Tag Archives: can
#435593 AI at the Speed of Light
Neural networks shine for solving tough problems such as facial and voice recognition, but conventional electronic versions are limited in speed and hungry for power. In theory, optics could beat digital electronic computers in the matrix calculations used in neural networks. However, optics had been limited by their inability to do some complex calculations that had required electronics. Now new experiments show that all-optical neural networks can tackle those problems.
The key attraction of neural networks is their massive interconnections among processors, comparable to the complex interconnections among neurons in the brain. This lets them perform many operations simultaneously, like the human brain does when looking at faces or listening to speech, making them more efficient for facial and voice recognition than traditional electronic computers that execute one instruction at a time.
Today's electronic neural networks have reached eight million neurons, but their future use in artificial intelligence may be limited by their high power usage and limited parallelism in connections. Optical connections through lenses are inherently parallel. The lens in your eye simultaneously focuses light from across your field of view onto the retina in the back of your eye, where an array of light-detecting nerve cells detects the light. Each cell then relays the signal it receives to neurons in the brain that process the visual signals to show us an image.
Glass lenses process optical signals by focusing light, which performs a complex mathematical operation called a Fourier transform that preserves the information in the original scene but rearranges is completely. One use of Fourier transforms is converting time variations in signal intensity into a plot of the frequencies present in the signal. The military used this trick in the 1950s to convert raw radar return signals recorded by an aircraft in flight into a three-dimensional image of the landscape viewed by the plane. Today that conversion is done electronically, but the vacuum-tube computers of the 1950s were not up to the task.
Development of neural networks for artificial intelligence started with electronics, but their AI applications have been limited by their slow processing and need for extensive computing resources. Some researchers have developed hybrid neural networks, in which optics perform simple linear operations, but electronics perform more complex nonlinear calculations. Now two groups have demonstrated simple all-optical neural networks that do all processing with light.
In May, Wolfram Pernice of the Institute of Physics at the University of Münster in Germany and colleagues reported testing an all-optical “neuron” in which signals change target materials between liquid and solid states, an effect that has been used for optical data storage. They demonstrated nonlinear processing, and produced output pulses like those from organic neurons. They then produced an integrated photonic circuit that incorporated four optical neurons operating at different wavelengths, each of which connected to 15 optical synapses. The photonic circuit contained more than 140 components and could recognize simple optical patterns. The group wrote that their device is scalable, and that the technology promises “access to the high speed and high bandwidth inherent to optical systems, thus enabling the direct processing of optical telecommunication and visual data.”
Now a group at the Hong Kong University of Science and Technology reports in Optica that they have made an all-optical neural network based on a different process, electromagnetically induced transparency, in which incident light affects how atoms shift between quantum-mechanical energy levels. The process is nonlinear and can be triggered by very weak light signals, says Shengwang Du, a physics professor and coauthor of the paper.
In their demonstration, they illuminated rubidium-85 atoms cooled by lasers to about 10 microKelvin (10 microdegrees above absolute zero). Although the technique may seem unusually complex, Du said the system was the most accessible one in the lab that could produce the desired effects. “As a pure quantum atomic system [it] is ideal for this proof-of-principle experiment,” he says.
Next, they plan to scale up the demonstration using a hot atomic vapor center, which is less expensive, does not require time-consuming preparation of cold atoms, and can be integrated with photonic chips. Du says the major challenges are reducing cost of the nonlinear processing medium and increasing the scale of the all-optical neural network for more complex tasks.
“Their demonstration seems valid,” says Volker Sorger, an electrical engineer at George Washington University in Washington who was not involved in either demonstration. He says the all-optical approach is attractive because it offers very high parallelism, but the update rate is limited to about 100 hertz because of the liquid crystals used in their test, and he is not completely convinced their approach can be scaled error-free. Continue reading →
Posted in Human Robots
#435591 Video Friday: This Robotic Thread Could ...
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!):
IEEE Africon 2019 – September 25-27, 2019 – Accra, Ghana
ISRR 2019 – October 6-10, 2019 – Hanoi, Vietnam
Ro-Man 2019 – October 14-18, 2019 – New Delhi, India
Humanoids 2019 – October 15-17, 2019 – Toronto, Canada
ARSO 2019 – October 31-1, 2019 – Beijing, China
ROSCon 2019 – October 31-1, 2019 – Macau
IROS 2019 – November 4-8, 2019 – Macau
Let us know if you have suggestions for next week, and enjoy today’s videos.
Eight engineering students from ETH Zurich are working on a year-long focus project to develop a multimodal robot called Dipper, which can fly, swim, dive underwater, and manage that difficult air-water transition:
The robot uses one motor to selectively drive either a propeller or a marine screw depending on whether it’s in flight or not. We’re told that getting the robot to autonomously do the water to air transition is still a work in progress, but that within a few weeks things should be much smoother.
[ Dipper ]
Thanks Simon!
Giving a jellyfish a hug without stressing them out is exactly as hard as you think, but Harvard’s robot will make sure that all jellyfish get the emotional (and physical) support that they need.
The gripper’s six “fingers” are composed of thin, flat strips of silicone with a hollow channel inside bonded to a layer of flexible but stiffer polymer nanofibers. The fingers are attached to a rectangular, 3D-printed plastic “palm” and, when their channels are filled with water, curl in the direction of the nanofiber-coated side. Each finger exerts an extremely low amount of pressure — about 0.0455 kPA, or less than one-tenth of the pressure of a human’s eyelid on their eye. By contrast, current state-of-the-art soft marine grippers, which are used to capture delicate but more robust animals than jellyfish, exert about 1 kPA.
The gripper was successfully able to trap each jellyfish against the palm of the device, and the jellyfish were unable to break free from the fingers’ grasp until the gripper was depressurized. The jellyfish showed no signs of stress or other adverse effects after being released, and the fingers were able to open and close roughly 100 times before showing signs of wear and tear.
[ Harvard ]
MIT engineers have developed a magnetically steerable, thread-like robot that can actively glide through narrow, winding pathways, such as the labyrinthine vasculature of the brain. In the future, this robotic thread may be paired with existing endovascular technologies, enabling doctors to remotely guide the robot through a patient’s brain vessels to quickly treat blockages and lesions, such as those that occur in aneurysms and stroke.
[ MIT ]
See NASA’s next Mars rover quite literally coming together inside a clean room at the Jet Propulsion Laboratory. This behind-the-scenes look at what goes into building and preparing a rover for Mars, including extensive tests in simulated space environments, was captured from March to July 2019. The rover is expected to launch to the Red Planet in summer 2020 and touch down in February 2021.
The Mars 2020 rover doesn’t have a name yet, but you can give it one! As long as you’re not too old! Which you probably are!
[ Mars 2020 ]
I desperately wish that we could watch this next video at normal speed, not just slowed down, but it’s quite impressive anyway.
Here’s one more video from the Namiki Lab showing some high speed tracking with a pair of very enthusiastic robotic cameras:
[ Namiki Lab ]
Normally, tedious modeling of mechanics, electronics, and information science is required to understand how insects’ or robots’ moving parts coordinate smoothly to take them places. But in a new study, biomechanics researchers at the Georgia Institute of Technology boiled down the sprints of cockroaches to handy principles and equations they then used to make a test robot amble about better.
[ Georgia Tech ]
More magical obstacle-dodging footage from Skydio’s still secret new drone.
We’ve been hard at work extending the capabilities of our upcoming drone, giving you ways to get the control you want without the stress of crashing. The result is you can fly in ways, and get shots, that would simply be impossible any other way. How about flying through obstacles at full speed, backwards?
[ Skydio ]
This is a cute demo with Misty:
[ Misty Robotics ]
We’ve seen pieces of hardware like this before, but always made out of hard materials—a soft version is certainly something new.
Utilizing vacuum power and soft material actuators, we have developed a soft reconfigurable surface (SRS) with multi-modal control and performance capabilities. The SRS is comprised of a square grid array of linear vacuum-powered soft pneumatic actuators (linear V-SPAs), built into plug-and-play modules which enable the arrangement, consolidation, and control of many DoF.
[ RRL ]
The EksoVest is not really a robot, but it’ll make you a cyborg! With super strength!
“This is NOT intended to give you super strength but instead give you super endurance and reduce fatigue so that you have more energy and less soreness at the end of your shift.”
Drat!
[ EksoVest ]
We have created a solution for parents, grandparents, and their children who are living separated. This is an amazing tool to stay connected from a distance through the intimacy that comes through interactive play with a child. For parents who travel for work, deployed military, and families spread across the country, the Cushybot One is much more than a toy; it is the opportunity for maintaining a deep connection with your young child from a distance.
Hmm.
I think the concept here is great, but it’s going to be a serious challenge to successfully commercialize.
[ Indiegogo ]
What happens when you equip RVR with a parachute and send it off a cliff? Watch this episode of RVR Launchpad to find out – then go Behind the Build to see how we (eventually) accomplished this high-flying feat.
[ Sphero ]
These omnidirectional crawler robots aren’t new, but that doesn’t keep them from being fun to watch.
[ NEDO ] via [ Impress ]
We’ll finish up the week with a couple of past ICRA and IROS keynote talks—one by Gill Pratt on The Reliability Challenges of Autonomous Driving, and the other from Peter Hart, on Making Shakey.
[ IEEE RAS ] Continue reading →
Posted in Human Robots
#435589 Construction Robots Learn to Excavate by ...
Pavel Savkin remembers the first time he watched a robot imitate his movements. Minutes earlier, the engineer had finished “showing” the robotic excavator its new goal by directing its movements manually. Now, running on software Savkin helped design, the robot was reproducing his movements, gesture for gesture. “It was like there was something alive in there—but I knew it was me,” he said.
Savkin is the CTO of SE4, a robotics software project that styles itself the “driver” of a fleet of robots that will eventually build human colonies in space. For now, SE4 is focused on creating software that can help developers communicate with robots, rather than on building hardware of its own.
The Tokyo-based startup showed off an industrial arm from Universal Robots that was running SE4’s proprietary software at SIGGRAPH in July. SE4’s demonstration at the Los Angeles innovation conference drew the company’s largest audience yet. The robot, nicknamed Squeezie, stacked real blocks as directed by SE4 research engineer Nathan Quinn, who wore a VR headset and used handheld controls to “show” Squeezie what to do.
As Quinn manipulated blocks in a virtual 3D space, the software learned a set of ordered instructions to be carried out in the real world. That order is essential for remote operations, says Quinn. To build remotely, developers need a way to communicate instructions to robotic builders on location. In the age of digital construction and industrial robotics, giving a computer a blueprint for what to build is a well-explored art. But operating on a distant object—especially under conditions that humans haven’t experienced themselves—presents challenges that only real-time communication with operators can solve.
The problem is that, in an unpredictable setting, even simple tasks require not only instruction from an operator, but constant feedback from the changing environment. Five years ago, the Swedish fiber network provider umea.net (part of the private Umeå Energy utility) took advantage of the virtual reality boom to promote its high-speed connections with the help of a viral video titled “Living with Lag: An Oculus Rift Experiment.” The video is still circulated in VR and gaming circles.
In the experiment, volunteers donned headgear that replaced their real-time biological senses of sight and sound with camera and audio feeds of their surroundings—both set at a 3-second delay. Thus equipped, volunteers attempt to complete everyday tasks like playing ping-pong, dancing, cooking, and walking on a beach, with decidedly slapstick results.
At outer-orbit intervals, including SE4’s dream of construction projects on Mars, the limiting factor in communication speed is not an artificial delay, but the laws of physics. The shifting relative positions of Earth and Mars mean that communications between the planets—even at the speed of light—can take anywhere from 3 to 22 minutes.
A long-distance relationship
Imagine trying to manage a construction project from across an ocean without the benefit of intelligent workers: sending a ship to an unknown world with a construction crew and blueprints for a log cabin, and four months later receiving a letter back asking how to cut down a tree. The parallel problem in long-distance construction with robots, according to SE4 CEO Lochlainn Wilson, is that automation relies on predictability. “Every robot in an industrial setting today is expecting a controlled environment.”
Platforms for applying AR and VR systems to teach tasks to artificial intelligences, as SE4 does, are already proliferating in manufacturing, healthcare, and defense. But all of the related communications systems are bound by physics and, specifically, the speed of light.
The same fundamental limitation applies in space. “Our communications are light-based, whether they’re radio or optical,” says Laura Seward Forczyk, a planetary scientist and consultant for space startups. “If you’re going to Mars and you want to communicate with your robot or spacecraft there, you need to have it act semi- or mostly-independently so that it can operate without commands from Earth.”
Semantic control
That’s exactly what SE4 aims to do. By teaching robots to group micro-movements into logical units—like all the steps to building a tower of blocks—the Tokyo-based startup lets robots make simple relational judgments that would allow them to receive a full set of instruction modules at once and carry them out in order. This sidesteps the latency issue in real-time bilateral communications that could hamstring a project or at least make progress excruciatingly slow.
The key to the platform, says Wilson, is the team’s proprietary operating software, “Semantic Control.” Just as in linguistics and philosophy, “semantics” refers to meaning itself, and meaning is the key to a robot’s ability to make even the smallest decisions on its own. “A robot can scan its environment and give [raw data] to us, but it can’t necessarily identify the objects around it and what they mean,” says Wilson.
That’s where human intelligence comes in. As part of the demonstration phase, the human operator of an SE4-controlled machine “annotates” each object in the robot’s vicinity with meaning. By labeling objects in the VR space with useful information—like which objects are building material and which are rocks—the operator helps the robot make sense of its real 3D environment before the building begins.
Giving robots the tools to deal with a changing environment is an important step toward allowing the AI to be truly independent, but it’s only an initial step. “We’re not letting it do absolutely everything,” said Quinn. “Our robot is good at moving an object from point A to point B, but it doesn’t know the overall plan.” Wilson adds that delegating environmental awareness and raw mechanical power to separate agents is the optimal relationship for a mixed human-robot construction team; it “lets humans do what they’re good at, while robots do what they do best.”
This story was updated on 4 September 2019. Continue reading →
Posted in Human Robots
#435583 Soft Self-Healing Materials for Robots ...
If there’s one thing we know about robots, it’s that they break. They break, like, literally all the time. The software breaks. The hardware breaks. The bits that you think could never, ever, ever possibly break end up breaking just when you need them not to break the most, and then you have to try to explain what happened to your advisor who’s been standing there watching your robot fail and then stay up all night fixing the thing that seriously was not supposed to break.
While most of this is just a fundamental characteristic of robots that can’t be helped, the European Commission is funding a project called SHERO (Self HEaling soft RObotics) to try and solve at least some of those physical robot breaking problems through the use of structural materials that can autonomously heal themselves over and over again.
SHERO is a three year, €3 million collaboration between Vrije Universiteit Brussel, University of Cambridge, École Supérieure de Physique et de Chimie Industrielles de la ville de Paris (ESPCI-Paris), and Swiss Federal Laboratories for Materials Science and Technology (Empa). As the name SHERO suggests, the goal of the project is to develop soft materials that can completely recover from the kinds of damage that robots are likely to suffer in day to day operations, as well as the occasional more extreme accident.
Most materials, especially soft materials, are fixable somehow, whether it’s with super glue or duct tape. But fixing things involves a human first identifying when they’re broken, and then performing a potentially skill, labor, time, and money intensive task. SHERO’s soft materials will, eventually, make this entire process autonomous, allowing robots to self-identify damage and initiate healing on their own.
Photos: SHERO Project
The damaged robot finger [top] can operate normally after healing itself.
How the self-healing material works
What these self-healing materials can do is really pretty amazing. The researchers are actually developing two different types—the first one heals itself when there’s an application of heat, either internally or externally, which gives some control over when and how the healing process starts. For example, if the robot is handling stuff that’s dirty, you’d want to get it cleaned up before healing it so that dirt doesn’t become embedded in the material. This could mean that the robot either takes itself to a heating station, or it could activate some kind of embedded heating mechanism to be more self-sufficient.
The second kind of self-healing material is autonomous, in that it will heal itself at room temperature without any additional input, and is probably more suitable for relatively minor scrapes and cracks. Here are some numbers about how well the healing works:
Autonomous self-healing polymers do not require heat. They can heal damage at room temperature. Developing soft robotic systems from autonomous self-healing polymers excludes the need of additional heating devices… The healing however takes some time. The healing efficiency after 3 days, 7 days and 14 days is respectively 62 percent, 91 percent and 97 percent.
This material was used to develop a healable soft pneumatic hand. Relevant large cuts can be healed entirely without the need of external heat stimulus. Depending on the size of the damage and even more on the location of damage, the healing takes only seconds or up to a week. Damage on locations on the actuator that are subjected to very small stresses during actuation was healed instantaneously. Larger damages, like cutting the actuator completely in half, took 7 days to heal. But even this severe damage could be healed completely without the need of any external stimulus.
Applications of self-healing robots
Both of these materials can be mixed together, and their mechanical properties can be customized so that the structure that they’re a part of can be tuned to move in different ways. The researchers also plan on introducing flexible conductive sensors into the material, which will help sense damage as well as providing position feedback for control systems. A lot of development will happen over the next few years, and for more details, we spoke with Bram Vanderborght at Vrije Universiteit in Brussels.
IEEE Spectrum: How easy or difficult or expensive is it to produce these materials? Will they add significant cost to robotic grippers?
Bram Vanderborght: They are definitely more expensive materials, but it’s also a matter of size of production. At the moment, we’ve made a few kilograms of the material (enough to make several demonstrators), and the price already dropped significantly from when we ordered 100 grams of the material in the first phase of the project. So probably the cost of the gripper will be higher [than a regular gripper], but you won’t need to replace the gripper as often as other grippers that need to be replaced due to wear, so it can be an advantage.
Moreover due to the method of 3D printing the material, the surface is smoother and airtight (so no post-processing is required to make it airtight). Also, the smooth surface is better to avoid contamination for food handling, for example.
In commercial or industrial applications, gradual fatigue seems to be a more common issue than more abrupt trauma like cuts. How well does the self-healing work to improve durability over long periods of time?
We did not test for gradual fatigue over very long times. But both macroscopic and microscopic damage can be healed. So hopefully it can provide an answer here as well.
Image: SHERO Project
After developing a self-healing robot gripper, the researchers plan to use similar materials to build parts that can be used as the skeleton of robots, allowing them to repair themselves on a regular basis.
How much does the self-healing capability restrict the material properties? What are the limits for softness or hardness or smoothness or other characteristics of the material?
Typically the mechanical properties of networked polymers are much better than thermoplastics. Our material is a networked polymer but in which the crosslinks are reversible. We can change quite a lot of parameters in the design of the materials. So we can develop very stiff (fracture strain at 1.24 percent) and very elastic materials (fracture strain at 450 percent). The big advantage that our material has is we can mix it to have intermediate properties. Moreover, at the interface of the materials with different mechanical properties, we have the same chemical bonds, so the interface is perfect. While other materials, they may need to glue it, which gives local stresses and a weak spot.
When the material heals itself, is it less structurally sound in that spot? Can it heal damage that happens to the same spot over and over again?
In theory we can heal it an infinite amount of times. When the wound is not perfectly aligned, of course in that spot it will become weaker. Also too high temperatures lead to irreversible bonds, and impurities lead to weak spots.
Besides grippers and skins, what other potential robotics applications would this technology be useful for?
Most of self healing materials available now are used for coatings. What we are developing are structural components, therefore the mechanical properties of the material need to be good for such applications. So maybe part of the skeleton of the robot can be developed with such materials to make it lighter, since can be designed for regular repair. And for exceptional loads, it breaks and can be repaired like our human body.
[ SHERO Project ] Continue reading →
Posted in Human Robots
#435579 RoMeLa’s Newest Robot Is a ...
A few years ago, we wrote about NABiRoS, a bipedal robot from Dennis Hong’s Robotics & Mechanisms Laboratory (RoMeLa) at UCLA. Unlike pretty much any other biped we’d ever seen, NABiRoS had a unique kinematic configuration that had it using its two legs to walk sideways, which offered some surprising advantages.
As it turns out, bipeds aren’t the only robots that can potentially benefit from a bit of a kinematic rethink. RoMeLa has redesigned quadrupedal robots too—rather than model them after a quadrupedal animal like a dog or a horse, RoMeLa’s ALPHRED robots use four legs arranged symmetrically around the body of the robot, allowing it to walk, run, hop, and jump, as well as manipulate and carry objects, karate chop through boards, and even roller skate on its butt. This robot can do it all.
Impressive, right? This is ALPHRED 2, and its predecessor, the original ALPHRED, was introduced at IROS 2018. Both ALPHREDs are axisymmetric about the vertical axis, meaning that they don’t have a front or a back and are perfectly happy to walk in any direction you like. Traditional quadrupeds like Spot or Laikago can also move sideways and backwards, but their leg arrangement makes them more efficient at moving in one particular direction, and also results in some curious compromises like a preference for going down stairs backwards. ANYmal is a bit more flexible in that it can reverse its knees, but it’s still got that traditional quadrupedal two-by-two configuration.
ALPHRED 2’s four symmetrical limbs can be used for a whole bunch of stuff. It can do quadrupedal walking and running, and it’s able to reach stable speeds of up to 1.5 m/s. If you want bipedal walking, it can do that NABiRoS-style, although it’s still a bit fragile at the moment. Using two legs for walking leaves two legs free, and those legs can turn into arms. A tripedal compromise configuration, with three legs and one arm, is more stable and allows the robot to do things like push buttons, open doors, and destroy property. And thanks to passive wheels under its body, ALPHRED 2 can use its limbs to quickly and efficiently skate around:
The impressive performance of the robot comes courtesy of a custom actuator that RoMeLa designed specifically for dynamic legged locomotion. They call it BEAR, or Back-Drivable Electromechanical Actuator for Robots. These are optionally liquid-cooled motors capable of proprioceptive sensing, consisting of a DC motor, a single stage 10:1 planetary gearbox, and channels through the back of the housing that coolant can be pumped through. The actuators have a peak torque of 32 Nm, and a continuous torque of about 8 Nm with passive air cooling. With liquid cooling, the continuous torque jumps to about 21 Nm. And in the videos above, ALPHRED 2 isn’t even running the liquid cooling system, suggesting that it’s capable of much higher sustained performance.
Photo: RoMeLa
Using two legs for walking leaves two legs free, and those legs can turn into arms.
RoMeLa has produced a bunch of very creative robots, and we appreciate that they also seem to produce a bunch of very creative demos showing why their unusual approaches are in fact (at least in some specific cases) somewhat practical. With the recent interest in highly dynamic robots that can be reliably useful in environments infested with humans, we can’t wait to see what kinds of exciting tricks the next (presumably liquid-cooled) version will be able to do.
[ RoMeLa ] Continue reading →
Posted in Human Robots