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The minute details of rogue drone’s movements in the air may unwittingly reveal the drone pilot’s location—possibly enabling authorities to bring the drone down before, say, it has the opportunity to disrupt air traffic or cause an accident. And it’s possible without requiring expensive arrays of radio triangulation and signal-location antennas.

So says a team of Israeli researchers who have trained an AI drone-tracking algorithm to reveal the drone operator’s whereabouts, with a better than 80 per cent accuracy level. They are now investigating whether the algorithm can also uncover the pilot’s level of expertise and even possibly their identity.

Gera Weiss—professor of computer science at Ben-Gurion University of the Negev in Beersheba, Israel—said the algorithm his team has developed partly relies on the specific terrain around an airport or other high-security location.

After testing neural nets including dense networks and convolutional neural networks, the researchers found that a kind of recurrent neural net called a “gated-recurrent unit” (GRU) network worked best for drone tracking. “Recurrent networks are good at this,” Weiss said. “They consider the sequenced reality of the data—not just in space but in time.”

So, he said, a security professional at an airport, for instance, would hire white-hat malefactors to launch a drone from various locations around the airport. The security team would then record the drone’s exact movements on airport radar systems.

Ultimately, the GRU algorithm would then train on this data—knowing in this case the pilot’s location and the peculiar details of the drone’s flight patterns.

Depending on the specific terrain at any given airport, a pilot operating a drone near a camouflaging patch of forest, for instance, might have an unobstructed view of the runway. But that location might also be a long distance away, possibly making the operator more prone to errors in precise tracking of the drone. Whereas a pilot operating nearer to the runway may not make those same tracking errors but may also have to contend with big blind spots because of their proximity to, say, a parking garage or control tower.

And in every case, he said, simple geometry could begin to reveal important clues about a pilot’s location, too. When a drone is far enough away, motion along a pilot’s line of sight can be harder for the pilot to detect than motion perpendicular to their line of sight. This also could become a significant factor in an AI algorithm working to discover pilot location from a particular drone flight pattern.

The sum total of these various terrain-specific and terrain-agnostic effects, then, could be a giant finger pointing to the operator. This AI application would also be unaffected by any relay towers or other signal spoofing mechanisms the pilot may have put in place.

Weiss said his group tested their drone tracking algorithm using Microsoft Research’s open source drone and autonomous vehicle simulator AirSim. The group presented their work-in-progress at the Fourth International Symposium on Cyber Security, Cryptology and Machine Learning at Ben-Gurion University earlier this month.

Their paper boasts a 73 per cent accuracy rate in discovering drone pilots’ locations. Weiss said that in the few weeks since publishing that result, they’ve now improved the accuracy rate to 83 per cent.

Now that the researchers have proved the algorithm’s concept, Weiss said, they’re hoping next to test it in real-world airport settings. “I’ve already been approached by people who have the flight permissions,” he said. “I am a university professor. I’m not a trained pilot. Now people that do have the facility to fly drones [can] run this physical experiment.”

Weiss said it’s as yet unclear how terrain-agnostic their algorithm is. Could a neural net trained on the terrain surrounding one airport then be effectively deployed at another airport—or another untrained region of the same airport?

Another open question, he said, involves whether the algorithm could also be reversed: Could drone flight patterns around an unmapped terrain then be used to discover features of the terrain?

Weiss said they hope to tackle these questions in future research, alongside possible applications of a series of recent findings that attempt to rate operator skill levels from motion tracking data.

One finding even goes so far as to pinpoint and classify idiosyncrasies in piloting skills—so perhaps repeat offenders might one day be spotted by the thumbprint their copter leaves on the sky.

We introduce Robot DE NIRO, an autonomous, collaborative, humanoid robot for mobile manipulation. We built DE NIRO to perform a wide variety of manipulation behaviors, with a focus on pick-and-place tasks. DE NIRO is designed to be used in a domestic environment, especially in support of caregivers working with the elderly. Given this design focus, DE NIRO can interact naturally, reliably, and safely with humans, autonomously navigate through environments on command, intelligently retrieve or move target objects, and avoid collisions efficiently. We describe DE NIRO's hardware and software, including an extensive vision sensor suite of 2D and 3D LIDARs, a depth camera, and a 360-degree camera rig; two types of custom grippers; and a custom-built exoskeleton called DE VITO. We demonstrate DE NIRO's manipulation capabilities in three illustrative challenges: First, we have DE NIRO perform a fetch-an-object challenge. Next, we add more cognition to DE NIRO's object recognition and grasping abilities, confronting it with small objects of unknown shape. Finally, we extend DE NIRO's capabilities into dual-arm manipulation of larger objects. We put particular emphasis on the features that enable DE NIRO to interact safely and naturally with humans. Our contribution is in sharing how a humanoid robot with complex capabilities can be designed and built quickly with off-the-shelf hardware and open-source software. Supplementary Material including our code, a documentation, videos and the CAD models of several hardware parts are openly available at https://www.imperial.ac.uk/robot-intelligence/software/.

Robots! Robots! Robots! This collection of fun activity sheets for kids is a perfect introduction to the amazing world of robots.

The activities are meant to be intuitive and clear, with little direction needed so that it’s easy for most kids to do the work independently. We think these activities can be enjoyed by kids age 6 to 12, though older kids (and adults!) may want to explore some of them, too.

The sheets can be printed out or done on a computer or tablet. Some activities require that kids use IEEE Spectrum’s Robots Guide to learn more about some of the robots; other activities, however, don’t require access to the site.

⇒ Get the activity sheets here!

This material was created by Spectrum and is free. If you’d like to support this project, please consider making a donation through the IEEE Foundation. We’re regularly updating the Robots Guide, and hope to keep improving the activity sheets too. Have fun with robots!

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.

Recent work suggests that collective computation of social structure can minimize uncertainty about the social and physical environment, facilitating adaptation. We explore these ideas by studying how fission-fusion social structure arises in spider monkey (Ateles geoffroyi) groups, exploring whether monkeys use social knowledge to collectively compute subgroup size distributions adaptive for foraging in variable environments. We assess whether individual decisions to stay in or leave subgroups are conditioned on strategies based on the presence or absence of others. We search for this evidence in a time series of subgroup membership. We find that individuals have multiple strategies, suggesting that the social knowledge of different individuals is important. These stay-leave strategies provide microscopic inputs to a stochastic model of collective computation encoded in a family of circuits. Each circuit represents an hypothesis for how collectives combine strategies to make decisions, and how these produce various subgroup size distributions. By running these circuits forward in simulation we generate new subgroup size distributions and measure how well they match food abundance in the environment using transfer entropies. We find that spider monkeys decide to stay or go using information from multiple individuals and that they can collectively compute a distribution of subgroup size that makes efficient use of ephemeral sources of nutrition. We are able to artificially tune circuits with subgroup size distributions that are a better fit to the environment than the observed. This suggests that a combination of measurement error, constraint, and adaptive lag are diminishing the power of collective computation in this system. These results are relevant for a more general understanding of the emergence of ordered states in multi-scale social systems with adaptive properties–both natural and engineered.

Modeling of soft robots is typically performed at the static level or at a second-order fully dynamic level. Controllers developed upon these models have several advantages and disadvantages. Static controllers, based on the kinematic relations tend to be the easiest to develop, but by sacrificing accuracy, efficiency and the natural dynamics. Controllers developed using second-order dynamic models tend to be computationally expensive, but allow optimal control. Here we propose that the dynamic model of a soft robot can be reduced to first-order dynamical equation owing to their high damping and low inertial properties, as typically observed in nature, with minimal loss in accuracy. This paper investigates the validity of this assumption and the advantages it provides to the modeling and control of soft robots. Our results demonstrate that this model approximation is a powerful tool for developing closed-loop task-space dynamic controllers for soft robots by simplifying the planning and sensory feedback process with minimal effects on the controller accuracy.

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 ]

Multimodal integration is an important process in perceptual decision-making. In humans, this process has often been shown to be statistically optimal, or near optimal: sensory information is combined in a fashion that minimizes the average error in perceptual representation of stimuli. However, sometimes there are costs that come with the optimization, manifesting as illusory percepts. We review audio-visual facilitations and illusions that are products of multisensory integration, and the computational models that account for these phenomena. In particular, the same optimal computational model can lead to illusory percepts, and we suggest that more studies should be needed to detect and mitigate these illusions, as artifacts in artificial cognitive systems. We provide cautionary considerations when designing artificial cognitive systems with the view of avoiding such artifacts. Finally, we suggest avenues of research toward solutions to potential pitfalls in system design. We conclude that detailed understanding of multisensory integration and the mechanisms behind audio-visual illusions can benefit the design of artificial cognitive systems.

As if it’s not hard enough to make very small mobile robots, once you’ve gotten the power and autonomy all figured out (good luck with that), your robot isn’t going to be all that useful unless it can carry some payload. And the payload that everybody wants robots to carry is a camera, which is of course a relatively big, heavy, power hungry payload. Great, just great.

This whole thing is frustrating because tiny, lightweight, power efficient vision systems are all around us. Literally, all around us right this second, stuffed into the heads of insects. We can’t make anything quite that brilliant (yet), but roboticists from the University of Washington, in Seattle, have gotten us a bit closer, with the smallest wireless, steerable video camera we’ve ever seen—small enough to fit on the back of a microbot, or even a live bug.

To make a camera this small, the UW researchers, led by Shyam Gollakota, a professor of computer science and engineering, had to start nearly from scratch, primarily because existing systems aren’t nearly so constrained by power availability. Even things like swallowable pill cameras require batteries that weigh more than a gram, but only power the camera for under half an hour. With a focus on small size and efficiency, they started with an off-the-shelf ultra low-power image sensor that’s 2.3 mm wide and weighs 6.7 mg. They stuck on a Bluetooth 5.0 chip (3 mm wide, 6.8 mg), and had a fun time connecting those two things together without any intermediary hardware to broadcast the camera output. A functional wireless camera also requires a lens (20 mg) and an antenna, which is just 5 mm of wire. An accelerometer is useful so that insect motion can be used to trigger the camera, minimizing the redundant frames that you’d get from a robot or an insect taking a nap.

Photo: University of Washington The microcamera developed by the UW researchers can stream monochrome video at up to 5 frames per second to a cellphone 120 meters away. 

The last bit to make up this system is a mechanically steerable “head,” weighing 35 mg and bringing the total weight of the wireless camera system to 84 mg. If the look of the little piezoelectric actuator seems familiar, you have very good eyes because it’s tiny, and also, it’s the same kind of piezoelectric actuator that the folks at UW use to power their itty bitty flying robots. It’s got a 60-degree panning range, but also requires a 96 mg boost converter to function, which is a huge investment in size and weight just to be able to point the camera a little bit. But overall, the researchers say that this pays off, because not having to turn the entire robot (or insect) when you want to look around reduces the energy consumption of the system as a whole by a factor of up to 84 (!). 

Photo: University of Washington Insects are very mobile platforms for outdoor use, but they’re also not easy to steer, so the researchers also built a little insect-scale robot that they could remotely control while watching the camera feed. As it turns out, this seems to be the smallest, power-autonomous terrestrial robot with a camera ever made.

This efficiency means that the wireless camera system can stream video frames (160x120 pixels monochrome) to a cell phone up to 120 meters away for up to 6 hours when powered by a 0.5-g, 10-mAh battery. A live, first-bug view can be streamed at up to 5 frames per second. The system was successfully tested on a pair of darkling beetles that were allowed to roam freely outdoors, and the researchers noted that they could also mount it on spiders or moths, or anything else that could handle the payload. (The researchers removed the electronics from the insects after the experiments and observed no noticeable adverse effects on their behavior.)

The researchers are already thinking about what it might take to put a wireless camera system on something that flies, and it’s not going to be easy—a bumblebee can only carry between 100 and 200 mg. The power system is the primary limitation here, but it might be possible to use a solar cell to cut down on battery requirements. And the camera itself could be scaled down as well, by using a completely custom sensor and a different type of lens. The other thing to consider is that with a long-range wireless link and a vision system, it’s possible to add sophisticated vision-based autonomy to tiny robots by doing the computation remotely. So, next time you see something scuttling across the ground, give it another look, because it might be looking right back at you.

“Wireless steerable vision for live insects and insect-scale robots,” by Vikram Iyer, Ali Najafi, Johannes James, Sawyer Fuller, and Shyamnath Gollakota from the University of Washington, is published in Science Robotics.

Over the last 10 years, the PR2 has helped roboticists make an enormous amount of progress in mobile manipulation over a relatively short time. I mean, it’s been a decade already, but still—robots are hard, and giving a bunch of smart people access to a capable platform where they didn’t have to worry about hardware and could instead focus on doing interesting and useful things helped to establish a precedent for robotics research going forward.

Unfortunately, not everyone can afford an enormous US $400,000 robot, and even if they could, PR2s are getting very close to the end of their lives. There are other mobile manipulators out there taking the place of the PR2, but so far, size and cost have largely restricted them to research labs. Lots of good research is being done, but it’s getting to the point where folks want to take the next step: making mobile manipulators real-world useful.

Today, a company called Hello Robot is announcing a new mobile manipulator called the Stretch RE1. With offices in the San Francisco Bay Area and in Atlanta, Ga., Hello Robot is led by Aaron Edsinger and Charlie Kemp, and by combining decades of experience in industry and academia they’ve managed to come up with a robot that’s small, lightweight, capable, and affordable, all at the same time. For now, it’s a research platform, but eventually, its creators hope that it will be able to come into our homes and take care of us when we need it to.

A fresh look at mobile manipulators

To understand the concept behind Stretch, it’s worth taking a brief look back at what Edsinger and Kemp have been up to for the past 10 years. Edsinger co-founded Meka Robotics in 2007, which built expensive, high performance humanoid arms, torsos, and heads for the research market. Meka was notable for being the first robotics company (as far as we know) to sell robot arms that used series elastic actuators, and the company worked extensively with Georgia Tech researchers. In 2011, Edsinger was one of the co-founders of Redwood Robotics (along with folks from SRI and Willow Garage), which was going to develop some kind of secret and amazing new robot arm before Google swallowed it in late 2013. At the same time, Google also acquired Meka and a bunch of other robotics companies, and Edsinger ended up at Google as one of the directors of its robotics program, until he left to co-found Hello Robot in 2017.

Meanwhile, since 2007 Kemp has been a robotics professor at Georgia Tech, where he runs the Healthcare Robotics Lab. Kemp’s lab was one of the 11 PR2 beta sites, giving him early experience with a ginormous mobile manipulator. Much of the research that Kemp has spent the last decade on involves robots providing assistance to untrained users, often through direct physical contact, and frequently either in their own homes or in a home environment. We should mention that the Georgia Tech PR2 is still going, most recently doing some clever material classification work in a paper for IROS later this year.

Photo: Hello Robot Hello Robot co-founder and CEO Aaron Edsinger says that, although Stretch is currently a research platform, he hopes to see the robot deployed in home environments, adding that the “impact we want to have is through robots that are helpful to people in society.”

So with all that in mind, where’d Hello Robot come from? As it turns out, both Edsinger and Kemp were in Rodney Brooks’ group at MIT, so it’s perhaps not surprising that they share some of the same philosophies about what robots should be and what they should be used for. After collaborating on a variety of projects over the years, in 2017 Edsinger was thinking about his next step after Google when Kemp stopped by to show off some video of a new robot prototype that he’d been working on—the prototype for Stretch. “As soon as I saw it, I knew that was exactly the kind of thing I wanted to be working on,” Edsinger told us. “I’d become frustrated with the complexity of the robots being built to do manipulation in home environments and around people, and it solved a lot of problems in an elegant way.” 

For Kemp, Stretch is an attempt to get everything he’s been teaching his robots out of his lab at Georgia Tech and into the world where it can actually be helpful to people. “Right from the beginning, we were trying to take our robots out to real homes and interact with real people,” says Kemp. Georgia Tech’s PR2, for example, worked extensively with Henry and Jane Evans, helping Henry (a quadriplegic) regain some of the bodily autonomy he had lost. With the assistance of the PR2, Henry was able to keep himself comfortable for hours without needing a human caregiver to be constantly with him. “I felt like I was making a commitment in some ways to some of the people I was working with,” Kemp told us. “But 10 years later, I was like, where are these things? I found that incredibly frustrating. Stretch is an effort to try to push things forward.”

A robot you can put in the backseat of a car

One way to put Stretch in context is to think of it almost as a reaction to the kitchen sink philosophy of the PR2. Where the PR2 was designed to be all the robot anyone could ever need (plus plenty of robot that nobody really needed) embodied in a piece of hardware that weighs 225 kilograms and cost nearly half a million dollars, Stretch is completely focused on being just the robot that is actually necessary in a form factor that’s both much smaller and affordable. The entire robot weighs a mere 23 kg in a footprint that’s just a 34 cm square. As you can see from the video, it’s small enough (and safe enough) that it can be moved by a child. The cost? At $17,950 apiece—or a bit less if you buy a bunch at once—Stretch costs a fraction of what other mobile manipulators sell for.

It might not seem like size or weight should be that big of an issue, but it very much is, explains Maya Cakmak, a robotics professor at the University of Washington, in Seattle. Cakmak worked with PR2 and Henry Evans when she was at Willow Garage, and currently has access to both a PR2 and a Fetch research robot. “When I think about my long term research vision, I want to deploy service robots in real homes,” Cakmak told us. Unfortunately, it’s the robots themselves that have been preventing her from doing this—both the Fetch and the PR2 are large enough that moving them anywhere requires a truck and a lift, which also limits the home that they can be used in. “For me, I felt immediately that Stretch is very different, and it makes a lot of sense,” she says. “It’s safe and lightweight, you can probably put it in the backseat of a car.” For Cakmak, Stretch’s size is the difference between being able to easily take a robot to the places she wants to do research in, and not. And cost is a factor as well, since a cheaper robot means more access for her students. “I got my refurbished PR2 for $180,000,” Cakmak says. “For that, with Stretch I could have 10!”

“I felt immediately that Stretch is very different. It’s safe and lightweight, you can probably put it in the backseat of a car. I got my refurbished PR2 for $180,000. For that, with Stretch I could have 10!” —Maya Cakmak, University of Washington

Of course, a portable robot doesn’t do you any good if the robot itself isn’t sophisticated enough to do what you need it to do. Stretch is certainly a compromise in functionality in the interest of small size and low cost, but it’s a compromise that’s been carefully thought out, based on the experience that Edsinger has building robots and the experience that Kemp has operating robots in homes. For example, most mobile manipulators are essentially multi-degrees-of-freedom arms on mobile bases. Stretch instead leverages its wheeled base to move its arm in the horizontal plane, which (most of the time) works just as well as an extra DoF or two on the arm while saving substantially on weight and cost. Similarly, Stretch relies almost entirely on one sensor, an Intel RealSense D435i on a pan-tilt head that gives it a huge range of motion. The RealSense serves as a navigation camera, manipulation camera, a 3D mapping system, and more. It’s not going to be quite as good for a task that might involve fine manipulation, but most of the time it’s totally workable and you’re saving on cost and complexity. 

Stretch has been relentlessly optimized to be the absolutely minimum robot to do mobile manipulation in a home or workplace environment. In practice, this meant figuring out exactly what it was absolutely necessary for Stretch to be able to do. With an emphasis on manipulation, that meant defining the workspace of the robot, or what areas it’s able to usefully reach. “That was one thing we really had to push hard on,” says Edsinger. “Reachability.” He explains that reachability and a small mobile base tend not to go together, because robot arms (which tend to weigh a lot) can cause a small base to tip, especially if they’re moving while holding a payload. At the same time, Stretch needed to be able to access both countertops and the floor, while being able to reach out far enough to hand people things without having to be right next to them. To come up with something that could meet all those requirements, Edsinger and Kemp set out to reinvent the robot arm.

Stretch’s key innovation: a stretchable arm

The design they came up with is rather ingenious in its simplicity and how well it works. Edsinger explains that the arm consists of five telescoping links: one fixed and four moving. They are constructed of custom carbon fiber, and are driven by a single motor, which is attached to the robot’s vertical pole. The strong, lightweight structure allows the arm to extend over half a meter and hold up to 1.5 kg. Although the company has a patent pending for the design, Edsinger declined to say whether the links are driven by a belt, cables, or gears. “We don’t want to disclose too much of the secret sauce [with regard to] the drive mechanism.” He added that the arm was “one of the most significant engineering challenges on the robot in terms of getting the desired reach, compactness, precision, smoothness, force sensitivity, and low cost to all happily coexist.”

Photo: Hello Robot Stretch’s arm consists of five telescoping links constructed of custom carbon fiber, and are driven by a single motor, which is attached to the robot’s vertical pole, minimizing weight and inertia. The arm has a reach of over half a meter and can hold up to 1.5 kg.

Another interesting features of Stretch is its interface with the world—its gripper. There are countless different gripper designs out there, each and every one of which is the best at gripping some particular subset of things. But making a generalized gripper for all of the stuff that you’d find in a home is exceptionally difficult. Ideally, you’d want some sort of massive experimental test program where thousands and thousands of people test out different gripper designs in their homes for long periods of time and then tell you which ones work best. Obviously, that’s impractical for a robotics startup, but Kemp realized that someone else was already running the study for him: Amazon.

“I had this idea that there are these assistive grabbers that people with disabilities use to grasp objects in the real world,” he told us. Kemp went on Amazon’s website and looked at the top 10 grabbers and the reviews from thousands of users. He then bought a bunch of different ones and started testing them. “This one [Stretch’s gripper], I almost didn’t order it, it was such a weird looking thing,” he says. “But it had great reviews on Amazon, and oh my gosh, it just blew away the other grabbers. And I was like, that’s it. It just works.”

Stretch’s teleoperated and autonomous capabilities

As with any robot intended to be useful outside of a structured environment, hardware is only part of the story, and arguably not even the most important part. In order for Stretch to be able to operate out from under the supervision of a skilled roboticist, it has to be either easy to control, or autonomous. Ideally, it’s both, and that’s what Hello Robot is working towards, although things didn’t start out that way, Kemp explains. “From a minimalist standpoint, we began with the notion that this would be a teleoperated robot. But in the end, you just don’t get the real power of the robot that way, because you’re tied to a person doing stuff. As much as we fought it, autonomy really is a big part of the future for this kind of system.”

Here’s a look at some of Stretch’s teleoperated capabilities. We’re told that Stretch is very easy to get going right out of the box, although this teleoperation video from Hello Robot looks like it’s got a skilled and experienced user in the loop:

For such a low-cost platform, the autonomy (even at this early stage) is particularly impressive:

Since it’s not entirely clear from the video exactly what’s autonomous, here’s a brief summary of a couple of the more complex behaviors that Kemp sent us:

  • Object grasping: Stretch uses its 3D camera to find the nearest flat surface using a virtual overhead view. It then segments significant blobs on top of the surface. It selects the largest blob in this virtual overhead view and fits an ellipse to it. It then generates a grasp plan that makes use of the center of the ellipse and the major and minor axes. Once it has a plan, Stretch orients its gripper, moves to the pre-grasp pose, moves to the grasp pose, closes its gripper based on the estimated object width, lifts up, and retracts.
  • Mapping, navigating, and reaching to a 3D point: These demonstrations all use FUNMAP (Fast Unified Navigation, Manipulation and Planning). It’s all novel custom Python code. Even a single head scan performed by panning the 3D camera around can result in a very nice 3D representation of Stretch’s surroundings that includes the nearby floor. This is surprisingly unusual for robots, which often have their cameras too low to see many interesting things in a human environment. While mapping, Stretch selects where to scan next in a non-trivial way that considers factors such as the quality of previous observations, expected new observations, and navigation distance. The plan that Stretch uses to reach the target 3D point has been optimized for navigation and manipulation. For example, it finds a final robot pose that provides a large manipulation workspace for Stretch, which must consider nearby obstacles, including obstacles on the ground. 
  • Object handover: This is a simple demonstration of object handovers. Stretch performs Cartesian motions to move its gripper to a body-relative position using a good motion heuristic, which is to extend the arm as the last step. These simple motions work well due to the design of Stretch. It still surprises me how well it moves the object to comfortable places near my body, and how unobtrusive it is. The goal point is specified relative to a 3D frame attached to the person’s mouth estimated using deep learning models (shown in the RViz visualization video). Specifically, Stretch targets handoff at a 3D point that is 20 cm below the estimated position of the mouth and 25 cm away along the direction of reaching.

Much of these autonomous capabilities come directly from Kemp’s lab, and the demo code is available for anyone to use. (Hello Robot says all of Stretch’s software is open source.)

Photo: Hello Robot Hello Robot co-founder and CEO Aaron Edsinger says Stretch is designed to work with people in homes and workplaces and can be teleoperated to do a variety of tasks, including picking up toys, removing laundry from a dryer, and playing games with kids.

As of right now, Stretch is very much a research platform. You’re going to see it in research labs doing research things, and hopefully in homes and commercial spaces as well, but still under the supervision of professional roboticists. As you may have guessed, though, Hello Robot’s vision is a bit broader than that. “The impact we want to have is through robots that are helpful to people in society,” Edsinger says. “We think primarily in the home context, but it could be in healthcare, or in other places. But we really want to have our robots be impactful, and useful. To us, useful is exciting.” Adds Kemp: “I have a personal bias, but we’d really like this technology to benefit older adults and caregivers. Rather than creating a specialized assistive device, we want to eventually create an inexpensive consumer device for everyone that does lots of things.”

Neither Edsinger nor Kemp would say much more on this for now, and they were very explicit about why—they’re being deliberately cautious about raising expectations, having seen what’s happened to some other robotics companies over the past few years. Without VC funding (Hello Robot is currently bootstrapping itself into existence), Stretch is being sold entirely on its own merits. So far, it seems to be working. Stretch robots are already in a half dozen research labs, and we expect that with today’s announcement, we’ll start seeing them much more frequently.

This article appears in the October 2020 print issue as “A Robot That Keeps It Simple.”

Plants are movers, but the nature of their movement differs dramatically from that of creatures that move their whole body from point A to point B. Plants grow to where they are going. Bio-inspired robotics sometimes emulates plants' growth-based movement; but growing is part of a broader system of movement guidance and control. We argue that ecological psychology's conception of “information” and “control” can simultaneously make sense of what it means for a plant to navigate its environment and provide a control scheme for the design of ecological plant-inspired robotics. In this effort, we will outline several control laws and give special consideration to the class of control laws identified by tau theory, such as time to contact.

Complex maritime missions, both above and below the surface, have traditionally been carried out by manned surface ships and submarines equipped with advanced sensor systems. Unmanned Maritime Vehicles (UMVs) are increasingly demonstrating their potential for improving existing naval capabilities due to their rapid deployability, easy scalability, and high reconfigurability, offering a reduction in both operational time and cost. In addition, they mitigate the risk to personnel by leaving the man far-from-the-risk but in-the-loop of decision making. In the long-term, a clear interoperability framework between unmanned systems, human operators, and legacy platforms will be crucial for effective joint operations planning and execution. However, the present multi-vendor multi-protocol solutions in multi-domain UMVs activities are hard to interoperate without common mission control interfaces and communication protocol schemes. Furthermore, the underwater domain presents significant challenges that cannot be satisfied with the solutions developed for terrestrial networks. In this paper, the interoperability topic is discussed blending a review of the technological growth from 2000 onwards with recent authors' in-field experience; finally, important research directions for the future are given. Within the broad framework of interoperability in general, the paper focuses on the aspect of interoperability among UMVs not neglecting the role of the human operator in the loop. The picture emerging from the review demonstrates that interoperability is currently receiving a high level of attention with a great and diverse deal of effort. Besides, the manuscript describes the experience from a sea trial exercise, where interoperability has been demonstrated by integrating heterogeneous autonomous UMVs into the NATO Centre for Maritime Research and Experimentation (CMRE) network, using different robotic middlewares and acoustic modem technologies to implement a multistatic active sonar system. A perspective for the interoperability in marine robotics missions emerges in the paper, through a discussion of current capabilities, in-field experience and future advanced technologies unique to UMVs. Nonetheless, their application spread is slowed down by the lack of human confidence. In fact, an interoperable system-of-systems of autonomous UMVs will require operators involved only at a supervisory level. As trust develops, endorsed by stable and mature interoperability, human monitoring will be diminished to exploit the tremendous potential of fully autonomous UMVs.

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.

Evidently, the folks at Unitree were paying attention to last week’s Video Friday.

[ Unitree ]

RoboSoft 2020 was a virtual conference this year (along with everything else), but they still held a soft robots contest, and here are four short vids—you can watch the rest of them here.

RoboSoft 2020 ]

If you were wondering why SoftBank bought Aldebaran Robotics and Boston Dynamics, here’s the answer.

I am now a Hawks fan. GO HAWKS!

[ Softbank Hawks ] via [ RobotStart ]

Scientists at the University of Liverpool have developed a fully autonomous mobile robot to assist them in their research. Using a type of AI, the robot has been designed to work uninterrupted for weeks at a time, allowing it to analyse data and make decisions on what to do next. Using a flexible arm with customised gripper it can be calibrated to interact with most standard lab equipment and machinery as well as navigate safely around human co-workers and obstacles.

[ Nature ]

Oregon State’s Cassie has been on break for a couple of months, but it’s back in the lab and moving alarmingly quickly.

[ DRL ]

The current situation linked to COVID-19 sadly led to the postponing of this year RoboCup 2020 at Bordeaux. As an official sponsor of The RoboCup, SoftBank Robotics wanted to take this opportunity to thank all RoboCupers and The RoboCup Federation for their support these past 13 years. We invite you to take a look at NAO’s adventure at The RoboCup as the official robot of the Standard Platform League. See you in Bordeaux 2021!

[ RoboCup 2021 ]

Miniature SAW robot crawling inside the intestines of a pig. You’re welcome.

[ Zarrouk Lab ]

The video demonstrates fast autonomous flight experiments in cluttered unknown environments, with the support of a robust and perception-aware replanning framework called RAPTOR. The associated paper is submitted to TRO.


Since we haven’t gotten autonomy quite right yet, there’s a lot of telepresence going on for robots that operate in public spaces. Usually, you’ve got one remote human managing multiple robots, so it would be nice to make that interface a little more friendly, right?

[ HCI Lab ]

Arguable whether or not this is a robot, but it’s cool enough to spend a minute watching.

[ Ishikawa Lab ]

Communication is critical to collaboration; however, too much of it can degrade performance. Motivated by the need for effective use of a robot’s communication modalities, in this work, we present a computational framework that decides if, when, and what to communicate during human-robot collaboration.

[ Interactive Robotics ]

Robotiq has released the next generation of the grippers for collaborative robots: the 2F-85 and 2F-140. Both models gain greater robustness, safety, and customizability while retaining the same key benefits that have inspired thousands of manufacturers to choose them since their launch 6 years ago.

[ Robotiq ]

ANYmal C, the autonomous legged robot designed for industrial challenging environments, provides the mobility, autonomy and inspection intelligence to enable safe and efficient inspection operations. In this virtual showcase, discover how ANYmal climbs stairs, recovers from a fall, performs an autonomous mission and avoids obstacles, docks to charge by itself, digitizes analogue sensors and monitors the environment.

[ ANYbotics ]

At Waymo, we are committed to addressing inequality, and we believe listening is a critical first step toward driving positive change. Earlier this year, five Waymonauts sat down to share their thoughts on equity at work, challenging the status quo, and more. This is what they had to say.

[ Waymo ]

Nice of ABB to take in old robots and upgrade them to turn them into new robots again. Robots forever!

[ ABB ]

It’s nice seeing the progress being made by GITAI, one of the teams competing in the ANA Avatar XPRIZE Challenge, and also meet the humans behind the robots.

[ GITAI ] via [ XPRIZE ]

One more talk from the ICRA Legged Robotics Workshop: Jingyu Liu from DeepRobotics and Qiuguo Zhu from Zhejiang University.

[ Deep Robotics ]

Can artificial intelligence help the search for life elsewhere in the solar system? NASA thinks the answer may be “yes”—and not just on Mars either.

A pilot AI system is now being tested for use on the ExoMars mission that is currently slated to launch in the summer or fall of 2022. The machine-learning algorithms being developed will help science teams decide how to test Martian soil samples to return only the most meaningful data.

For ExoMars, the AI system will only be used back on earth to analyze data gather by the ExoMars rover. But if the system proves to be as useful to the rovers as now suspected, a NASA mission to Saturn’s moon Titan (now scheduled for 2026 launch) could automate the scientific sleuthing process in the field. This mission will rely on the Dragonfly octocopter drone to fly from surface location to surface location through Titan’s dense atmosphere and drill for signs of life there.

The hunt for microbial life in another world’s soil, either as fossilized remnants or as present-day samples, is very challenging, says Eric Lyness, software lead of the NASA Goddard Planetary Environments Lab in Greenbelt, Md. There is of course no precedent to draw upon, because no one has yet succeeded in astrobiology’s holy grail quest.

But that doesn’t mean AI can’t provide substantial assistance. Lyness explained that for the past few years he’d been puzzling over how to automate portions of an exploratory mission’s geochemical investigation, wherever in the solar system the scientific craft may be.

Last year he decided to try machine learning. “So we got some interns,” he said. “People right out of college or in college, who have been studying machine learning. … And they did some amazing stuff. It turned into much more than we expected.” Lyness and his collaborators presented their scientific analysis algorithm at a geochemistry conference last month.

Illustration: ESA The ExoMars rover, named Rosalind Franklin, will be the first that can drill down to 2-meter depths, where living soil bacteria could possibly be found.

ExoMars’s rover—named Rosalind Franklin, after one of the co-discoverers of DNA—will be the first that can drill down to 2-meter depths, beyond where solar UV light might penetrate and kill any life forms. In other words, ExoMars will be the first Martian craft with the ability to reach soil depths where living soil bacteria could possibly be found.

“We could potentially find forms of life, microbes or other things like that,” Lyness said. However, he quickly added, very little conclusive evidence today exists to suggest that there’s present-day (microbial) life on Mars. (NASA’s Curiosity rover has sent back some inexplicable observations of both methane and molecular oxygen in the Martian atmosphere that could conceivably be a sign of microbial life forms, though non-biological processes could explain these anomalies too.)

Less controversially, the Rosalind Franklin rover’s drill could also turn up fossilized evidence of life in the Martian soil from earlier epochs when Mars was more hospitable.

NASA’s contribution to the joint Russian/European Space Agency ExoMars project is an instrument called a mass spectrometer that will be used to analyze soil samples from the drill cores. Here, Lyness said, is where AI could really provide a helping hand.

Because the Dragonfly drone and possibly a future mission to Jupiter’s moon Europa would be operating in hostile environments with less opportunity for data transmission to Earth, automating a craft’s astrobiological exploration would be practically a requirement

The spectrometer, which studies the mass distribution of ions in a sample of material, works by blasting the drilled soil sample with a laser and then mapping out the atomic masses of the various molecules and portions of molecules that the laser has liberated. The problem is any given mass spectrum could originate from any number of source compounds, minerals and components. Which always makes analyzing a mass spectrum a gigantic puzzle.

Lyness said his group is studying the mineral montmorillonite, a commonplace component of the Martian soil, to see the many ways it might reveal itself in a mass spectrum. Then his team sneaks in an organic compound with the montmorillonite sample to see how that changes the mass spectrometer output.

“It could take a long time to really break down a spectrum and understand why you’re seeing peaks at certain [masses] in the spectrum,” he said. “So anything you can do to point scientists into a direction that says, ‘Don’t worry, I know it’s not this kind of thing or that kind of thing,’ they can more quickly identify what’s in there.”

Lyness said the ExoMars mission will provide a fertile training ground for his team’s as-yet-unnamed AI algorithm. (He said he’s open to suggestions—though, please, no spoof Boaty McBoatface submissions need apply.)

Because the Dragonfly drone and possibly a future astrobiology mission to Jupiter’s moon Europa would be operating in much more hostile environments with much less opportunity for data transmission back and forth to Earth, automating a craft’s astrobiological exploration would be practically a requirement.

All of which points to a future in mid-2030s in which a nuclear-powered octocopter on a moon of Saturn flies from location to location to drill for evidence of life on this tantalizingly bio-possible world. And machine learning will help power the science.

“We should be researching how to make the science instruments smarter,” Lyness said. “If you can make it smarter at the source, especially for planetary exploration, it has huge payoffs.”

In our study, we tested a combination of virtual reality (VR) and robotics in the original adjuvant method of post-stroke lower limb walk restoration in acute phase using a simulation with visual and tactile biofeedback based on VR immersion and physical impact to the soles of patients. The duration of adjuvant therapy was 10 daily sessions of 15 min each. The study showed the following significant rehabilitation progress in Control (N = 27) vs. Experimental (N = 35) groups, respectively: 1.56 ± 0.29 (mean ± SD) and 2.51 ± 0.31 points by Rivermead Mobility Index (p = 0.0286); 2.15 ± 0.84 and 6.29 ± 1.20 points by Fugl-Meyer Assessment Lower Extremities scale (p = 0.0127); and 6.19 ± 1.36 and 13.49 ± 2.26 points by Berg Balance scale (p = 0.0163). P-values were obtained by the Mann–Whitney U test. The simple and intuitive mechanism of rehabilitation, including through the use of sensory and semantic components, allows the therapy of a patient with diaschisis and afferent and motor aphasia. Safety of use allows one to apply the proposed method of therapy at the earliest stage of a stroke. We consider the main finding of this study that the application of rehabilitation with implicit interaction with VR environment produced by the robotics action has measurable significant influence on the restoration of the affected motor function of the lower limbs compared with standard rehabilitation therapy.

Group interactions are widely observed in nature to optimize a set of critical collective behaviors, most notably sensing and decision making in uncertain environments. Nevertheless, these interactions are commonly modeled using local (proximity) networks, in which individuals interact within a certain spatial range. Recently, other interaction topologies have been revealed to support the emergence of higher levels of scalability and rapid information exchange. One prominent example is scale-free networks. In this study, we aim to examine the impact of scale-free communication when implemented for a swarm foraging task in dynamic environments. We model dynamic (uncertain) environments in terms of changes in food density and analyze the collective response of a simulated swarm with communication topology given by either proximity or scale-free networks. Our results suggest that scale-free networks accelerate the process of building up a rapid collective response to cope with the environment changes. However, this comes at the cost of lower coherence of the collective decision. Moreover, our findings suggest that the use of scale-free networks can improve swarm performance due to two side-effects introduced by using long-range interactions and frequent network regeneration. The former is a topological consequence, while the latter is a necessity due to robot motion. These two effects lead to reduced spatial correlations of a robot's behavior with its neighborhood and to an enhanced opinion mixing, i.e., more diversified information sampling. These insights were obtained by comparing the swarm performance in presence of scale-free networks to scenarios with alternative network topologies, and proximity networks with and without packet loss.

The majority of robot arms are built out of some combination of long straight tubes and actuated joints. This isn’t surprising, since our limbs are built the same way, which was a clever and efficient bit of design. By adding more tubes and joints (or degrees of freedom), you can increase the versatility of your robot arm, but the tradeoff is that complexity, weight, and cost will increase, too.

At ICRA, researchers from Imperial College London’s REDS Lab, headed by Nicolas Rojas, introduced a design for a robot that’s built around a malleable structure rather than a rigid one, allowing you to improve how versatile the arm is without having to add extra degrees of freedom. The idea is that you’re no longer constrained to static tubes and joints but can instead reconfigure your robot to set it up exactly the way you want and easily change it whenever you feel like.

Inside of that bendable section of arm are layers and layers of mylar sheets, cut into flaps and stacked on top of one another so that each flap is overlapping or overlapped by at least 11 other flaps. The mylar is slippery enough that under most circumstances, the flaps can move smoothly against each other, letting you adjust the shape of the arm. The flaps are sealed up between latex membranes, and when air is pumped out from between the membranes, they press down on each other and turn the whole structure rigid, locking itself in whatever shape you’ve put it in.

Image: Imperial College London The malleable part of the robot consists of layers of mylar sheets, cut into flaps that can move smoothly against each other, letting you adjust the shape of the arm. The flaps are sealed up between latex membranes, and when air is pumped out from between the membranes, they press down on each other and turn the whole structure rigid, locking itself in whatever shape you’ve put it in.

The nice thing about this system is that it’s a sort of combination of a soft robot and a rigid robot—you get the flexibility (both physical and metaphorical) of a soft system, without necessarily having to deal with all of the control problems. It’s more mechanically complex than either (as hybrid systems tend to be), but you save on cost, size, and weight, and reduce the number of actuators you need, which tend to be points of failure. You do need to deal with creating and maintaining a vacuum, and the fact that the malleable arm is not totally rigid, but depending on your application, those tradeoffs could easily be worth it.

For more details, we spoke with first author Angus B. Clark via email.

IEEE Spectrum: Where did this idea come from?

Angus Clark: The idea of malleable robots came from the realization that the majority of serial robot arms have 6 or more degrees of freedom (DoF)—usually rotary joints—yet are typically performing tasks that only require 2 or 3 DoF. The idea of a robot arm that achieves flexibility and adaptation to tasks but maintains the simplicity of a low DoF system, along with the rapid development of variable stiffness continuum robots for medical applications, inspired us to develop the malleable robot concept.

What are some ways in which a malleable robot arm could provide unique advantages, and what are some potential applications that could leverage these advantages?

Malleable robots have the ability to complete multiple traditional tasks, such as pick and place or bin picking operations, without the added bulk of extra joints that are not directly used within each task, as the flexibility of the robot arm is provided by ​a malleable link instead. This results in an overall smaller form factor, including weight and footprint of the robot, as well as a lower power requirement and cost of the robot as fewer joints are needed, without sacrificing adaptability. This makes the robot ideal for scenarios where any of these factors are critical, such as in space robotics—where every kilogram saved is vital—or in rehabilitation robotics, where cost reduction may facilitate adoption, to name two examples. Moreover, the collaborative soft-robot-esque nature of malleable robots also tends towards collaborative robots in factories working safely alongside and with humans.

“The idea of malleable robots came from the realization that the majority of serial robot arms have 6 or more degrees of freedom (DoF), yet are typically performing tasks that only require 2 or 3 DoF” —Angus B. Clark, Imperial College London

Compared to a conventional rigid link between joints, what are the disadvantages of using a malleable link?

Currently the maximum stiffness of a malleable link is considerably weaker than that of an equivalent solid steel rigid link, and this is one of the key areas we are focusing research on improving as motion precision and accuracy are impacted. We have created the largest existing variable stiffness link at roughly 800 mm length and 50 mm diameter, which suits malleable robots towards small and medium size workspaces. Our current results evaluating this accuracy are good, however achieving a uniform stiffness across the entire malleable link can be problematic due to the production of wrinkles under bending in the encapsulating membrane. As demonstrated by our SCARA topology results, this can produce slight structural variations resulting in reduced accuracy.

Does the robot have any way of knowing its own shape? Potentially, could this system reconfigure itself somehow?

Currently we compute the robot topology using motion tracking, with markers placed on the joints of the robot. Using distance geometry, we are then able to obtain the forward and inverse kinematics of the robot, of which we can use to control the end effector (the gripper) of the robot. Ideally, in the future we would love to develop a system that no longer requires the use of motion tracking cameras.

As for the robot reconfiguring itself, which we call an “intrinsic malleable link,” there are many methods that have been demonstrated for controlling a continuum structure, such as using positive pressure or via tendon wires, however the ability to in real-time determine the curvature of the link, not just the joint positions, is a significant hurdle to solve. However, we hope to see future development on malleable robots work towards solving this problem.

What are you working on next?

For us, refining the kinematics of the robot to enable a robust and complete system for allowing a user to collaboratively reshape the robot, while still achieving the accuracy expected from robotic systems, is our current main goal. Malleable robots are a brand new field we have introduced, and as such provide many opportunities for development and optimization. Over the coming years, we hope to see other researchers work alongside us to solve these problems.

“Design and Workspace Characterization of Malleable Robots,” by Angus B. Clark and Nicolas Rojas from Imperial College London, was presented at ICRA 2020. < Back to IEEE Journal Watch

Bioinspired and biomimetic soft machines rely on functions and working principles that have been abstracted from biology but that have evolved over 3.5 billion years. So far, few examples from the huge pool of natural models have been examined and transferred to technical applications. Like living organisms, subsequent generations of soft machines will autonomously respond, sense, and adapt to the environment. Plants as concept generators remain relatively unexplored in biomimetic approaches to robotics and related technologies, despite being able to grow, and continuously adapt in response to environmental stimuli. In this research review, we highlight recent developments in plant-inspired soft machine systems based on movement principles. We focus on inspirations taken from fast active movements in the carnivorous Venus flytrap (Dionaea muscipula) and compare current developments in artificial Venus flytraps with their biological role model. The advantages and disadvantages of current systems are also analyzed and discussed, and a new state-of-the-art autonomous system is derived. Incorporation of the basic structural and functional principles of the Venus flytrap into novel autonomous applications in the field of robotics not only will inspire further plant-inspired biomimetic developments but might also advance contemporary plant-inspired robots, leading to fully autonomous systems utilizing bioinspired working concepts.

Extracting information from noisy signals is of fundamental importance for both biological and artificial perceptual systems. To provide tractable solutions to this challenge, the fields of human perception and machine signal processing (SP) have developed powerful computational models, including Bayesian probabilistic models. However, little true integration between these fields exists in their applications of the probabilistic models for solving analogous problems, such as noise reduction, signal enhancement, and source separation. In this mini review, we briefly introduce and compare selective applications of probabilistic models in machine SP and human psychophysics. We focus on audio and audio-visual processing, using examples of speech enhancement, automatic speech recognition, audio-visual cue integration, source separation, and causal inference to illustrate the basic principles of the probabilistic approach. Our goal is to identify commonalities between probabilistic models addressing brain processes and those aiming at building intelligent machines. These commonalities could constitute the closest points for interdisciplinary convergence.