them with our understanding of how humans and other animals are able to sense and interact with the world around us is bringing robotics closer and closer to becoming truly flexible and adaptable, and, eventually, omnipresent.

But before they can get out and shape the world, as these studies show, they will need to understand themselves.

Image Credit: jumbojan / Shutterstock.com Continue reading

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#433634 This Robotic Skin Makes Inanimate ...

In Goethe’s poem “The Sorcerer’s Apprentice,” made world-famous by its adaptation in Disney’s Fantasia, a lazy apprentice, left to fetch water, uses magic to bewitch a broom into performing his chores for him. Now, new research from Yale has opened up the possibility of being able to animate—and automate—household objects by fitting them with a robotic skin.

Yale’s Soft Robotics lab, the Faboratory, is led by Professor Rebecca Kramer-Bottiglio, and has long investigated the possibilities associated with new kinds of manufacturing. While the typical image of a robot is hard, cold steel and rigid movements, soft robotics aims to create something more flexible and versatile. After all, the human body is made up of soft, flexible surfaces, and the world is designed for us. Soft, deformable robots could change shape to adapt to different tasks.

When designing a robot, key components are the robot’s sensors, which allow it to perceive its environment, and its actuators, the electrical or pneumatic motors that allow the robot to move and interact with its environment.

Consider your hand, which has temperature and pressure sensors, but also muscles as actuators. The omni-skins, as the Science Robotics paper dubs them, combine sensors and actuators, embedding them into an elastic sheet. The robotic skins are moved by pneumatic actuators or memory alloy that can bounce back into shape. If this is then wrapped around a soft, deformable object, moving the skin with the actuators can allow the object to crawl along a surface.

The key to the design here is flexibility: rather than adding chips, sensors, and motors into every household object to turn them into individual automatons, the same skin can be used for many purposes. “We can take the skins and wrap them around one object to perform a task—locomotion, for example—and then take them off and put them on a different object to perform a different task, such as grasping and moving an object,” said Kramer-Bottiglio. “We can then take those same skins off that object and put them on a shirt to make an active wearable device.”

The task is then to dream up applications for the omni-skins. Initially, you might imagine demanding a stuffed toy to fetch the remote control for you, or animating a sponge to wipe down kitchen surfaces—but this is just the beginning. The scientists attached the skins to a soft tube and camera, creating a worm-like robot that could compress itself and crawl into small spaces for rescue missions. The same skins could then be worn by a person to sense their posture. One could easily imagine this being adapted into a soft exoskeleton for medical or industrial purposes: for example, helping with rehabilitation after an accident or injury.

The initial motivating factor for creating the robots was in an environment where space and weight are at a premium, and humans are forced to improvise with whatever’s at hand: outer space. Kramer-Bottoglio originally began the work after NASA called out for soft robotics systems for use by astronauts. Instead of wasting valuable rocket payload by sending up a heavy metal droid like ATLAS to fetch items or perform repairs, soft robotic skins with modular sensors could be adapted for a range of different uses spontaneously.

By reassembling components in the soft robotic skin, a crumpled ball of paper could provide the chassis for a robot that performs repairs on the spaceship, or explores the lunar surface. The dynamic compression provided by the robotic skin could be used for g-suits to protect astronauts when they rapidly accelerate or decelerate.

“One of the main things I considered was the importance of multi-functionality, especially for deep space exploration where the environment is unpredictable. The question is: How do you prepare for the unknown unknowns? … Given the design-on-the-fly nature of this approach, it’s unlikely that a robot created using robotic skins will perform any one task optimally,” Kramer-Bottiglio said. “However, the goal is not optimization, but rather diversity of applications.”

There are still problems to resolve. Many of the videos of the skins indicate that they can rely on an external power supply. Creating new, smaller batteries that can power wearable devices has been a focus of cutting-edge materials science research for some time. Much of the lab’s expertise is in creating flexible, stretchable electronics that can be deformed by the actuators without breaking the circuitry. In the future, the team hopes to work on streamlining the production process; if the components could be 3D printed, then the skins could be created when needed.

In addition, robotic hardware that’s capable of performing an impressive range of precise motions is quite an advanced technology. The software to control those robots, and enable them to perform a variety of tasks, is quite another challenge. With soft robots, it can become even more complex to design that control software, because the body itself can change shape and deform as the robot moves. The same set of programmed motions, then, can produce different results depending on the environment.

“Let’s say I have a soft robot with four legs that crawls along the ground, and I make it walk up a hard slope,” Dr. David Howard, who works on robotics at CSIRO in Australia, explained to ABC.

“If I make that slope out of gravel and I give it the same control commands, the actual body is going to deform in a different way, and I’m not necessarily going to know what that is.”

Despite these and other challenges, research like that at the Faboratory still hopes to redefine how we think of robots and robotics. Instead of a robot that imitates a human and manipulates objects, the objects themselves will become programmable matter, capable of moving autonomously and carrying out a range of tasks. Futurists speculate about a world where most objects are automated to some degree and can assemble and repair themselves, or are even built entirely of tiny robots.

The tale of the Sorcerer’s Apprentice was first written in 1797, at the dawn of the industrial revolution, over a century before the word “robot” was even coined. Yet more and more roboticists aim to prove Arthur C Clarke’s maxim: any sufficiently advanced technology is indistinguishable from magic.

Image Credit: Joran Booth, The Faboratory Continue reading

Posted in Human Robots

#431862 Want Self-Healing Robots and Tires? ...

We all have scars, and each one tells a story. Tales of tomfoolery, tales of haphazardness, or in my case, tales of stupidity.
Whether the cause of your scar was a push-bike accident, a lack of concentration while cutting onions, or simply the byproduct of an active lifestyle, the experience was likely extremely painful and distressing. Not to mention the long and vexatious recovery period, stretching out for weeks and months after the actual event!
Cast your minds back to that time. How you longed for instant relief from your discomfort! How you longed to have your capabilities restored in an instant!
Well, materials that can heal themselves in an instant may not be far from becoming a reality—and a family of them known as elastomers holds the key.
“Elastomer” is essentially a big, fancy word for rubber. However, elastomers have one unique property—they are capable of returning to their original form after being vigorously stretched and deformed.
This unique property of elastomers has caught the eye of many scientists around the world, particularly those working in the field of robotics. The reason? Elastomer can be encouraged to return to its original shape, in many cases by simply applying heat. The implication of this is the quick and cost-effective repair of “wounds”—cuts, tears, and punctures to the soft, elastomer-based appendages of a robot’s exoskeleton.

Researchers from Vrije University in Brussels, Belgium have been toying with the technique, and with remarkable success. The team built a robotic hand with fingers made of a type of elastomer. They found that cuts and punctures were indeed able to repair themselves simply by applying heat to the affected area.
How long does the healing process take? In this instance, about a day. Now that’s a lot shorter than the weeks and months of recovery time we typically need for a flesh wound, during which we are unable to write, play the guitar, or do the dishes. If you consider the latter to be a bad thing…
However, it’s not the first time scientists have played around with elastomers and examined their self-healing properties. Another team of scientists, headed up by Cheng-Hui Li and Chao Wang, discovered another type of elastomer that exhibited autonomous self-healing properties. Just to help you picture this stuff, the material closely resembles animal muscle— strong, flexible, and elastic. With autogenetic restorative powers to boot.
Advancements in the world of self-healing elastomers, or rubbers, may also affect the lives of everyday motorists. Researchers from the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) have developed a self-healing rubber material that could be used to make tires that repair their own punctures.
This time the mechanism of self-healing doesn’t involve heat. Rather, it is related to a physical phenomenon associated with the rubber’s unique structure. Normally, when a large enough stress is applied to a typical rubber, there is catastrophic failure at the focal point of that stress. The self-healing rubber the researchers created, on the other hand, distributes that same stress evenly over a network of “crazes”—which are like cracks connected by strands of fiber.
Here’s the interesting part. Not only does this unique physical characteristic of the rubber prevent catastrophic failure, it facilitates self-repair. According to Harvard researchers, when the stress is released, the material snaps back to its original form and the crazes heal.
This wonder material could be used in any number of rubber-based products.
Professor Jinrong Wu, of Sichuan University, China, and co-author of the study, happened to single out tires: “Imagine that we could use this material as one of the components to make a rubber tire… If you have a cut through the tire, this tire wouldn’t have to be replaced right away. Instead, it would self-heal while driving, enough to give you leeway to avoid dramatic damage,” said Wu.
So where to from here? Well, self-healing elastomers could have a number of different applications. According to the article published by Quartz, cited earlier, the material could be used on artificial limbs. Perhaps it will provide some measure of structural integrity without looking like a tattered mess after years of regular use.
Or perhaps a sort of elastomer-based hybrid skin is on the horizon. A skin in which wounds heal instantly. And recovery time, unlike your regular old human skin of yesteryear, is significantly slashed. Furthermore, this future skin might eliminate those little reminders we call scars.
For those with poor judgment skills, this spells an end to disquieting reminders of our own stupidity.
Image Credit: Vrije Universiteit Brussel / Prof. Dr. ir. Bram Vanderborght Continue reading

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