Tag Archives: engineers

#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

#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

#437769 Q&A: Facebook’s CTO Is at War With ...

Photo: Patricia de Melo Moreira/AFP/Getty Images

Facebook chief technology officer Mike Schroepfer leads the company’s AI and integrity efforts.

Facebook’s challenge is huge. Billions of pieces of content—short and long posts, images, and combinations of the two—are uploaded to the site daily from around the world. And any tiny piece of that—any phrase, image, or video—could contain so-called bad content.

In its early days, Facebook relied on simple computer filters to identify potentially problematic posts by their words, such as those containing profanity. These automatically filtered posts, as well as posts flagged by users as offensive, went to humans for adjudication.

In 2015, Facebook started using artificial intelligence to cull images that contained nudity, illegal goods, and other prohibited content; those images identified as possibly problematic were sent to humans for further review.

By 2016, more offensive photos were reported by Facebook’s AI systems than by Facebook users (and that is still the case).

In 2018, Facebook CEO Mark Zuckerberg made a bold proclamation: He predicted that within five or ten years, Facebook’s AI would not only look for profanity, nudity, and other obvious violations of Facebook’s policies. The tools would also be able to spot bullying, hate speech, and other misuse of the platform, and put an immediate end to them.

Today, automated systems using algorithms developed with AI scan every piece of content between the time when a user completes a post and when it is visible to others on the site—just fractions of a second. In most cases, a violation of Facebook’s standards is clear, and the AI system automatically blocks the post. In other cases, the post goes to human reviewers for a final decision, a workforce that includes 15,000 content reviewers and another 20,000 employees focused on safety and security, operating out of more than 20 facilities around the world.

In the first quarter of this year, Facebook removed or took other action (like appending a warning label) on more than 9.6 million posts involving hate speech, 8.6 million involving child nudity or exploitation, almost 8 million posts involving the sale of drugs, 2.3 million posts involving bullying and harassment, and tens of millions of posts violating other Facebook rules.

Right now, Facebook has more than 1,000 engineers working on further developing and implementing what the company calls “integrity” tools. Using these systems to screen every post that goes up on Facebook, and doing so in milliseconds, is sucking up computing resources. Facebook chief technology officer Mike Schroepfer, who is heading up Facebook’s AI and integrity efforts, spoke with IEEE Spectrum about the team’s progress on building an AI system that detects bad content.

Since that discussion, Facebook’s policies around hate speech have come under increasing scrutiny, with particular attention on divisive posts by political figures. A group of major advertisers in June announced that they would stop advertising on the platform while reviewing the situation, and civil rights groups are putting pressure on others to follow suit until Facebook makes policy changes related to hate speech and groups that promote hate, misinformation, and conspiracies.

Facebook CEO Mark Zuckerberg responded with news that Facebook will widen the category of what it considers hateful content in ads. Now the company prohibits claims that people from a specific race, ethnicity, national origin, religious affiliation, caste, sexual orientation, gender identity, or immigration status are a threat to the physical safety, health, or survival of others. The policy change also aims to better protect immigrants, migrants, refugees, and asylum seekers from ads suggesting these groups are inferior or expressing contempt. Finally, Zuckerberg announced that the company will label some problematic posts by politicians and government officials as content that violates Facebook’s policies.

However, civil rights groups say that’s not enough. And an independent audit released in July also said that Facebook needs to go much further in addressing civil rights concerns and disinformation.

Schroepfer indicated that Facebook’s AI systems are designed to quickly adapt to changes in policy. “I don’t expect considerable technical changes are needed to adjust,” he told Spectrum.

This interview has been edited and condensed for clarity.

IEEE Spectrum: What are the stakes of content moderation? Is this an existential threat to Facebook? And is it critical that you deal well with the issue of election interference this year?

Schroepfer: It’s probably existential; it’s certainly massive. We are devoting a tremendous amount of our attention to it.

The idea that anyone could meddle in an election is deeply disturbing and offensive to all of us here, just as people and citizens of democracies. We don’t want to see that happen anywhere, and certainly not on our watch. So whether it’s important to the company or not, it’s important to us as people. And I feel a similar way on the content-moderation side.

There are not a lot of easy choices here. The only way to prevent people, with certainty, from posting bad things is to not let them post anything. We can take away all voice and just say, “Sorry, the Internet’s too dangerous. No one can use it.” That will certainly get rid of all hate speech online. But I don’t want to end up in that world. And there are variants of that world that various governments are trying to implement, where they get to decide what’s true or not, and you as a person don’t. I don’t want to get there either.

My hope is that we can build a set of tools that make it practical for us to do a good enough job, so that everyone is still excited about the idea that anyone can share what they want, and so that Facebook is a safe and reasonable place for people to operate in.

Spectrum: You joined Facebook in 2008, before AI was part of the company’s toolbox. When did that change? When did you begin to think that AI tools would be useful to Facebook?

Schroepfer: Ten years ago, AI wasn’t commercially practical; the technology just didn’t work very well. In 2012, there was one of those moments that a lot of people point to as the beginning of the current revolution in deep learning and AI. A computer-vision model—a neural network—was trained using what we call supervised training, and it turned out to be better than all the existing models.

Spectrum: How is that training done, and how did computer-vision models come to Facebook?

Image: Facebook

Just Broccoli? Facebook’s image analysis algorithms can tell the difference between marijuana [left] and tempura broccoli [right] better than some humans.

Schroepfer: Say I take a bunch of photos and I have people look at them. If they see a photo of a cat, they put a text label that says cat; if it’s one of a dog, the text label says dog. If you build a big enough data set and feed that to the neural net, it learns how to tell the difference between cats and dogs.

Prior to 2012, it didn’t work very well. And then in 2012, there was this moment where it seemed like, “Oh wow, this technique might work.” And a few years later we were deploying that form of technology to help us detect problematic imagery.

Spectrum: Do your AI systems work equally well on all types of prohibited content?

Schroepfer: Nudity was technically easiest. I don’t need to understand language or culture to understand that this is either a naked human or not. Violence is a much more nuanced problem, so it was harder technically to get it right. And with hate speech, not only do you have to understand the language, it may be very contextual, even tied to recent events. A week before the Christchurch shooting [New Zealand, 2019], saying “I wish you were in the mosque” probably doesn’t mean anything. A week after, that might be a terrible thing to say.

Spectrum: How much progress have you made on hate speech?

Schroepfer: AI, in the first quarter of 2020, proactively detected 88.8 percent of the hate-speech content we removed, up from 80.2 percent in the previous quarter. In the first quarter of 2020, we took action on 9.6 million pieces of content for violating our hate-speech policies.

Image: Facebook

Off Label: Sometimes image analysis isn’t enough to determine whether a picture posted violates the company’s policies. In considering these candy-colored vials of marijuana, for example, the algorithms can look at any accompanying text and, if necessary, comments on the post.

Spectrum: It sounds like you’ve expanded beyond tools that analyze images and are also using AI tools that analyze text.

Schroepfer: AI started off as very siloed. People worked on language, people worked on computer vision, people worked on video. We’ve put these things together—in production, not just as research—into multimodal classifiers.

[Schroepfer shows a photo of a pan of Rice Krispies treats, with text referring to it as a “potent batch”] This is a case in which you have an image, and then you have the text on the post. This looks like Rice Krispies. On its own, this image is fine. You put the text together with it in a bigger model; that can then understand what’s going on. That didn’t work five years ago.

Spectrum: Today, every post that goes up on Facebook is immediately checked by automated systems. Can you explain that process?

Image: Facebook

Bigger Picture: Identifying hate speech is often a matter of context. Either the text or the photo in this post isn’t hateful standing alone, but putting them together tells a different story.

Schroepfer: You upload an image and you write some text underneath it, and the systems look at both the image and the text to try to see which, if any, policies it violates. Those decisions are based on our Community Standards. It will also look at other signals on the posts, like the comments people make.

It happens relatively instantly, though there may be times things happen after the fact. Maybe you uploaded a post that had misinformation in it, and at the time you uploaded it, we didn’t know it was misinformation. The next day we fact-check something and scan again; we may find your post and take it down. As we learn new things, we’re going to go back through and look for violations of what we now know to be a problem. Or, as people comment on your post, we might update our understanding of it. If people are saying, “That’s terrible,” or “That’s mean,” or “That looks fake,” those comments may be an interesting signal.

Spectrum: How is Facebook applying its AI tools to the problem of election interference?

Schroepfer: I would split election interference into two categories. There are times when you’re going after the content, and there are times you’re going after the behavior or the authenticity of the person.

On content, if you’re sharing misinformation, saying, “It’s super Wednesday, not super Tuesday, come vote on Wednesday,” that’s a problem whether you’re an American sitting in California or a foreign actor.

Other times, people create a series of Facebook pages pretending they’re Americans, but they’re really a foreign entity. That is a problem on its own, even if all the content they’re sharing completely meets our Community Standards. The problem there is that you have a foreign government running an information operation.

There, you need different tools. What you’re trying to do is put pieces together, to say, “Wait a second. All of these pages—Martians for Justice, Moonlings for Justice, and Venusians for Justice”—are all run by an administrator with an IP address that’s outside the United States. So they’re all connected, even though they’re pretending to not be connected. That’s a very different problem than me sitting in my office in Menlo Park [Calif.] sharing misinformation.

I’m not going to go into lots of technical detail, because this is an area of adversarial nature. The fundamental problem you’re trying to solve is that there’s one entity coordinating the activity of a bunch of things that look like they’re not all one thing. So this is a series of Instagram accounts, or a series of Facebook pages, or a series of WhatsApp accounts, and they’re pretending to be totally different things. We’re looking for signals that these things are related in some way. And we’re looking through the graph [what Facebook calls its map of relationships between users] to understand the properties of this network.

Spectrum: What cutting-edge AI tools and methods have you been working on lately?

Schroepfer: Supervised learning, with humans setting up the instruction process for the AI systems, is amazingly effective. But it has a very obvious flaw: the speed at which you can develop these things is limited by how fast you can curate the data sets. If you’re dealing in a problem domain where things change rapidly, you have to rebuild a new data set and retrain the whole thing.

Self-supervision is inspired by the way people learn, by the way kids explore the world around them. To get computers to do it themselves, we take a bunch of raw data and build a way for the computer to construct its own tests. For language, you scan a bunch of Web pages, and the computer builds a test where it takes a sentence, eliminates one of the words, and figures out how to predict what word belongs there. And because it created the test, it actually knows the answer. I can use as much raw text as I can find and store because it’s processing everything itself and doesn’t require us to sit down and build the information set. In the last two years there has been a revolution in language understanding as a result of AI self-supervised learning.

Spectrum: What else are you excited about?

Schroepfer: What we’ve been working on over the last few years is multilingual understanding. Usually, when I’m trying to figure out, say, whether something is hate speech or not I have to go through the whole process of training the model in every language. I have to do that one time for every language. When you make a post, the first thing we have to figure out is what language your post is in. “Ah, that’s Spanish. So send it to the Spanish hate-speech model.”

We’ve started to build a multilingual model—one box where you can feed in text in 40 different languages and it determines whether it’s hate speech or not. This is way more effective and easier to deploy.

To geek out for a second, just the idea that you can build a model that understands a concept in multiple languages at once is crazy cool. And it not only works for hate speech, it works for a variety of things.

When we started working on this multilingual model years ago, it performed worse than every single individual model. Now, it not only works as well as the English model, but when you get to the languages where you don’t have enough data, it’s so much better. This rapid progress is very exciting.

Spectrum: How do you move new AI tools from your research labs into operational use?

Schroepfer: Engineers trying to make the next breakthrough will often say, “Cool, I’ve got a new thing and it achieved state-of-the-art results on machine translation.” And we say, “Great. How long does it take to run in production?” They say, “Well, it takes 10 seconds for every sentence to run on a CPU.” And we say, “It’ll eat our whole data center if we deploy that.” So we take that state-of-the-art model and we make it 10 or a hundred or a thousand times more efficient, maybe at the cost of a little bit of accuracy. So it’s not as good as the state-of-the-art version, but it’s something we can actually put into our data centers and run in production.

Spectrum: What’s the role of the humans in the loop? Is it true that Facebook currently employs 35,000 moderators?

Schroepfer: Yes. Right now our goal is not to reduce that. Our goal is to do a better job catching bad content. People often think that the end state will be a fully automated system. I don’t see that world coming anytime soon.

As automated systems get more sophisticated, they take more and more of the grunt work away, freeing up the humans to work on the really gnarly stuff where you have to spend an hour researching.

We also use AI to give our human moderators power tools. Say I spot this new meme that is telling everyone to vote on Wednesday rather than Tuesday. I have a tool in front of me that says, “Find variants of that throughout the system. Find every photo with the same text, find every video that mentions this thing and kill it in one shot.” Rather than, I found this one picture, but then a bunch of other people upload that misinformation in different forms.

Another important aspect of AI is that anything I can do to prevent a person from having to look at terrible things is time well spent. Whether it’s a person employed by us as a moderator or a user of our services, looking at these things is a terrible experience. If I can build systems that take the worst of the worst, the really graphic violence, and deal with that in an automated fashion, that’s worth a lot to me. Continue reading

Posted in Human Robots

#437765 Video Friday: Massive Robot Joins ...

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!):

AWS Cloud Robotics Summit – August 18-19, 2020 – [Online 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.

Here are some professional circus artists messing around with an industrial robot for fun, like you do.

The acrobats are part of Östgötateatern, a Swedish theatre group, and the chair bit got turned into its own act, called “The Last Fish.” But apparently the Swedish Work Environment Authority didn’t like that an industrial robot—a large ABB robotic arm—was being used in an artistic performance, arguing that the same safety measures that apply in a factory setting would apply on stage. In other words, the robot had to operate inside a protective cage and humans could not physically interact with it.

When told that their robot had to be removed, the acrobats went to court. And won! At least that’s what we understand from this Swedish press release. The court in Linköping, in southern Sweden, ruled that the safety measures taken by the theater had been sufficient. The group had worked with a local robotics firm, Dyno Robotics, to program the manipulator and learn how to interact with it as safely as possible. The robot—which the acrobats say is the eighth member of their troupe—will now be allowed to return.

[ Östgötateatern ]

Houston Mechathronics’ Aquanaut continues to be awesome, even in the middle of a pandemic. It’s taken the big step (big swim?) out of NASA’s swimming pool and into open water.

[ HMI ]

Researchers from Carnegie Mellon University and Facebook AI Research have created a navigation system for robots powered by common sense. The technique uses machine learning to teach robots how to recognize objects and understand where they’re likely to be found in house. The result allows the machines to search more strategically.

[ CMU ]

Cassie manages 2.1 m/s, which is uncomfortably fast in a couple of different ways.

Next, untethered. After that, running!

[ Michigan Robotics ]

Engineers at Caltech have designed a new data-driven method to control the movement of multiple robots through cluttered, unmapped spaces, so they do not run into one another.

Multi-robot motion coordination is a fundamental robotics problem with wide-ranging applications that range from urban search and rescue to the control of fleets of self-driving cars to formation-flying in cluttered environments. Two key challenges make multi-robot coordination difficult: first, robots moving in new environments must make split-second decisions about their trajectories despite having incomplete data about their future path; second, the presence of larger numbers of robots in an environment makes their interactions increasingly complex (and more prone to collisions).

To overcome these challenges, Soon-Jo Chung, Bren Professor of Aerospace, and Yisong Yue, professor of computing and mathematical sciences, along with Caltech graduate student Benjamin Rivière (MS ’18), postdoctoral scholar Wolfgang Hönig, and graduate student Guanya Shi, developed a multi-robot motion-planning algorithm called “Global-to-Local Safe Autonomy Synthesis,” or GLAS, which imitates a complete-information planner with only local information, and “Neural-Swarm,” a swarm-tracking controller augmented to learn complex aerodynamic interactions in close-proximity flight.

[ Caltech ]

Fetch Robotics’ Freight robot is now hauling around pulsed xenon UV lamps to autonomously disinfect spaces with UV-A, UV-B, and UV-C, all at the same time.

[ SmartGuard UV ]

When you’re a vertically symmetrical quadruped robot, there is no upside-down.

[ Ghost Robotics ]

In the virtual world, the objects you pick up do not exist: you can see that cup or pen, but it does not feel like you’re touching them. That presented a challenge to EPFL professor Herbert Shea. Drawing on his extensive experience with silicone-based muscles and motors, Shea wanted to find a way to make virtual objects feel real. “With my team, we’ve created very small, thin and fast actuators,” explains Shea. “They are millimeter-sized capsules that use electrostatic energy to inflate and deflate.” The capsules have an outer insulating membrane made of silicone enclosing an inner pocket filled with oil. Each bubble is surrounded by four electrodes, that can close like a zipper. When a voltage is applied, the electrodes are pulled together, causing the center of the capsule to swell like a blister. It is an ingenious system because the capsules, known as HAXELs, can move not only up and down, but also side to side and around in a circle. “When they are placed under your fingers, it feels as though you are touching a range of different objects,” says Shea.

[ EPFL ]

Through the simple trick of reversing motors on impact, a quadrotor can land much more reliably on slopes.

[ Sherbrooke ]

Turtlebot delivers candy at Harvard.

I <3 Turtlebot SO MUCH

[ Harvard ]

Traditional drone controllers are a little bit counterintuitive, because there’s one stick that’s forwards and backwards and another stick that’s up and down but they’re both moving on the same axis. How does that make sense?! Here’s a remote that gives you actual z-axis control instead.

[ Fenics ]

Thanks Ashley!

Lio is a mobile robot platform with a multifunctional arm explicitly designed for human-robot interaction and personal care assistant tasks. The robot has already been deployed in several health care facilities, where it is functioning autonomously, assisting staff and patients on an everyday basis.

[ F&P Robotics ]

Video shows a ground vehicle autonomously exploring and mapping a multi-storage garage building and a connected patio on Carnegie Mellon University campus. The vehicle runs onboard state estimation and mapping leveraging range, vision, and inertial sensing, local planning for collision avoidance, and terrain analysis. All processing is real-time and no post-processing involved. The vehicle drives at 2m/s through the exploration run. This work is dedicated to DARPA Subterranean Challange.

[ CMU ]

Raytheon UK’s flagship STEM programme, the Quadcopter Challenge, gives 14-15 year olds the chance to participate in a hands-on, STEM-based engineering challenge to build a fully operational quadcopter. Each team is provided with an identical kit of parts, tools and instructions to build and customise their quadcopter, whilst Raytheon UK STEM Ambassadors provide mentoring, technical support and deliver bite-size learning modules to support the build.

[ Raytheon ]

A video on some of the research work that is being carried out at The Australian Centre for Field Robotics, University of Sydney.

[ University of Sydney ]

Jeannette Bohg, assistant professor of computer science at Stanford University, gave one of the Early Career Award Keynotes at RSS 2020.

[ RSS 2020 ]

Adam Savage remembers Grant Imahara.

[ Tested ] Continue reading

Posted in Human Robots

#437751 Startup and Academics Find Path to ...

Engineers have been chasing a form of AI that could drastically lower the energy required to do typical AI things like recognize words and images. This analog form of machine learning does one of the key mathematical operations of neural networks using the physics of a circuit instead of digital logic. But one of the main things limiting this approach is that deep learning’s training algorithm, back propagation, has to be done by GPUs or other separate digital systems.

Now University of Montreal AI expert Yoshua Bengio, his student Benjamin Scellier, and colleagues at startup Rain Neuromorphics have come up with way for analog AIs to train themselves. That method, called equilibrium propagation, could lead to continuously learning, low-power analog systems of a far greater computational ability than most in the industry now consider possible, according to Rain CTO Jack Kendall.

Analog circuits could save power in neural networks in part because they can efficiently perform a key calculation, called multiply and accumulate. That calculation multiplies values from inputs according to various weights, and then it sums all those values up. Two fundamental laws of electrical engineering can basically do that, too. Ohm’s Law multiplies voltage and conductance to give current, and Kirchoff’s Current Law sums the currents entering a point. By storing a neural network’s weights in resistive memory devices, such as memristors, multiply-and-accumulate can happen completely in analog, potentially reducing power consumption by orders of magnitude.

The reason analog AI systems can’t train themselves today has a lot to do with the variability of their components. Just like real neurons, those in analog neural networks don’t all behave exactly alike. To do back propagation with analog components, you must build two separate circuit pathways. One going forward to come up with an answer (called inferencing), the other going backward to do the learning so that the answer becomes more accurate. But because of the variability of analog components, the pathways don't match up.

“You end up accumulating error as you go backwards through the network,” says Bengio. To compensate, a network would need lots of power-hungry analog-to-digital and digital-to-analog circuits, defeating the point of going analog.

Equilibrium propagation allows learning and inferencing to happen on the same network, partly by adjusting the behavior of the network as a whole. “What [equilibrium propagation] allows us to do is to say how we should modify each of these devices so that the overall circuit performs the right thing,” he says. “We turn the physical computation that is happening in the analog devices directly to our advantage.”

Right now, equilibrium propagation is only working in simulation. But Rain plans to have a hardware proof-of-principle in late 2021, according to CEO and cofounder Gordon Wilson. “We are really trying to fundamentally reimagine the hardware computational substrate for artificial intelligence, find the right clues from the brain, and use those to inform the design of this,” he says. The result could be what they call end-to-end analog AI systems that capable of running sophisticated robots or even playing a role in data centers. Both of those are currently considered beyond the capabilities of analog AI, which is now focused only on adding inferencing abilities to sensors and other low-power “edge” devices, while leaving the learning to GPUs. Continue reading

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