Tag Archives: art

#435632 DARPA Subterranean Challenge: Tunnel ...

The Tunnel Circuit of the DARPA Subterranean Challenge starts later this week at the NIOSH research mine just outside of Pittsburgh, Pennsylvania. From 15-22 August, 11 teams will send robots into a mine that they've never seen before, with the goal of making maps and locating items. All DARPA SubT events involve tunnels of one sort or another, but in this case, the “Tunnel Circuit” refers to mines as opposed to urban underground areas or natural caves. This month’s challenge is the first of three discrete events leading up to a huge final event in August of 2021.

While the Tunnel Circuit competition will be closed to the public, and media are only allowed access for a single day (which we'll be at, of course), DARPA has provided a substantial amount of information about what teams will be able to expect. We also have details from the SubT Integration Exercise, called STIX, which was a completely closed event that took place back in April. STIX was aimed at giving some teams (and DARPA) a chance to practice in a real tunnel environment.

For more general background on SubT, here are some articles to get you all caught up:

SubT: The Next DARPA Challenge for Robotics

Q&A with DARPA Program Manager Tim Chung

Meet The First Nine Teams

It makes sense to take a closer look at what happened at April's STIX exercise, because it is (probably) very similar to what teams will experience in the upcoming Tunnel Circuit. STIX took place at Edgar Experimental Mine in Colorado, and while no two mines are the same (and many are very, very different), there are enough similarities for STIX to have been a valuable experience for teams. Here's an overview video of the exercise from DARPA:

DARPA has also put together a much more detailed walkthrough of the STIX mine exercise, which gives you a sense of just how vast, complicated, and (frankly) challenging for robots the mine environment is:

So, that's the kind of thing that teams had to deal with back in April. Since the event was an exercise, rather than a competition, DARPA didn't really keep score, and wouldn't comment on the performance of individual teams. We've been trolling YouTube for STIX footage, though, to get a sense of how things went, and we found a few interesting videos.

Here's a nice overview from Team CERBERUS, which used drones plus an ANYmal quadruped:

Team CTU-CRAS also used drones, along with a tracked robot:

Team Robotika was brave enough to post video of a “fatal failure” experienced by its wheeled robot; the poor little bot gets rescued at about 7:00 in case you get worried:

So that was STIX. But what about the Tunnel Circuit competition this week? Here's a course preview video from DARPA:

It sort of looks like the NIOSH mine might be a bit less dusty than the Edgar mine was, but it could also be wetter and muddier. It’s hard to tell, because we’re just getting a few snapshots of what’s probably an enormous area with kilometers of tunnels that the robots will have to explore. But DARPA has promised “constrained passages, sharp turns, large drops/climbs, inclines, steps, ladders, and mud, sand, and/or water.” Combine that with the serious challenge to communications imposed by the mine itself, and robots will have to be both physically capable, and almost entirely autonomous. Which is, of course, exactly what DARPA is looking to test with this challenge.

Lastly, we had a chance to catch up with Tim Chung, Program Manager for the Subterranean Challenge at DARPA, and ask him a few brief questions about STIX and what we have to look forward to this week.

IEEE Spectrum: How did STIX go?

Tim Chung: It was a lot of fun! I think it gave a lot of the teams a great opportunity to really get a taste of what these types of real world environments look like, and also what DARPA has in store for them in the SubT Challenge. STIX I saw as an experiment—a learning experience for all the teams involved (as well as the DARPA team) so that we can continue our calibration.

What do you think teams took away from STIX, and what do you think DARPA took away from STIX?

I think the thing that teams took away was that, when DARPA hosts a challenge, we have very audacious visions for what the art of the possible is. And that's what we want—in my mind, the purpose of a DARPA Grand Challenge is to provide that inspiration of, ‘Holy cow, someone thinks we can do this!’ So I do think the teams walked away with a better understanding of what DARPA's vision is for the capabilities we're seeking in the SubT Challenge, and hopefully walked away with a better understanding of the technical, physical, even maybe mental challenges of doing this in the wild— which will all roll back into how they think about the problem, and how they develop their systems.

This was a collaborative exercise, so the DARPA field team was out there interacting with the other engineers, figuring out what their strengths and weaknesses and needs might be, and even understanding how to handle the robots themselves. That will help [strengthen] connections between these university teams and DARPA going forward. Across the board, I think that collaborative spirit is something we really wish to encourage, and something that the DARPA folks were able to take away.

What do we have to look forward to during the Tunnel Circuit?

The vision here is that the Tunnel Circuit is representative of one of the three subterranean subdomains, along with urban and cave. Characteristics of all of these three subdomains will be mashed together in an epic final course, so that teams will have to face hints of tunnel once again in that final event.

Without giving too much away, the NIOSH mine will be similar to the Edgar mine in that it's a human-made environment that supports mining operations and research. But of course, every site is different, and these differences, I think, will provide good opportunities for the teams to shine.

Again, we'll be visiting the NIOSH mine in Pennsylvania during the Tunnel Circuit and will post as much as we can from there. But if you’re an actual participant in the Subterranean Challenge, please tweet me @BotJunkie so that I can follow and help share live updates.

[ DARPA Subterranean Challenge ] 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

#435474 Watch China’s New Hybrid AI Chip Power ...

When I lived in Beijing back in the 90s, a man walking his bike was nothing to look at. But today, I did a serious double-take at a video of a bike walking his man.

No kidding.

The bike itself looks overloaded but otherwise completely normal. Underneath its simplicity, however, is a hybrid computer chip that combines brain-inspired circuits with machine learning processes into a computing behemoth. Thanks to its smart chip, the bike self-balances as it gingerly rolls down a paved track before smoothly gaining speed into a jogging pace while navigating dexterously around obstacles. It can even respond to simple voice commands such as “speed up,” “left,” or “straight.”

Far from a circus trick, the bike is a real-world demo of the AI community’s latest attempt at fashioning specialized hardware to keep up with the challenges of machine learning algorithms. The Tianjic (天机*) chip isn’t just your standard neuromorphic chip. Rather, it has the architecture of a brain-like chip, but can also run deep learning algorithms—a match made in heaven that basically mashes together neuro-inspired hardware and software.

The study shows that China is readily nipping at the heels of Google, Facebook, NVIDIA, and other tech behemoths investing in developing new AI chip designs—hell, with billions in government investment it may have already had a head start. A sweeping AI plan from 2017 looks to catch up with the US on AI technology and application by 2020. By 2030, China’s aiming to be the global leader—and a champion for building general AI that matches humans in intellectual competence.

The country’s ambition is reflected in the team’s parting words.

“Our study is expected to stimulate AGI [artificial general intelligence] development by paving the way to more generalized hardware platforms,” said the authors, led by Dr. Luping Shi at Tsinghua University.

A Hardware Conundrum
Shi’s autonomous bike isn’t the first robotic two-wheeler. Back in 2015, the famed research nonprofit SRI International in Menlo Park, California teamed up with Yamaha to engineer MOTOBOT, a humanoid robot capable of driving a motorcycle. Powered by state-of-the-art robotic hardware and machine learning, MOTOBOT eventually raced MotoGPTM world champion Valentino Rossi in a nail-biting match-off.

However, the technological core of MOTOBOT and Shi’s bike vastly differ, and that difference reflects two pathways towards more powerful AI. One, exemplified by MOTOBOT, is software—developing brain-like algorithms with increasingly efficient architecture, efficacy, and speed. That sounds great, but deep neural nets demand so many computational resources that general-purpose chips can’t keep up.

As Shi told China Science Daily: “CPUs and other chips are driven by miniaturization technologies based on physics. Transistors might shrink to nanoscale-level in 10, 20 years. But what then?” As more transistors are squeezed onto these chips, efficient cooling becomes a limiting factor in computational speed. Tax them too much, and they melt.

For AI processes to continue, we need better hardware. An increasingly popular idea is to build neuromorphic chips, which resemble the brain from the ground up. IBM’s TrueNorth, for example, contains a massively parallel architecture nothing like the traditional Von Neumann structure of classic CPUs and GPUs. Similar to biological brains, TrueNorth’s memory is stored within “synapses” between physical “neurons” etched onto the chip, which dramatically cuts down on energy consumption.

But even these chips are limited. Because computation is tethered to hardware architecture, most chips resemble just one specific type of brain-inspired network called spiking neural networks (SNNs). Without doubt, neuromorphic chips are highly efficient setups with dynamics similar to biological networks. They also don’t play nicely with deep learning and other software-based AI.

Brain-AI Hybrid Core
Shi’s new Tianjic chip brought the two incompatibilities together onto a single piece of brainy hardware.

First was to bridge the deep learning and SNN divide. The two have very different computation philosophies and memory organizations, the team said. The biggest difference, however, is that artificial neural networks transform multidimensional data—image pixels, for example—into a single, continuous, multi-bit 0 and 1 stream. In contrast, neurons in SNNs activate using something called “binary spikes” that code for specific activation events in time.

Confused? Yeah, it’s hard to wrap my head around it too. That’s because SNNs act very similarly to our neural networks and nothing like computers. A particular neuron needs to generate an electrical signal (a “spike”) large enough to transfer down to the next one; little blips in signals don’t count. The way they transmit data also heavily depends on how they’re connected, or the network topology. The takeaway: SNNs work pretty differently than deep learning.

Shi’s team first recreated this firing quirk in the language of computers—0s and 1s—so that the coding mechanism would become compatible with deep learning algorithms. They then carefully aligned the step-by-step building blocks of the two models, which allowed them to tease out similarities into a common ground to further build on. “On the basis of this unified abstraction, we built a cross-paradigm neuron scheme,” they said.

In general, the design allowed both computational approaches to share the synapses, where neurons connect and store data, and the dendrites, the outgoing branches of the neurons. In contrast, the neuron body, where signals integrate, was left reconfigurable for each type of computation, as were the input branches. Each building block was combined into a single unified functional core (FCore), which acts like a deep learning/SNN converter depending on its specific setup. Translation: the chip can do both types of previously incompatible computation.

The Chip
Using nanoscale fabrication, the team arranged 156 FCores, containing roughly 40,000 neurons and 10 million synapses, onto a chip less than a fifth of an inch in length and width. Initial tests showcased the chip’s versatility, in that it can run both SNNs and deep learning algorithms such as the popular convolutional neural network (CNNs) often used in machine vision.

Compared to IBM TrueNorth, the density of Tianjic’s cores increased by 20 percent, speeding up performance ten times and increasing bandwidth at least 100-fold, the team said. When pitted against GPUs, the current hardware darling of machine learning, the chip increased processing throughput up to 100 times, while using just a sliver (1/10,000) of energy.

Although these stats are great, real-life performance is even better as a demo. Here’s where the authors gave their Tianjic brain a body. The team combined one chip with multiple specialized networks to process vision, balance, voice commands, and decision-making in real time. Object detection and target tracking, for example, relied on a deep neural net CNN, whereas voice commands and balance data were recognized using an SNN. The inputs were then integrated inside a neural state machine, which churned out decisions to downstream output modules—for example, controlling the handle bar to turn left.

Thanks to the chip’s brain-like architecture and bilingual ability, Tianjic “allowed all of the neural network models to operate in parallel and realized seamless communication across the models,” the team said. The result is an autonomous bike that rolls after its human, balances across speed bumps, avoids crashing into roadblocks, and answers to voice commands.

General AI?
“It’s a wonderful demonstration and quite impressive,” said the editorial team at Nature, which published the study on its cover last week.

However, they cautioned, when comparing Tianjic with state-of-the-art chips designed for a single problem toe-to-toe on that particular problem, Tianjic falls behind. But building these jack-of-all-trades hybrid chips is definitely worth the effort. Compared to today’s limited AI, what people really want is artificial general intelligence, which will require new architectures that aren’t designed to solve one particular problem.

Until people start to explore, innovate, and play around with different designs, it’s not clear how we can further progress in the pursuit of general AI. A self-driving bike might not be much to look at, but its hybrid brain is a pretty neat place to start.

*The name, in Chinese, means “heavenly machine,” “unknowable mystery of nature,” or “confidentiality.” Go figure.

Image Credit: Alexander Ryabintsev / Shutterstock.com Continue reading

Posted in Human Robots

#435423 Moving Beyond Mind-Controlled Limbs to ...

Brain-machine interface enthusiasts often gush about “closing the loop.” It’s for good reason. On the implant level, it means engineering smarter probes that only activate when they detect faulty electrical signals in brain circuits. Elon Musk’s Neuralink—among other players—are readily pursuing these bi-directional implants that both measure and zap the brain.

But to scientists laboring to restore functionality to paralyzed patients or amputees, “closing the loop” has broader connotations. Building smart mind-controlled robotic limbs isn’t enough; the next frontier is restoring sensation in offline body parts. To truly meld biology with machine, the robotic appendage has to “feel one” with the body.

This month, two studies from Science Robotics describe complementary ways forward. In one, scientists from the University of Utah paired a state-of-the-art robotic arm—the DEKA LUKE—with electrically stimulating remaining nerves above the attachment point. Using artificial zaps to mimic the skin’s natural response patterns to touch, the team dramatically increased the patient’s ability to identify objects. Without much training, he could easily discriminate between the small and large and the soft and hard while blindfolded and wearing headphones.

In another, a team based at the National University of Singapore took inspiration from our largest organ, the skin. Mimicking the neural architecture of biological skin, the engineered “electronic skin” not only senses temperature, pressure, and humidity, but continues to function even when scraped or otherwise damaged. Thanks to artificial nerves that transmit signals far faster than our biological ones, the flexible e-skin shoots electrical data 1,000 times quicker than human nerves.

Together, the studies marry neuroscience and robotics. Representing the latest push towards closing the loop, they show that integrating biological sensibilities with robotic efficiency isn’t impossible (super-human touch, anyone?). But more immediately—and more importantly—they’re beacons of hope for patients who hope to regain their sense of touch.

For one of the participants, a late middle-aged man with speckled white hair who lost his forearm 13 years ago, superpowers, cyborgs, or razzle-dazzle brain implants are the last thing on his mind. After a barrage of emotionally-neutral scientific tests, he grasped his wife’s hand and felt her warmth for the first time in over a decade. His face lit up in a blinding smile.

That’s what scientists are working towards.

Biomimetic Feedback
The human skin is a marvelous thing. Not only does it rapidly detect a multitude of sensations—pressure, temperature, itch, pain, humidity—its wiring “binds” disparate signals together into a sensory fingerprint that helps the brain identify what it’s feeling at any moment. Thanks to over 45 miles of nerves that connect the skin, muscles, and brain, you can pick up a half-full coffee cup, knowing that it’s hot and sloshing, while staring at your computer screen. Unfortunately, this complexity is also why restoring sensation is so hard.

The sensory electrode array implanted in the participant’s arm. Image Credit: George et al., Sci. Robot. 4, eaax2352 (2019)..
However, complex neural patterns can also be a source of inspiration. Previous cyborg arms are often paired with so-called “standard” sensory algorithms to induce a basic sense of touch in the missing limb. Here, electrodes zap residual nerves with intensities proportional to the contact force: the harder the grip, the stronger the electrical feedback. Although seemingly logical, that’s not how our skin works. Every time the skin touches or leaves an object, its nerves shoot strong bursts of activity to the brain; while in full contact, the signal is much lower. The resulting electrical strength curve resembles a “U.”

The LUKE hand. Image Credit: George et al., Sci. Robot. 4, eaax2352 (2019).
The team decided to directly compare standard algorithms with one that better mimics the skin’s natural response. They fitted a volunteer with a robotic LUKE arm and implanted an array of electrodes into his forearm—right above the amputation—to stimulate the remaining nerves. When the team activated different combinations of electrodes, the man reported sensations of vibration, pressure, tapping, or a sort of “tightening” in his missing hand. Some combinations of zaps also made him feel as if he were moving the robotic arm’s joints.

In all, the team was able to carefully map nearly 120 sensations to different locations on the phantom hand, which they then overlapped with contact sensors embedded in the LUKE arm. For example, when the patient touched something with his robotic index finger, the relevant electrodes sent signals that made him feel as if he were brushing something with his own missing index fingertip.

Standard sensory feedback already helped: even with simple electrical stimulation, the man could tell apart size (golf versus lacrosse ball) and texture (foam versus plastic) while blindfolded and wearing noise-canceling headphones. But when the team implemented two types of neuromimetic feedback—electrical zaps that resembled the skin’s natural response—his performance dramatically improved. He was able to identify objects much faster and more accurately under their guidance. Outside the lab, he also found it easier to cook, feed, and dress himself. He could even text on his phone and complete routine chores that were previously too difficult, such as stuffing an insert into a pillowcase, hammering a nail, or eating hard-to-grab foods like eggs and grapes.

The study shows that the brain more readily accepts biologically-inspired electrical patterns, making it a relatively easy—but enormously powerful—upgrade that seamlessly integrates the robotic arms with the host. “The functional and emotional benefits…are likely to be further enhanced with long-term use, and efforts are underway to develop a portable take-home system,” the team said.

E-Skin Revolution: Asynchronous Coded Electronic Skin (ACES)
Flexible electronic skins also aren’t new, but the second team presented an upgrade in both speed and durability while retaining multiplexed sensory capabilities.

Starting from a combination of rubber, plastic, and silicon, the team embedded over 200 sensors onto the e-skin, each capable of discerning contact, pressure, temperature, and humidity. They then looked to the skin’s nervous system for inspiration. Our skin is embedded with a dense array of nerve endings that individually transmit different types of sensations, which are integrated inside hubs called ganglia. Compared to having every single nerve ending directly ping data to the brain, this “gather, process, and transmit” architecture rapidly speeds things up.

The team tapped into this biological architecture. Rather than pairing each sensor with a dedicated receiver, ACES sends all sensory data to a single receiver—an artificial ganglion. This setup lets the e-skin’s wiring work as a whole system, as opposed to individual electrodes. Every sensor transmits its data using a characteristic pulse, which allows it to be uniquely identified by the receiver.

The gains were immediate. First was speed. Normally, sensory data from multiple individual electrodes need to be periodically combined into a map of pressure points. Here, data from thousands of distributed sensors can independently go to a single receiver for further processing, massively increasing efficiency—the new e-skin’s transmission rate is roughly 1,000 times faster than that of human skin.

Second was redundancy. Because data from individual sensors are aggregated, the system still functioned even when any individual receptors are damaged, making it far more resilient than previous attempts. Finally, the setup could easily scale up. Although the team only tested the idea with 240 sensors, theoretically the system should work with up to 10,000.

The team is now exploring ways to combine their invention with other material layers to make it water-resistant and self-repairable. As you might’ve guessed, an immediate application is to give robots something similar to complex touch. A sensory upgrade not only lets robots more easily manipulate tools, doorknobs, and other objects in hectic real-world environments, it could also make it easier for machines to work collaboratively with humans in the future (hey Wall-E, care to pass the salt?).

Dexterous robots aside, the team also envisions engineering better prosthetics. When coated onto cyborg limbs, for example, ACES may give them a better sense of touch that begins to rival the human skin—or perhaps even exceed it.

Regardless, efforts that adapt the functionality of the human nervous system to machines are finally paying off, and more are sure to come. Neuromimetic ideas may very well be the link that finally closes the loop.

Image Credit: Dan Hixson/University of Utah College of Engineering.. Continue reading

Posted in Human Robots