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#437820 In-Shoe Sensors and Mobile Robots Keep ...

In shoe sensor

Researchers at Stevens Institute of Technology are leveraging some of the newest mechanical and robotic technologies to help some of our oldest populations stay healthy, active, and independent.

Yi Guo, professor of electrical and computer engineering and director of the Robotics and Automation Laboratory, and Damiano Zanotto, assistant professor of mechanical engineering, and director of the Wearable Robotic Systems Laboratory, are collaborating with Ashley Lytle, assistant professor in Stevens’ College of Arts and Letters, and Ashwini K. Rao of Columbia University Medical Center, to combine an assistive mobile robot companion with wearable in-shoe sensors in a system designed to help elderly individuals maintain the balance and motion they need to thrive.

“Balance and motion can be significant issues for this population, and if elderly people fall and experience an injury, they are less likely to stay fit and exercise,” Guo said. “As a consequence, their level of fitness and performance decreases. Our mobile robot companion can help decrease the chances of falling and contribute to a healthy lifestyle by keeping their walking function at a good level.”

The mobile robots are designed to lead walking sessions and using the in-shoe sensors, monitor the user’s gait, indicate issues, and adjust the exercise speed and pace. The initiative is part of a four-year National Science Foundation research project.

“For the first time, we’re integrating our wearable sensing technology with an autonomous mobile robot,” said Zanotto, who worked with elderly people at Columbia University Medical Center for three years before coming to Stevens in 2016. “It’s exciting to be combining these different areas of expertise to leverage the strong points of wearable sensing technology, such as accurately capturing human movement, with the advantages of mobile robotics, such as much larger computational powers.”

The team is developing algorithms that fuse real-time data from smart, unobtrusive, in-shoe sensors and advanced on-board sensors to inform the robot’s navigation protocols and control the way the robot interacts with elderly individuals. It’s a promising way to assist seniors in safely doing walking exercises and maintaining their quality of life.

Bringing the benefits of the lab to life

Guo and Zanotto are working with Lytle, an expert in social and health psychology, to implement a social connectivity capability and make the bi-directional interaction between human and robot even more intuitive, engaging, and meaningful for seniors.

“Especially during COVID, it’s important for elderly people living on their own to connect socially with family and friends,” Zanotto said, “and the robot companion will also offer teleconferencing tools to provide that interaction in an intuitive and transparent way.”

“We want to use the robot for social connectedness, perhaps integrating it with a conversation agent such as Alexa,” Guo added. “The goal is to make it a companion robot that can sense, for example, that you are cooking, or you’re in the living room, and help with things you would do there.”

It’s a powerful example of how abstract concepts can have meaningful real-life benefits.

“As engineers, we tend to work in the lab, trying to optimize our algorithms and devices and technologies,” Zanotto noted, “but at the end of the day, what we do has limited value unless it has impact on real life. It’s fascinating to see how the devices and technologies we’re developing in the lab can be applied to make a difference for real people.”

Maintaining balance in a global pandemic

Although COVID-19 has delayed the planned testing at a senior center in New York City, it has not stopped the team’s progress.

“Although we can’t test on elderly populations yet, our students are still testing in the lab,” Guo said. “This summer and fall, for the first time, the students validated the system’s real-time ability to monitor and assess the dynamic margin of stability during walking—in other words, to evaluate whether the person following the robot is walking normally or has a risk of falling. They’re also designing parameters for the robot to give early warnings and feedback that help the human subjects correct posture and gait issues while walking.”

Those warnings would be literally underfoot, as the in-shoe sensors would pulse like a vibrating cell phone to deliver immediate directional information to the subject.

“We’re not the first to use this vibrotactile stimuli technology, but this application is new,” Zanotto said.

So far, the team has published papers in top robotics publication venues including IEEE Transactions on Neural Systems and Rehabilitation Engineering and the 2020 IEEE International Conference on Robotics and Automation (ICRA). It’s a big step toward realizing the synergies of bringing the technical expertise of engineers to bear on the clinical focus on biometrics—and the real lives of seniors everywhere. Continue reading

Posted in Human Robots

#437809 Q&A: The Masterminds Behind ...

Illustration: iStockphoto

Getting a car to drive itself is undoubtedly the most ambitious commercial application of artificial intelligence (AI). The research project was kicked into life by the 2004 DARPA Urban Challenge and then taken up as a business proposition, first by Alphabet, and later by the big automakers.

The industry-wide effort vacuumed up many of the world’s best roboticists and set rival companies on a multibillion-dollar acquisitions spree. It also launched a cycle of hype that paraded ever more ambitious deadlines—the most famous of which, made by Alphabet’s Sergei Brin in 2012, was that full self-driving technology would be ready by 2017. Those deadlines have all been missed.

Much of the exhilaration was inspired by the seeming miracles that a new kind of AI—deep learning—was achieving in playing games, recognizing faces, and transliterating voices. Deep learning excels at tasks involving pattern recognition—a particular challenge for older, rule-based AI techniques. However, it now seems that deep learning will not soon master the other intellectual challenges of driving, such as anticipating what human beings might do.

Among the roboticists who have been involved from the start are Gill Pratt, the chief executive officer of Toyota Research Institute (TRI) , formerly a program manager at the Defense Advanced Research Projects Agency (DARPA); and Wolfram Burgard, vice president of automated driving technology for TRI and president of the IEEE Robotics and Automation Society. The duo spoke with IEEE Spectrum’s Philip Ross at TRI’s offices in Palo Alto, Calif.

This interview has been condensed and edited for clarity.

IEEE Spectrum: How does AI handle the various parts of the self-driving problem?

Photo: Toyota

Gill Pratt

Gill Pratt: There are three different systems that you need in a self-driving car: It starts with perception, then goes to prediction, and then goes to planning.

The one that by far is the most problematic is prediction. It’s not prediction of other automated cars, because if all cars were automated, this problem would be much more simple. How do you predict what a human being is going to do? That’s difficult for deep learning to learn right now.

Spectrum: Can you offset the weakness in prediction with stupendous perception?

Photo: Toyota Research Institute for Burgard

Wolfram Burgard

Wolfram Burgard: Yes, that is what car companies basically do. A camera provides semantics, lidar provides distance, radar provides velocities. But all this comes with problems, because sometimes you look at the world from different positions—that’s called parallax. Sometimes you don’t know which range estimate that pixel belongs to. That might make the decision complicated as to whether that is a person painted onto the side of a truck or whether this is an actual person.

With deep learning there is this promise that if you throw enough data at these networks, it’s going to work—finally. But it turns out that the amount of data that you need for self-driving cars is far larger than we expected.

Spectrum: When do deep learning’s limitations become apparent?

Pratt: The way to think about deep learning is that it’s really high-performance pattern matching. You have input and output as training pairs; you say this image should lead to that result; and you just do that again and again, for hundreds of thousands, millions of times.

Here’s the logical fallacy that I think most people have fallen prey to with deep learning. A lot of what we do with our brains can be thought of as pattern matching: “Oh, I see this stop sign, so I should stop.” But it doesn’t mean all of intelligence can be done through pattern matching.

“I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur?”
—Gill Pratt, Toyota Research Institute

For instance, when I’m driving and I see a mother holding the hand of a child on a corner and trying to cross the street, I am pretty sure she’s not going to cross at a red light and jaywalk. I know from my experience being a human being that mothers and children don’t act that way. On the other hand, say there are two teenagers—with blue hair, skateboards, and a disaffected look. Are they going to jaywalk? I look at that, you look at that, and instantly the probability in your mind that they’ll jaywalk is much higher than for the mother holding the hand of the child. It’s not that you’ve seen 100,000 cases of young kids—it’s that you understand what it is to be either a teenager or a mother holding a child’s hand.

You can try to fake that kind of intelligence. If you specifically train a neural network on data like that, you could pattern-match that. But you’d have to know to do it.

Spectrum: So you’re saying that when you substitute pattern recognition for reasoning, the marginal return on the investment falls off pretty fast?

Pratt: That’s absolutely right. Unfortunately, we don’t have the ability to make an AI that thinks yet, so we don’t know what to do. We keep trying to use the deep-learning hammer to hammer more nails—we say, well, let’s just pour more data in, and more data.

Spectrum: Couldn’t you train the deep-learning system to recognize teenagers and to assign the category a high propensity for jaywalking?

Burgard: People have been doing that. But it turns out that these heuristics you come up with are extremely hard to tweak. Also, sometimes the heuristics are contradictory, which makes it extremely hard to design these expert systems based on rules. This is where the strength of the deep-learning methods lies, because somehow they encode a way to see a pattern where, for example, here’s a feature and over there is another feature; it’s about the sheer number of parameters you have available.

Our separation of the components of a self-driving AI eases the development and even the learning of the AI systems. Some companies even think about using deep learning to do the job fully, from end to end, not having any structure at all—basically, directly mapping perceptions to actions.

Pratt: There are companies that have tried it; Nvidia certainly tried it. In general, it’s been found not to work very well. So people divide the problem into blocks, where we understand what each block does, and we try to make each block work well. Some of the blocks end up more like the expert system we talked about, where we actually code things, and other blocks end up more like machine learning.

Spectrum: So, what’s next—what new technique is in the offing?

Pratt: If I knew the answer, we’d do it. [Laughter]

Spectrum: You said that if all cars on the road were automated, the problem would be easy. Why not “geofence” the heck out of the self-driving problem, and have areas where only self-driving cars are allowed?

Pratt: That means putting in constraints on the operational design domain. This includes the geography—where the car should be automated; it includes the weather, it includes the level of traffic, it includes speed. If the car is going slow enough to avoid colliding without risking a rear-end collision, that makes the problem much easier. Street trolleys operate with traffic still in some parts of the world, and that seems to work out just fine. People learn that this vehicle may stop at unexpected times. My suspicion is, that is where we’ll see Level 4 autonomy in cities. It’s going to be in the lower speeds.

“We are now in the age of deep learning, and we don’t know what will come after.”
—Wolfram Burgard, Toyota Research Institute

That’s a sweet spot in the operational design domain, without a doubt. There’s another one at high speed on a highway, because access to highways is so limited. But unfortunately there is still the occasional debris that suddenly crosses the road, and the weather gets bad. The classic example is when somebody irresponsibly ties a mattress to the top of a car and it falls off; what are you going to do? And the answer is that terrible things happen—even for humans.

Spectrum: Learning by doing worked for the first cars, the first planes, the first steam boilers, and even the first nuclear reactors. We ran risks then; why not now?

Pratt: It has to do with the times. During the era where cars took off, all kinds of accidents happened, women died in childbirth, all sorts of diseases ran rampant; the expected characteristic of life was that bad things happened. Expectations have changed. Now the chance of dying in some freak accident is quite low because of all the learning that’s gone on, the OSHA [Occupational Safety and Health Administration] rules, UL code for electrical appliances, all the building standards, medicine.

Furthermore—and we think this is very important—we believe that empathy for a human being at the wheel is a significant factor in public acceptance when there is a crash. We don’t know this for sure—it’s a speculation on our part. I’ve driven, I’ve had close calls; that could have been me that made that mistake and had that wreck. I think people are more tolerant when somebody else makes mistakes, and there’s an awful crash. In the case of an automated car, we worry that that empathy won’t be there.

Photo: Toyota

Toyota is using this
Platform 4 automated driving test vehicle, based on the Lexus LS, to develop Level-4 self-driving capabilities for its “Chauffeur” project.

Spectrum: Toyota is building a system called Guardian to back up the driver, and a more futuristic system called Chauffeur, to replace the driver. How can Chauffeur ever succeed? It has to be better than a human plus Guardian!

Pratt: In the discussions we’ve had with others in this field, we’ve talked about that a lot. What is the standard? Is it a person in a basic car? Or is it a person with a car that has active safety systems in it? And what will people think is good enough?

These systems will never be perfect—there will always be some accidents, and no matter how hard we try there will still be occasions where there will be some fatalities. At what threshold are people willing to say that’s okay?

Spectrum: You were among the first top researchers to warn against hyping self-driving technology. What did you see that so many other players did not?

Pratt: First, in my own case, during my time at DARPA I worked on robotics, not cars. So I was somewhat of an outsider. I was looking at it from a fresh perspective, and that helps a lot.

Second, [when I joined Toyota in 2015] I was joining a company that is very careful—even though we have made some giant leaps—with the Prius hybrid drive system as an example. Even so, in general, the philosophy at Toyota is kaizen—making the cars incrementally better every single day. That care meant that I was tasked with thinking very deeply about this thing before making prognostications.

And the final part: It was a new job for me. The first night after I signed the contract I felt this incredible responsibility. I couldn’t sleep that whole night, so I started to multiply out the numbers, all using a factor of 10. How many cars do we have on the road? Cars on average last 10 years, though ours last 20, but let’s call it 10. They travel on an order of 10,000 miles per year. Multiply all that out and you get 10 to the 10th miles per year for our fleet on Planet Earth, a really big number. I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur? And the answer was so incredibly good that I knew it would take a long time. That was five years ago.

Burgard: We are now in the age of deep learning, and we don’t know what will come after. We are still making progress with existing techniques, and they look very promising. But the gradient is not as steep as it was a few years ago.

Pratt: There isn’t anything that’s telling us that it can’t be done; I should be very clear on that. Just because we don’t know how to do it doesn’t mean it can’t be done. Continue reading

Posted in Human Robots

#437805 Video Friday: Quadruped Robot HyQ ...

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!):

RSS 2020 – July 12-16, 2020 – [Virtual Conference]
CLAWAR 2020 – August 24-26, 2020 – [Virtual Conference]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
IROS 2020 – October 25-29, 2020 – Las Vegas, Nevada
ICSR 2020 – November 14-16, 2020 – Golden, Colorado
Let us know if you have suggestions for next week, and enjoy today’s videos.

Four-legged HyQ balancing on two legs. Nice results from the team at IIT’s Dynamic Legged Systems Lab. And we can’t wait to see the “ninja walk,” currently shown in simulation, implemented with the real robot!

The development of balance controllers for legged robots with point feet remains a challenge when they have to traverse extremely constrained environments. We present a balance controller that has the potential to achieve line walking for quadruped robots. Our initial experiments show the 90-kg robot HyQ balancing on two feet and recovering from external pushes, as well as some changes in posture achieved without losing balance.

[ IIT ]

Thanks Victor!

Ava Robotics’ telepresence robot has been beheaded by MIT, and it now sports a coronavirus-destroying UV array.

UV-C light has proven to be effective at killing viruses and bacteria on surfaces and aerosols, but it’s unsafe for humans to be exposed. Fortunately, Ava’s telepresence robot doesn’t require any human supervision. Instead of the telepresence top, the team subbed in a UV-C array for disinfecting surfaces. Specifically, the array uses short-wavelength ultraviolet light to kill microorganisms and disrupt their DNA in a process called ultraviolet germicidal irradiation. The complete robot system is capable of mapping the space — in this case, GBFB’s warehouse — and navigating between waypoints and other specified areas. In testing the system, the team used a UV-C dosimeter, which confirmed that the robot was delivering the expected dosage of UV-C light predicted by the model.

[ MIT ]

While it’s hard enough to get quadrupedal robots to walk in complex environments, this work from the Robotic Systems Lab at ETH Zurich shows some impressive whole body planning that allows ANYmal to squeeze its body through small or weirdly shaped spaces.

[ RSL ]

Engineering researchers at North Carolina State University and Temple University have developed soft robots inspired by jellyfish that can outswim their real-life counterparts. More practically, the new jellyfish-bots highlight a technique that uses pre-stressed polymers to make soft robots more powerful.

The researchers also used the technique to make a fast-moving robot that resembles a larval insect curling its body, then jumping forward as it quickly releases its stored energy. Lastly, the researchers created a three-pronged gripping robot – with a twist. Most grippers hang open when “relaxed,” and require energy to hold on to their cargo as it is lifted and moved from point A to point B. But this claw’s default position is clenched shut. Energy is required to open the grippers, but once they’re in position, the grippers return to their “resting” mode – holding their cargo tight.

[ NC State ]

As control skills increase, we are more and more impressed by what a Cassie bipedal robot can do. Those who have been following our channel, know that we always show the limitations of our work. So while there is still much to do, you gotta like the direction things are going. Later this year, you will see this controller integrated with our real-time planner and perception system. Autonomy with agility! Watch out for us!

[ University of Michigan ]

GITAI’s S1 arm is a little less exciting than their humanoid torso, but it looks like this one might actually be going to the ISS next year.

Here’s how the humanoid would handle a similar task:

[ GITAI ]

Thanks Fan!

If you need a robot that can lift 250 kg at 10 m/s across a workspace of a thousand cubic meters, here’s your answer.

[ Fraunhofer ]

Penn engineers with funding from the National Science Foundation, have nanocardboard plates able to levitate when bright light is shone on them. This fleet of tiny aircraft could someday explore the skies of other worlds, including Mars. The thinner atmosphere there would give the flyers a boost, enabling them to carry payloads ten times as massive as they are, making them an efficient, light-weight alternative to the Mars helicopter.

[ UPenn ]

Erin Sparks, assistant professor in Plant and Soil Sciences, dreamed of a robot she could use in her research. A perfect partnership was formed when Adam Stager, then a mechanical engineering Ph.D. student, reached out about a robot he had a gut feeling might be useful in agriculture. The pair moved forward with their research with corn at the UD Farm, using the robot to capture dynamic phenotyping information of brace roots over time.

[ Sparks Lab ]

This is a video about robot spy turtles but OMG that bird drone landing gear.

[ PBS ]

If you have a DJI Mavic, you now have something new to worry about.

[ DroGone ]

I was able to spot just one single person in the warehouse footage in this video.

[ Berkshire Grey ]

Flyability has partnered with the ROBINS Project to help fill gaps in the technology used in ship inspections. Watch this video to learn more about the ROBINS project and how Flyability’s drones for confined spaces are helping make inspections on ships safer, cheaper, and more efficient.

[ Flyability ]

In this video, a mission of the Alpha Aerial Scout of Team CERBERUS during the DARPA Subterranean Challenge Urban Circuit event is presented. The Alpha Robot operates inside the Satsop Abandoned Power Plant and performs autonomous exploration. This deployment took place during the 3rd field trial of team CERBERUS during the Urban Circuit event of the DARPA Subterranean Challenge.

[ ARL ]

More excellent talks from the remote Legged Robots ICRA workshop- we’ve posted three here, but there are several other good talks this week as well.

[ ICRA 2020 Legged Robots Workshop ] Continue reading

Posted in Human Robots

#437776 Video Friday: This Terrifying Robot Will ...

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!):

CLAWAR 2020 – August 24-26, 2020 – [Virtual Conference]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
IROS 2020 – October 25-29, 2020 – Las Vegas, Nevada
ICSR 2020 – November 14-16, 2020 – Golden, Colorado
Let us know if you have suggestions for next week, and enjoy today's videos.

The Aigency, which created the FitBot launch video below, is “the world’s first talent management resource for robotic personalities.”

Robots will be playing a bigger role in our lives in the future. By learning to speak their language and work with them now, we can make this future better for everybody. If you’re a creator that’s producing content to entertain and educate people, robots can be a part of that. And we can help you. Robotic actors can show up alongside the rest of your actors.

The folks at Aigency have put together a compilation reel of clips they’ve put on TikTok, which is nice of them, because some of us don’t know how to TikTok because we’re old and boring.

Do googly eyes violate the terms and conditions?

[ Aigency ]

Shane Wighton of the “Stuff Made Here” YouTube channel, who you might remember from that robotic basketball hoop, has a new invention: A haircut robot. This is not the the first barber bot, but previous designs typically used hair clippers. Shane wanted his robot to use scissors. Hilarious and terrifying at once.

[ Stuff Made Here ]

Starting in October of 2016, Prof. Charlie Kemp and Henry M. Clever invented a new kind of robot. They named the prototype NewRo. In March of 2017, Prof. Kemp filmed this video of Henry operating NewRo to perform a number of assistive tasks. While visiting the Bay Area for a AAAI Symposium workshop at Stanford, Prof. Kemp showed this video to a select group of people to get advice, including Dr. Aaron Edsinger. In August of 2017, Dr. Edsinger and Dr. Kemp founded Hello Robot Inc. to commercialize this patent pending assistive technology. Hello Robot Inc. licensed the intellectual property (IP) from Georgia Tech. After three years of stealthy effort, Hello Robot Inc. revealed Stretch, a new kind of robot!

[ Georgia Tech ]

NASA’s Ingenuity Mars Helicopter will make history's first attempt at powered flight on another planet next spring. It is riding with the agency's next mission to Mars (the Mars 2020 Perseverance rover) as it launches from Cape Canaveral Air Force Station later this summer. Perseverance, with Ingenuity attached to its belly, will land on Mars February 18, 2021.

[ JPL ]

For humans, it can be challenging to manipulate thin flexible objects like ropes, wires, or cables. But if these problems are hard for humans, they are nearly impossible for robots. As a cable slides between the fingers, its shape is constantly changing, and the robot’s fingers must be constantly sensing and adjusting the cable’s position and motion. A group of researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and from the MIT Department of Mechanical Engineering pursued the task from a different angle, in a manner that more closely mimics us humans. The team’s new system uses a pair of soft robotic grippers with high-resolution tactile sensors (and no added mechanical constraints) to successfully manipulate freely moving cables.

The team observed that it was difficult to pull the cable back when it reached the edge of the finger, because of the convex surface of the GelSight sensor. Therefore, they hope to improve the finger-sensor shape to enhance the overall performance. In the future, they plan to study more complex cable manipulation tasks such as cable routing and cable inserting through obstacles, and they want to eventually explore autonomous cable manipulation tasks in the auto industry.

[ MIT ]

Gripping robots typically have troubles grabbing transparent or shiny objects. A new technique by Carnegie Mellon University relies on color camera system and machine learning to recognize shapes based on color.

[ CMU ]

A new robotic prosthetic leg prototype offers a more natural, comfortable gait while also being quieter and more energy efficient than other designs. The key is the use of new small and powerful motors with fewer gears, borrowed from the space industry. This streamlined technology enables a free-swinging knee and regenerative braking, which charges the battery during use with energy that would typically be dissipated when the foot hits the ground. This feature enables the leg to more than double a typical prosthetic user's walking needs with one charge per day.

[ University of Michigan ]

Thanks Kate!

This year’s Wonder League teams have been put to the test not only with the challenges set forth by Wonder Workshop and Cartoon Network as they look to help the creek kids from Craig of the Creek solve the greatest mystery of all – the quest for the Lost Realm but due to forces outside their control. With a global pandemic displacing many teams from one another due to lockdowns and quarantines, these teams continued to push themselves to find new ways to work together, solve problems, communicate more effectively, and push themselves to complete a journey that they started and refused to give up on. We at Wonder Workshop are humbled and in awe of all these teams have accomplished.

[ Wonder Workshop ]

Thanks Nicole!

Meet Colin Creager, a mechanical engineer at NASA's Glenn Research Center. Colin is focusing on developing tires that can be used on other worlds. These tires use coil springs made of a special shape memory alloy that will let rovers move across sharp jagged rocks or through soft sand on the Moon or Mars.

[ NASA ]

To be presented at IROS this year, “the first on robot collision detection system using low cost microphones.”

[ Rutgers ]

Robot and mechanism designs inspired by the art of Origami have the potential to generate compact, deployable, lightweight morphing structures, as seen in nature, for potential applications in search-and-rescue, aerospace systems, and medical devices. However, it is challenging to obtain actuation that is easily patternable, reversible, and made with a scalable manufacturing process for origami-inspired self-folding machines. In this work, we describe an approach to design reversible self-folding machines using liquid crystal elastomer (LCE), that contracts when heated, as an artificial muscle.

[ UCSD ]

Just in case you need some extra home entertainment, and you’d like cleaner floors at the same time.

[ iRobot ]

Sure, toss it from a drone. Or from orbit. Whatever, it’s squishy!

[ Squishy Robotics ]

The [virtual] RSS conference this week featured an excellent lineup of speakers and panels, and the best part about it being virtual is that you can watch them all at your leisure! Here’s what’s been posted so far:

[ RSS 2020 ]

Lockheed Martin Robotics Seminar: Toward autonomous flying insect-sized robots: recent results in fabrication, design, power systems, control, and sensing with Sawyer Fuller.

[ UMD ]

In this episode of the AI Podcast, Lex interviews Sergey Levine.

[ AI Podcast ] Continue reading

Posted in Human Robots

#437763 Peer Review of Scholarly Research Gets ...

In the world of academics, peer review is considered the only credible validation of scholarly work. Although the process has its detractors, evaluation of academic research by a cohort of contemporaries has endured for over 350 years, with “relatively minor changes.” However, peer review may be set to undergo its biggest revolution ever—the integration of artificial intelligence.

Open-access publisher Frontiers has debuted an AI tool called the Artificial Intelligence Review Assistant (AIRA), which purports to eliminate much of the grunt work associated with peer review. Since the beginning of June 2020, every one of the 11,000-plus submissions Frontiers received has been run through AIRA, which is integrated into its collaborative peer-review platform. This also makes it accessible to external users, accounting for some 100,000 editors, authors, and reviewers. Altogether, this helps “maximize the efficiency of the publishing process and make peer-review more objective,” says Kamila Markram, founder and CEO of Frontiers.

AIRA’s interactive online platform, which is a first of its kind in the industry, has been in development for three years.. It performs three broad functions, explains Daniel Petrariu, director of project management: assessing the quality of the manuscript, assessing quality of peer review, and recommending editors and reviewers. At the initial validation stage, the AI can make up to 20 recommendations and flag potential issues, including language quality, plagiarism, integrity of images, conflicts of interest, and so on. “This happens almost instantly and with [high] accuracy, far beyond the rate at which a human could be expected to complete a similar task,” Markram says.

“We have used a wide variety of machine-learning models for a diverse set of applications, including computer vision, natural language processing, and recommender systems,” says Markram. This includes simple bag-of-words models, as well as more sophisticated deep-learning ones. AIRA also leverages a large knowledge base of publications and authors.

Markram notes that, to address issues of possible AI bias, “We…[build] our own datasets and [design] our own algorithms. We make sure no statistical biases appear in the sampling of training and testing data. For example, when building a model to assess language quality, scientific fields are equally represented so the model isn’t biased toward any specific topic.” Machine- and deep-learning approaches, along with feedback from domain experts, including errors, are captured and used as additional training data. “By regularly re-training, we make sure our models improve in terms of accuracy and stay up-to-date.”

The AI’s job is to flag concerns; humans take the final decisions, says Petrariu. As an example, he cites image manipulation detection—something AI is super-efficient at but is nearly impossible for a human to perform with the same accuracy. “About 10 percent of our flagged images have some sort of problem,” he adds. “[In academic publishing] nobody has done this kind of comprehensive check [using AI] before,” says Petrariu. AIRA, he adds, facilitates Frontiers’ mission to make science open and knowledge accessible to all. Continue reading

Posted in Human Robots