Tag Archives: topics

#439000 Can AI Stop People From Believing Fake ...

Posted on March 19, 2021 by Android

Machine learning algorithms provide a way to detect misinformation based on writing style and how articles are shared.

On topics as varied as climate change and the safety of vaccines, you will find a wave of misinformation all over social media. Trust in conventional news sources may seem lower than ever, but researchers are working on ways to give people more insight on whether they can believe what they read. Researchers have been testing artificial intelligence (AI) tools that could help filter legitimate news. But how trustworthy is AI when it comes to stopping the spread of misinformation?

Researchers at the Rensselaer Polytechnic Institute (RPI) and the University of Tennessee collaborated to study the role of AI in helping people identify whether the news they’re reading is legitimate or not.

The research paper, “Tailoring Heuristics and Timing AI Interventions for Supporting News Veracity Assessments,” was published in Computers in Human Behavior Reports. It discussed how crowdsourcing marketplace Amazon Mechanical Turk (AMT) can be used to identify misinformation for fresh news and specific heuristics, which are rules of thumb used to process information and consider its veracity. In other words, heuristics are essentially “shortcuts for decisions,” explained Dorit Nevo, an associate professor at RPI’s Lally School of Management and a lead author for the paper.

The study found that AI would be successful in flagging false stories only if the reader did not already have an opinion on the topic, Nevo said. When study subjects were set in their beliefs, confirmation bias kept them from reassessing their views.

Nevo said the first part of the project focused on whether subjects could detect misinformation around climate change and vaccines like the one designed to prevent chicken pox. Then, beginning in April 2020, her team studied how people responded to news related to COVID-19.

“With COVID-19, there was a significant difference,” Nevo said. They found that about 72 percent of respondents could identify misinformation about the coronavirus without heuristic clues, and roughly 93 percent were able to be convinced by the researcher’s heuristics that the content was fake.

Examples of heuristic clues include text with too many capital letters or the use of strong language, Nevo said.

There were two types of heuristics mentioned in the team’s paper: objective heuristics and source heuristics. They put a statement at the top of each article the subjects read; it instructed them to read the article and indicate whether they believed its central thesis.

“We either put a statement that says the AI finds this article reliable and accurate based on the objective heuristics, or we said the AI finds the source reliable,” Nevo said. “So that's the source heuristic.”

In her research on heuristics, Nevo found that people’s thinking takes one of two paths: The first path is to read the article, think about it and decide if they believe it; the second is to consider the source and what others think about the news, and decide whether to believe it before reading it.

Image: Dorit Nevo/RPI/IEEE Spectrum

Researchers at RPI researched the role of heuristics and AI in detecting whether people thought news was credible

Another research paper, “Timing Matters When Correcting Fake News,” published in the Proceedings of the National Academy of Science by researchers at Harvard University, differed from the RPI researchers in its findings. While Nevo and her collaborators found that it’s easier to convince people that a story is fake news before reading it, the Harvard researchers, led by Nadia M. Brashier, a psychologist and neuroscientist, discovered that a fact-check can convince people of misinformation even after reading headlines. When study subjects read true or false labels after reading a headline, that resulted in a 25.3 percent reduction in “subsequent misclassification,” when compared to headlines with no tag, Brashier and her team found.

In the end, fighting misinformation will require both computing and human efforts such as policy changes, says Benjamin D. Horne, an assistant professor of Information Sciences at the University of Tennessee and one of Nevo’s co-authors. He says the RPI-Tennessee work was inspired by AI tools he designed previously. Horne was previously a research assistant at RPI, where he developed machine learning (ML) algorithms that can detect partial truths as well as decontextualized truths and out-of-date information.

“Our algorithms are trained on source-level behavior, both when using the textual content of an article and the network of other news sources that it draws news from,” Horne said. “We have found that these two types of features together are quite good at distinguishing between sources labeled as reliable or unreliable by external news source ratings.”

The machine learning algorithms analyze the writing style and the content-sharing behavior of news outlets, Horne said. Researchers trained a supervised ML algorithm called Random Forest, a classification algorithm that uses decision trees.

AI for Detecting Fake News

So, what’s the potential for AI to be successful in detecting misinformation?

“The tools we have developed, and other tools developed in this area, have fairly high accuracy in lab settings,” says Horne. “For example, our most recent technical work showed around 83% accuracy in predicting when the source of a news article is reliable or unreliable.”

Despite the effectiveness of algorithms, old-fashioned fact-checking by journalists will still be required to combat fake news. AI could filter the information for fact-checkers to verify, according to Horne.

“AI tools are great at dealing with high quantities of information at fast speeds but lack the nuanced analysis that a journalist or fact-checker can provide,” Horne said. “I see a future where the two work together.” Continue reading →

Posted in Human Robots

#438080 Boston Dynamics’ Spot Robot Is Now ...

Posted on February 3, 2021 by Android

Boston Dynamics has been working on an arm for its Spot quadruped for at least five years now. There have been plenty of teasers along the way, including this 45-second clip from early 2018 of Spot using its arm to open a door, which at 85 million views seems to be Boston Dynamics’ most popular video ever by a huge margin. Obviously, there’s a substantial amount of interest in turning Spot from a highly dynamic but mostly passive sensor platform into a mobile manipulator that can interact with its environment.

As anyone who’s done mobile manipulation will tell you, actually building an arm is just the first step—the really tricky part is getting that arm to do exactly what you want it to do. In particular, Spot’s arm needs to be able to interact with the world with some amount of autonomy in order to be commercially useful, because you can’t expect a human (remote or otherwise) to spend all their time positioning individual joints or whatever to pick something up. So the real question about this arm is whether Boston Dynamics has managed to get it to a point where it’s autonomous enough that users with relatively little robotics experience will be able to get it to do useful tasks without driving themselves nuts.

Today, Boston Dynamics is announcing commercial availability of the Spot arm, along with some improved software called Scout plus a self-charging dock that’ll give the robot even more independence. And to figure out exactly what Spot’s new arm can do, we spoke with Zachary Jackowski, Spot Chief Engineer at Boston Dynamics.

Although Boston Dynamics’ focus has been on dynamic mobility and legged robots, the company has been working on manipulation for a very long time. We first saw an arm prototype on an early iteration of Spot in 2016, where it demonstrated some impressive functionality, including loading a dishwasher and fetching a beer in a way that only resulted in a minor catastrophe. But we’re guessing that Spot’s arm can trace its history back to BigDog’s crazy powerful hydraulic face-arm, which was causing mayhem with cinder blocks back in 2013:

Spot’s arm is not quite that powerful (it has to drag cinder blocks along the ground rather than fling them into space), but you can certainly see the resemblance. Here’s the video that Boston Dynamics posted yesterday to introduce Spot’s new arm:

A couple of things jumped out from this video right away. First, Spot is doing whole body manipulation with its arm, as opposed to just acting as a four-legged base that brings the arm where it needs to go. Planning looks to be very tightly integrated, such that if you ask the robot to manipulate an object, its arm, legs, and torso all work together to optimize that manipulation. Also, when Spot flips that electrical switch, you see the robot successfully grasp the switch, and then reposition its body in a way that looks like it provides better leverage for the flip, which is a neat trick. It looks like it may be able to use the strength of its legs to augment the strength of its arm, as when it’s dragging the cinder block around, which is surely an homage to BigDog. The digging of a hole is particularly impressive. But again, the real question is how much of this is autonomous or semi-autonomous in a way that will be commercially useful?

Before we get to our interview with Spot Chief Engineer Zack Jackowski, it’s worth watching one more video that Boston Dynamics shared with us:

This is notable because Spot is opening a door that’s not ADA compliant, and the robot is doing it with a simple two-finger gripper. Most robots you see interacting with doors rely on ADA compliant hardware, meaning (among other things) a handle that can be pushed rather than a knob that has to be twisted, because it’s much more challenging for a robot to grasp and twist a smooth round door knob than it is to just kinda bash down on a handle. That capability, combined with Spot being able to pass through a spring-loaded door, potentially opens up a much wider array of human environments to the robot, and that’s where we started our conversation with Jackowski.

IEEE Spectrum: At what point did you decide that for Spot’s arm to be useful, it had to be able to handle round door knobs?

Zachary Jackowski: We're like a lot of roboticists, where someone in a meeting about manipulation would say “it's time for the round doorknob” and people would start groaning a little bit. But the reality is that, in order to make a robot useful, you have to engage with the environments that users have. Spot’s arm uses a very simple gripper—it’s a one degree of freedom gripper, but a ton of thought has gone into all of the fine geometric contours of it such that it can grab that ADA compliant lever handle, and it’ll also do an enclosing grasp around a round door knob. The major point of a robot like Spot is to engage with the environment you have, and so you can’t cut out stuff like round door knobs.

We're thrilled to be launching the arm and getting it out with users and to have them start telling us what doors it works really well on, and what they're having trouble with. And we're going to be working on rapidly improving all this stuff. We went through a few campaigns of like, “this isn’t ready until we can open every single door at Boston Dynamics!” But every single door at Boston Dynamics and at our test lab is a small fraction of all the doors in the world. So we're prepared to learn a lot this year.

When we see Spot open a door, or when it does those other manipulation behaviors in the launch video, how much of that is autonomous, how much is scripted, and to what extent is there a human in the loop?

All of the scenes where the robot does a pick, like the snow scene or the laundry scene, that is actually an almost fully integrated autonomous behavior that has a bit of a script wrapped around it. We trained a detector for an object, and the robot is identifying that object in the environment, picking it, and putting it in the bin all autonomously. The scripted part of that is telling the robot to perform a series of picks.

One of the things that we’re excited about, and that roboticists have been excited about going back probably all the way to the DRC, is semi-autonomous manipulation. And so we have modes built into the interface where if you see an object that you want the robot to grab, all you have to do is tap that object on the screen, and the robot will walk up to it, use the depth camera in its gripper to capture a depth map, and plan a grasp on its own in real time. That’s all built-in, too.

The jump rope—robots don’t just go and jump rope on their own. We scripted an arm motion to move the rope, and wrote a script using our API to coordinate all three robots. Drawing “Boston Dynamics” in chalk in our parking lot was scripted also. One of our engineers wrote a really cool G-code interpreter that vectorizes graphics so that Spot can draw them.

So for an end user, if you wanted Spot to autonomously flip some switches for you, you’d just have to train Spot on your switches, and then Spot could autonomously perform the task?

There are a couple of ways that task could break down depending on how you’re interfacing with the robot. If you’re a tablet user, you’d probably just identify the switch yourself on the tablet’s screen, and the robot will figure out the grasp, and grasp it. Then you’ll enter a constrained manipulation mode on the tablet, and the robot will be able to actuate the switch. But the robot will take care of the complicated controls aspects, like figuring out how hard it has to pull, the center of rotation of the switch, and so on.

The video of Spot digging was pretty cool—how did that work?

That’s mostly a scripted behavior. There are some really interesting control systems topics in there, like how you’d actually do the right kinds of force control while you insert the trowel into the dirt, and how to maintain robot stability while you do it. The higher level task of how to make a good hole in the dirt—that’s scripted. But the part of the problem that’s actually digging, you need the right control system to actually do that, or you’ll dig your trowel into the ground and flip your robot over.

The last time we saw Boston Dynamics robots flipping switches and turning valves I think might have been during the DRC in 2015, when they had expert robot operators with control over every degree of freedom. How are things different now with Spot, and will non-experts in the commercial space really be able to get the robot to do useful tasks?

A lot of the things, like “pick the stuff up in the room,” or ‘turn that switch,” can all be done by a lightly trained operator using just the tablet interface. If you want to actually command all of Spot’s arm degrees of freedom, you can do that— not through the tablet, but the API does expose all of it. That’s actually a notable difference from the base robot; we’ve never opened up the part of the API that lets you command individual leg degrees of freedom, because we don’t think it’s productive for someone to do that. The arm is a little bit different. There are a lot of smart people working on arm motion planning algorithms, and maybe you want to plan your arm trajectory in a super precise way and then do a DRC-style interface where you click to approve it. You can do all that through the API if you want, but fundamentally, it’s also user friendly. It follows our general API design philosophy of giving you the highest level pieces of the toolbox that will enable you to solve a complex problem that we haven't thought of.

Looking back on it now, it’s really cool to see, after so many years, robots do the stuff that Gill Pratt was excited about kicking off with the DRC. And now it’s just a thing you can buy.

Is Spot’s arm safe?

You should follow the same safety rules that you’d follow when working with Spot normally, and that’s that you shouldn’t get within two meters of the robot when it’s powered on. Spot is not a cobot. You shouldn’t hug it. Fundamentally, the places where the robot is the most valuable are places where people don’t want to be, or shouldn’t be.

We’ve seen how people reacted to earlier videos of Spot using its arm—can you help us set some reasonable expectations for what this means for Spot?

You know, it gets right back to the normal assumptions about our robots that people make that aren’t quite reality. All of this manipulation work we’re doing— the robot’s really acting as a tool. Even if it’s an autonomous behavior, it’s a tool. The robot is digging a hole because it’s got a set of instructions that say “apply this much force over this much distance here, here, and here.”

It’s not digging a hole and planting a tree because it loves trees, as much as I’d love to build a robot that works like that.

Photo: Boston Dynamics

There isn’t too much to say about the dock, except that it’s a requirement for making Spot long-term autonomous. The uncomfortable looking charging contacts that Spot impales itself on also include hardwired network connectivity, which is important because Spot often comes back home with a huge amount of data that all needs to be offloaded and processed. Docking and undocking are autonomous— as soon as the robot sees the fiducial markers on the dock, auto docking is enabled and it takes one click to settle the robot down.

During a brief remote demo, we also learned some other interesting things about Spot’s updated remote interface. It’s very latency tolerant, since you don’t have to drive the robot directly (although you can if you want to). Click a point on the camera view and Spot will move there autonomously while avoiding obstacles, meaning that even if you’re dealing with seconds of lag, the robot will continue making safe progress. This will be especially important if (when?) Spot starts exploring the Moon.

The remote interface also has an option to adjust how close Spot can get to obstacles, or to turn the obstacle avoidance off altogether. The latter functionality is useful if Spot sees something as an obstacle that really isn’t, like a curtain, while the former is useful if the robot is operating in an environment where it needs to give an especially wide berth to objects that could be dangerous to run into. “The robot’s not perfect—robots will never be perfect,” Jackowski reminds us, which is something we really (seriously) appreciate hearing from folks working on powerful, dynamic robots. “No matter how good the robot is, you should always de-risk as much as possible.”

Another part of that de-risking is having the user let Spot know when it’s about to go up or down some stairs by putting into “Stair Mode” with a toggle switch in the remote interface. Stairs are still a challenge for Spot, and Stair Mode slows the robot down and encourages it to pitch its body more aggressively to get a better view of the stairs. You’re encouraged to use stair mode, and also encouraged to send Spot up and down stairs with its “head” pointing up the stairs both ways, but these are not requirements for stair navigation— if you want to, you can send Spot down stairs head first without putting it in stair mode. Jackowski says that eventually, Spot will detect stairways by itself even when not in stair mode and adjust itself accordingly, but for now, that de-risking is solidly in the hands of the user.

Spot’s sensor payload, which is what we were trying out for the demo, provided a great opportunity for us to hear Spot STOMP STOMP STOMPING all over the place, which was also an opportunity for us to ask Jackowski why they can’t make Spot a little quieter. “It’s advantageous for Spot to step a little bit hard for the same reason it’s advantageous for you to step a little bit hard if you’re walking around blindfolded—that reason is that it really lets you know where the ground is, particularly when you’re not sure what to expect.” He adds, “It’s all in the name of robustness— the robot might be a little louder, but it’s a little more sure of its footing.”

Boston Dynamics isn’t yet ready to disclose the price of an arm-equipped Spot, but if you’re a potential customer, now is the time to contact the Boston Dynamics sales team to ask them about it. As a reminder, the base model of Spot costs US $74,500, with extra sensing or compute adding a substantial premium on top of that.

There will be a livestream launch event taking place at 11am ET today, during which Boston Dynamics’ CEO Robert Playter, VP of Marketing Michael Perry, and other folks from Boston Dynamics will make presentations on this new stuff. It’ll be live at this link, or you can watch it below. Continue reading →

Posted in Human Robots

#437614 Video Friday: Poimo Is a Portable ...

Posted on November 21, 2020 by Android

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!):

IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today's videos.

Engineers at the University of California San Diego have built a squid-like robot that can swim untethered, propelling itself by generating jets of water. The robot carries its own power source inside its body. It can also carry a sensor, such as a camera, for underwater exploration.

[ UCSD ]

Thanks Ioana!

Shark Robotics, French and European leader in Unmanned Ground Vehicles, is announcing today a disinfection add-on for Boston Dynamics Spot robot, designed to fight the COVID-19 pandemic. The Spot robot with Shark’s purpose-built disinfection payload can decontaminate up to 2,000 m2 in 15 minutes, in any space that needs to be sanitized – such as hospitals, metro stations, offices, warehouses or facilities.

[ Shark Robotics ]

Here’s an update on the Poimo portable inflatable mobility project we wrote about a little while ago; while not strictly robotics, it seems like it holds some promise for rapidly developing different soft structures that robotics might find useful.

[ University of Tokyo ]

Thanks Ryuma!

Pretty cool that you can do useful force feedback teleop while video chatting through a “regular broadband Internet connection.” Although, what “regular” means to you is a bit subjective, right?

[ HEBI Robotics ]

Thanks Dave!

While NASA's Mars rover Perseverance travels through space toward the Red Planet, its nearly identical rover twin is hard at work on Earth. The vehicle system test bed (VSTB) rover named OPTIMISM is a full-scale engineering version of the Mars-bound rover. It is used to test hardware and software before the commands are sent up to the Perseverance rover.

[ NASA ]

Jacquard takes ordinary, familiar objects and enhances them with new digital abilities and experiences, while remaining true to their original purpose — like being your favorite jacket, backpack or a pair of shoes that you love to wear.

Our ambition is simple: to make life easier. By staying connected to your digital world, your things can do so much more. Skip a song by brushing your sleeve. Take a picture by tapping on a shoulder strap. Get reminded about the phone you left behind with a blink of light or a haptic buzz on your cuff.

[ Google ATAP ]

Should you attend the IROS 2020 workshop on “Planetary Exploration Robots: Challenges and Opportunities”? Of course you should!

[ Workshop ]

Kuka makes a lot of these videos where I can’t help but think that if they put as much effort into programming the robot as they did into producing the video, the result would be much more impressive.

[ Kuka ]

The Colorado School of Mines is one of the first customers to buy a Spot robot from Boston Dynamics to help with robotics research. Watch as scientists take Spot into the school's mine for the first time.

[ HCR ] via [ CNET ]

A very interesting soft(ish) actuator from Ayato Kanada at Kyushu University's Control Engineering Lab.

A flexible ultrasonic motor (FUSM), which generates linear motion as a novel soft actuator. This motor consists of a single metal cube stator with a hole and an elastic elongated coil spring inserted into the hole. When voltages are applied to piezoelectric plates on the stator, the coil spring moves back and forward as a linear slider. In the FUSM that uses the friction drive as the principle, the most important parameter for optimizing its output is the preload between the stator and slider. The coil spring has a slightly larger diameter than the stator hole and generates the preload by expanding in a radial direction. The coil springs act not only as a flexible slider but also as a resistive positional sensor. Changes in the resistance between the stator and the coil spring end are converted to a voltage and used for position detection.

[ Control Engineering Lab ]

Thanks Ayato!

We show how to use the limbs of a quadruped robot to identify fine-grained soil, representative for Martian regolith.

[ Paper ] via [ ANYmal Research ]

PR2 is serving breakfast and cleaning up afterwards. It’s slow, but all you have to do is eat and leave.

That poor PR2 is a little more naked than it's probably comfortable with.

[ EASE ]

NVIDIA researchers present a hierarchical framework that combines model-based control and reinforcement learning (RL) to synthesize robust controllers for a quadruped robot (the Unitree Laikago).

[ NVIDIA ]

What's interesting about this assembly task is that the robot is using its arm only for positioning, and doing the actual assembly with just fingers.

[ RC2L ]

In this electronics assembly application, Kawasaki's cobot duAro2 uses a tool changing station to tackle a multitude of tasks and assemble different CPU models.

Okay but can it apply thermal paste to a CPU in the right way? Personally, I find that impossible.

[ Kawasaki ]

You only need to watch this video long enough to appreciate the concept of putting a robot on a robot.

[ Impress ]

In this lecture, we’ll hear from the man behind one of the biggest robotics companies in the world, Boston Dynamics, whose robotic dog, Spot, has been used to encourage social distancing in Singapore and is now getting ready for FDA approval to be able to measure patients’ vital signs in hospitals.

[ Alan Turing Institute ]

Greg Kahn from UC Berkeley wrote in to share his recent dissertation talk on “Mobile Robot Learning.”

In order to create mobile robots that can autonomously navigate real-world environments, we need generalizable perception and control systems that can reason about the outcomes of navigational decisions. Learning-based methods, in which the robot learns to navigate by observing the outcomes of navigational decisions in the real world, offer considerable promise for obtaining these intelligent navigation systems. However, there are many challenges impeding mobile robots from autonomously learning to act in the real-world, in particular (1) sample-efficiency–how to learn using a limited amount of data? (2) supervision–how to tell the robot what to do? and (3) safety–how to ensure the robot and environment are not damaged or destroyed during learning? In this talk, I will present deep reinforcement learning methods for addressing these real world mobile robot learning challenges and show results which enable ground and aerial robots to navigate in complex indoor and outdoor environments.

[ UC Berkeley ]

Thanks Greg!

Leila Takayama from UC Santa Cruz (and previously Google X and Willow Garage) gives a talk entitled “Toward a more human-centered future of robotics.”

Robots are no longer only in outer space, in factory cages, or in our imaginations. We interact with robotic agents when withdrawing cash from bank ATMs, driving cars with adaptive cruise control, and tuning our smart home thermostats. In the moment of those interactions with robotic agents, we behave in ways that do not necessarily align with the rational belief that robots are just plain machines. Through a combination of controlled experiments and field studies, we use theories and concepts from the social sciences to explore ways that human and robotic agents come together, including how people interact with personal robots and how people interact through telepresence robots. Together, we will explore topics and raise questions about the psychology of human-robot interaction and how we could invent a future of a more human-centered robotics that we actually want to live in.

[ Leila Takayama ]

Roboticist and stand-up comedian Naomi Fitter from Oregon State University gives a talk on “Everything I Know about Telepresence.”

Telepresence robots hold promise to connect people by providing videoconferencing and navigation abilities in far-away environments. At the same time, the impacts of current commercial telepresence robots are not well understood, and circumstances of robot use including internet connection stability, odd personalizations, and interpersonal relationship between a robot operator and people co-located with the robot can overshadow the benefit of the robot itself. And although the idea of telepresence robots has been around for over two decades, available nonverbal expressive abilities through telepresence robots are limited, and suitable operator user interfaces for the robot (for example, controls that allow for the operator to hold a conversation and move the robot simultaneously) remain elusive. So where should we be using telepresence robots? Are there any pitfalls to watch out for? What do we know about potential robot expressivity and user interfaces? This talk will cover my attempts to address these questions and ways in which the robotics research community can build off of this work

[ Talking Robotics ] Continue reading →

Posted in Human Robots

#437608 Video Friday: Agility Robotics Raises ...

Posted on November 21, 2020 by Android

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!):

IROS 2020 – October 25-29, 2020 – [Online]
ROS World 2020 – November 12, 2020 – [Online]
CYBATHLON 2020 – November 13-14, 2020 – [Online]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

Digit is now in full commercial production and we’re excited to announce a $20M funding rounding round co-led by DCVC and Playground Global!

Digits for everyone!

[ Agility Robotics ]

A flexible rover that has both ability to travel long distances and rappel down hard-to-reach areas of scientific interest has undergone a field test in the Mojave Desert in California to showcase its versatility. Composed of two Axel robots, DuAxel is designed to explore crater walls, pits, scarps, vents and other extreme terrain on the moon, Mars and beyond.

This technology demonstration developed at NASA’s Jet Propulsion Laboratory in Southern California showcases the robot’s ability to split in two and send one of its halves — a two-wheeled Axle robot — over an otherwise inaccessible slope, using a tether as support and to supply power.

The rappelling Axel can then autonomously seek out areas to study, safely overcome slopes and rocky obstacles, and then return to dock with its other half before driving to another destination. Although the rover doesn’t yet have a mission, key technologies are being developed that might, one day, help us explore the rocky planets and moons throughout the solar system.

[ JPL ]

A rectangular robot as tiny as a few human hairs can travel throughout a colon by doing back flips, Purdue University engineers have demonstrated in live animal models. Why the back flips? Because the goal is to use these robots to transport drugs in humans, whose colons and other organs have rough terrain. Side flips work, too. Why a back-flipping robot to transport drugs? Getting a drug directly to its target site could remove side effects, such as hair loss or stomach bleeding, that the drug may otherwise cause by interacting with other organs along the way.

[ Purdue ]

This video shows the latest results in the whole-body locomotion control of the humanoid robot iCub achieved by the Dynamic Interaction Control line at IIT-Istituto Italiano di Tecnologia in Genova (Italy). In particular, the iCub now keeps the balance while walking and receiving pushes from an external user. The implemented control algorithms also ensure the robot to remain compliant during locomotion and human-robot interaction, a fundamental property to lower the possibility to harm humans that share the robot surrounding environment.

This is super impressive, considering that iCub was only able to crawl and was still tethered not too long ago. Also, it seems to be blinking properly now, so it doesn’t look like it’s always sleepy.

[ IIT ]

This video shows a set of new tests we performed on Bolt. We conducted tests on 5 different scenarios, 1) walking forward/backward 2) uneven surface 3) soft surface 4) push recovery 5) slippage recovery. Thanks to our feedback control based on Model Predictive Control, the robot can perform walking in the presence of all these uncertainties. We will open-source all the codes in a near future.

[ ODRI ]

The title of this video is “Can you throw your robot into a lake?” The title of this video should be, “Can you throw your robot into a lake and drive it out again?”

[ Norlab ]

AeroVironment Successfully Completes Sunglider Solar HAPS Stratospheric Test Flight, Surpassing 60,000 Feet Altitude and Demonstrating Broadband Mobile Connectivity.

[ AeroVironment ]

We present CoVR, a novel robotic interface providing strong kinesthetic feedback (100 N) in a room-scale VR arena. It consists of a physical column mounted on a 2D Cartesian ceiling robot (XY displacements) with the capacity of (1) resisting to body-scaled users actions such as pushing or leaning; (2) acting on the users by pulling or transporting them as well as (3) carrying multiple potentially heavy objects (up to 80kg) that users can freely manipulate or make interact with each other.

[ DeepAI ]

In a new video, personnel from Swiss energy supply company Kraftwerke Oberhasli AG (KWO) explain how they were able to keep employees out of harm’s way by using Flyability’s Elios 2 to collect visual data while building a new dam.

[ Flyability ]

Enjoy our Ascento robot fail compilation! With every failure we experience, we learn more and we can improve our robot for its next iteration, which will come soon… Stay tuned for more!

FYI posting a robot fails video will pretty much guarantee you a spot in Video Friday!

[ Ascento ]

Humans are remarkably good at using chopsticks. The Guinness World Record witnessed a person using chopsticks to pick up 65 M&Ms in just a minute. We aim to collect demonstrations from humans and to teach robot to use chopsticks.

[ UW Personal Robotics Lab ]

A surprising amount of personality from these Yaskawa assembly robots.

[ Yaskawa ]

This paper presents the system design, modeling, and control of the Aerial Robotic Chain Manipulator. This new robot design offers the potential to exert strong forces and moments to the environment, carry and lift significant payloads, and simultaneously navigate through narrow corridors. The presented experimental studies include a valve rotation task, a pick-and-release task, and the verification of load oscillation suppression to demonstrate the stability and performance of the system.

[ ARL ]

Whether animals or plants, whether in the water, on land or in the air, nature provides the model for many technical innovations and inventions. This is summed up in the term bionics, which is a combination of the words ‘biology‘ and ‘electronics’. At Festo, learning from nature has a long history, as our Bionic Learning Network is based on using nature as the source for future technologies like robots, assistance systems or drive solutions.

[ Festo ]

Dogs! Selfies! Thousands of LEGO bricks! This video has it all.

[ LEGO ]

An IROS workshop talk on “Cassie and Mini Cheetah Autonomy” by Maani Ghaffari and Jessy Grizzle from the University of Michigan.

[ Michigan Robotics ]

David Schaefer’s Cozmo robots are back with this mind-blowing dance-off!

What you just saw represents hundreds of hours of work, David tells us: “I wrote over 10,000 lines of code to create the dance performance as I had to translate the beats per minute of the song into motor rotations in order to get the right precision needed to make the moves look sharp. The most challenging move was the SpongeBob SquareDance as any misstep would send the Cozmos crashing into each other. LOL! Fortunately for me, Cozmo robots are pretty resilient.”

[ Life with Cozmo ]

Thanks David!

This week’s GRASP on Robotics seminar is by Sangbae Kim from MIT, on “Robots with Physical Intelligence.”

While industrial robots are effective in repetitive, precise kinematic tasks in factories, the design and control of these robots are not suited for physically interactive performance that humans do easily. These tasks require ‘physical intelligence’ through complex dynamic interactions with environments whereas conventional robots are designed primarily for position control. In order to develop a robot with ‘physical intelligence’, we first need a new type of machines that allow dynamic interactions. This talk will discuss how the new design paradigm allows dynamic interactive tasks. As an embodiment of such a robot design paradigm, the latest version of the MIT Cheetah robots and force-feedback teleoperation arms will be presented.

[ GRASP ]

This week’s CMU Ri Seminar is by Kevin Lynch from Northwestern, on “Robotics and Biosystems.”

Research at the Center for Robotics and Biosystems at Northwestern University encompasses bio-inspiration, neuromechanics, human-machine systems, and swarm robotics, among other topics. In this talk I will give an overview of some of our recent work on in-hand manipulation, robot locomotion on yielding ground, and human-robot systems.

[ CMU RI ] Continue reading →

Posted in Human Robots

#437564 How We Won the DARPA SubT Challenge: ...

Posted on November 18, 2020 by Android

This is a guest post. The views expressed here are those of the authors and do not necessarily represent positions of IEEE or its organizational units.​

“Do you smell smoke?” It was three days before the qualification deadline for the Virtual Tunnel Circuit of the DARPA Subterranean Challenge Virtual Track, and our team was barrelling through last-minute updates to our robot controllers in a small conference room at the Michigan Tech Research Institute (MTRI) offices in Ann Arbor, Mich. That’s when we noticed the smell. We’d assumed that one of the benefits of entering a virtual disaster competition was that we wouldn’t be exposed to any actual disasters, but equipment in the basement of the building MTRI shares had started to smoke. We evacuated. The fire department showed up. And as soon as we could, the team went back into the building, hunkered down, and tried to make up for the unexpected loss of several critical hours.

Team BARCS joins the SubT Virtual Track
The smoke incident happened more than a year after we first learned of the DARPA Subterranean Challenge. DARPA announced SubT early in 2018, and at that time, we were interested in building internal collaborations on multi-agent autonomy problems, and SubT seemed like the perfect opportunity. Though a few of us had backgrounds in robotics, the majority of our team was new to the field. We knew that submitting a proposal as a largely non-traditional robotics team from an organization not known for research in robotics was a risk. However, the Virtual Track gave us the opportunity to focus on autonomy and multi-agent teaming strategies, areas requiring skill in asynchronous computing and sensor data processing that are strengths of our Institute. The prevalence of open source code, small inexpensive platforms, and customizable sensors has provided the opportunity for experts in fields other than robotics to apply novel approaches to robotics problems. This is precisely what makes the Virtual Track of SubT appealing to us, and since starting SubT, autonomy has developed into a significant research thrust for our Institute. Plus, robots are fun!

After many hours of research, discussion, and collaboration, we submitted our proposal early in 2018. And several months later, we found out that we had won a contract and became a funded team (Team BARCS) in the SubT Virtual Track. Now we needed to actually make our strategy work for the first SubT Tunnel Circuit competition, taking place in August of 2019.

Building a team of virtual robots
A natural approach to robotics competitions like SubT is to start with the question of “what can X-type robot do” and then build a team and strategy around individual capabilities. A particular challenge for the SubT Virtual Track is that we can’t design our own systems; instead, we have to choose from a predefined set of simulated robots and sensors that DARPA provides, based on the real robots used by Systems Track teams. Our approach is to look at what a team of robots can do together, determining experimentally what the best team configuration is for each environment. By the final competition, ideally we will be demonstrating the value of combining platforms across multiple Systems Track teams into a single Virtual Track team. Each of the robot configurations in the competition has an associated cost, and team size is constrained by a total cost. This provides another impetus for limiting dependence on complex sensor packages, though our ranging preference is 3D lidar, which is the most expensive sensor!

Image: Michigan Tech Research Institute

The teams can rely on realistic physics and sensors but they start off with no maps of any kind, so the focus is on developing autonomous exploratory behavior, navigation methods, and object recognition for their simulated robots.

One of the frequent questions we receive about the Virtual Track is if it’s like a video game. While it may look similar on the surface, everything under the hood in a video game is designed to service the game narrative and play experience, not require novel research in AI and autonomy. The purpose of simulations, on the other hand, is to include full physics and sensor models (including noise and errors) to provide a testbed for prototyping and developing solutions to those real-world challenges. We are starting with realistic physics and sensors but no maps of any kind, so the focus is on developing autonomous exploratory behavior, navigation methods, and object recognition for our simulated robots.

Though the simulation is more like real life than a video game, it is not real life. Due to occasional software bugs, there are still non-physical events, like the robots falling through an invisible hole in the world or driving through a rock instead of over it or flipping head over heels when driving over a tiny lip between world tiles. These glitches, while sometimes frustrating, still allow the SubT Virtual platform to be realistic enough to support rapid prototyping of controller modules that will transition straightforwardly onto hardware, closing the loop between simulation and real-world robots.

Full autonomy for DARPA-hard scenarios
The Virtual Track requirement that the robotic agents be fully autonomous, rather than have a human supervisor, is a significant distinction between the Systems and Virtual Tracks of SubT. Our solutions must be hardened against software faults caused by things like missing and bad data since our robots can’t turn to us for help. In order for a team of robots to complete this objective reliably with no human-in-the-loop, all of the internal systems, from perception to navigation to control to actuation to communications, must be able to autonomously identify and manage faults and failures anywhere in the control chain.

The communications limitations in subterranean environments (both real and virtual) mean that we need to keep the amount of information shared between robots low, while making the usability of that information for joint decision-making high. This goal has guided much of our design for autonomous navigation and joint search strategy for our team. For example, instead of sharing the full SLAM map of the environment, our agents only share a simplified graphical representation of the space, along with data about frontiers it has not yet explored, and are able to merge its information with the graphs generated by other agents. The merged graph can then be used for planning and navigation without having full knowledge of the detailed 3D map.

The Virtual Track requires that the robotic agents be fully autonomous. With no human-in-the-loop, all of the internal systems, from perception to navigation to control to actuation to communications, must be able to identify and manage faults and failures anywhere in the control chain.

Since the objective of the SubT program is to advance the state-of-the-art in rapid autonomous exploration and mapping of subterranean environments by robots, our first software design choices focused on the mapping task. The SubT virtual environments are sufficiently rich as to provide interesting problems in building so-called costmaps that accurately separate obstructions that are traversable (like ramps) from legitimately impassible obstructions. An extra complication we discovered in the first course, which took place in mining tunnels, was that the angle of the lowest beam of the lidar was parallel to the down ramps in the tunnel environment, so they could not “see” the ground (or sometimes even obstructions on the ramp) until they got close enough to the lip of the ramp to receive lidar reflections off the bottom of the ramp. In this case, we had to not only change the costmap to convince the robot that there was safe ground to reach over the lip of the ramp, but also had to change the path planner to get the robot to proceed with caution onto the top of the ramp in case there were previously unseen obstructions on the ramp.

In addition to navigation in the costmaps, the robot must be able to generate its own goals to navigate to. This is what produces exploratory behavior when there is no map to start with. SLAM is used to generate a detailed map of the environment explored by a single robot—the space it has probed with its sensors. From the sensor data, we are able to extract information about the interior space of the environment while looking for holes in the data, to determine things like whether the current tunnel continues or ends, or how many tunnels meet at an intersection. Once we have some understanding of the interior space, we can place navigation goals in that space. These goals naturally update as the robot traverses the tunnel, allowing the entire space to be explored.

Sending our robots into the virtual unknown
The solutions for the Virtual Track competitions are tested by DARPA in multiple sequestered runs across many environments for each Circuit in the month prior to the Systems Track competition. We must wait until the joint award ceremony at the conclusion of the Systems Track to find out the results, and we are completely in the dark about placings before the awards are announced. It’s nerve-wracking! The challenges of the worlds used in the Circuit events are also hand-designed, so features of the worlds we use for development could be combined in ways we have not anticipated—it’s always interesting to see what features were prioritized after the event. We test everything in our controllers well enough to feel confident that we at least are submitting something reasonably stable and broadly capable, and once the solution is in, we can’t really do anything other than “let go” and get back to work on the next phase of development. Maybe it’s somewhat like sending your kid to college: “we did our best to prepare you for this world, little bots. Go do good.”

Image: Michigan Tech Research Institute

The first SubT competition was the Tunnel Circuit, featuring a labyrinthine environment that simulated human-engineered tunnels, including hazards such as vertical shafts and rubble.

The first competition was the Tunnel Circuit, in October 2019. This environment models human-engineered tunnels. Two substantial challenges in this environment were vertical shafts and rubble. Our team accrued 21 points over 15 competition runs in five separate tunnel environments for a second place finish, behind Team Coordinated Robotics.

The next phase of the SubT virtual competition was the Urban Circuit. Much of the difference between our Tunnel and Urban Circuit results came down to thorough testing to identify failure modes and implementations of checks and data filtering for fault tolerance. For example, in the SLAM nodes run by a single robot, the coordinates of the most recent sensor data are changed multiple times during processing and integration into the current global 3D map of the “visited” environment stored by that robot. If there is lag in IMU or clock data, the observation may be temporarily registered at a default location that is very far from the actual position. Since most of our decision processes for exploration are downstream from SLAM, this can cause faulty or impossible goals to be generated, and the robots then spend inordinate amounts of time trying to drive through walls. We updated our method to add a check to see if the new map position has jumped a far distance from the prior map position, and if so, we threw that data out.

Image: Michigan Tech Research Institute

In open spaces like the rooms in the Urban circuit, we adjusted our approach to exploration through graph generation to allow the robots to accurately identify viable routes while helping to prevent forays off platform edges.

Our approach to exploration through graph generation based on identification of interior spaces allowed us to thoroughly explore the centers of rooms, although we did have to make some changes from the Tunnel circuit to achieve that. In the Tunnel circuit, we used a simplified graph of the environment based on landmarks like intersections. The advantage of this approach is that it is straightforward for two robots to compare how the graphs of the space they explored individually overlap. In open spaces like the rooms in the Urban circuit, we chose to instead use a more complex, less directly comparable graph structure based on the individual robot’s trajectory. This allowed the robots to accurately identify viable routes between features like subway station platforms and subway tracks, as well as to build up the navigation space for room interiors, while helping to prevent forays off the platform edges. Frontier information is also integrated into the graph, providing a uniform data structure for both goal selection and route planning.

The results are in!
The award ceremony for the Urban Circuit was held concurrently with the Systems Track competition awards this past February in Washington State. We sent a team representative to participate in the Technical Interchange Meeting and present the approach for our team, and the rest of us followed along from our office space on the DARPAtv live stream. While we were confident in our solution, we had also been tracking the online leaderboard and knew our competitors were going to be submitting strong solutions. Since the competition environments are hand-designed, there are always novel challenges that could be presented in these environments as well. We knew we would put up a good fight, but it was very exciting to see BARCS appear in first place!

Any time we implement a new module in our control system, there is a lot of parameter tuning that has to happen to produce reliably good autonomous behavior. In the Urban Circuit, we did not sufficiently test some parameter values in our exploration modules. The effect of this was that the robots only chose to go down small hallways after they explored everything else in their environment, which meant very often they ran out of time and missed a lot of small rooms. This may be the biggest source of lost points for us in the Urban Circuit. One of our major plans going forward from the Urban Circuit is to integrate more sophisticated node selection methods, which can help our robots more intelligently prioritize which frontier nodes to visit. By going through all three Circuit challenges, we will learn how to appropriately add weights to the frontiers based on features of the individual environments. For the Final Challenge, when all three Circuit environments will be combined into large systems, we plan to implement adaptive controllers that will identify their environments and use the appropriate optimized parameters for that environment. In this way, we expect our agents to be able to (for example) prioritize hallways and other small spaces in Urban environments, and perhaps prioritize large openings over small in the Cave environments, if the small openings end up being treacherous overall.

Next for our team: Cave Circuit
Coming up next for Team BARCS is the Virtual Cave Circuit. We are in the middle of testing our hypothesis that our controller will transition from UGVs to UAVs and developing strategies for refining our solution to handle Cave Circuit environmental hazards. The UAVs have a shorter battery life than the UGVs, so executing a joint exploration strategy will also be a high priority for this event, as will completing our work on graph sharing and merging, which will give our robot teams more sophisticated options for navigation and teamwork. We’re reaching a threshold in development where we can start increasing the “smarts” of the robots, which we anticipate will be critical for the final competition, where all of the challenges of SubT will be combined to push the limits of innovation. The Cave Circuit will also have new environmental challenges to tackle: dynamic features such as rock falls have been added, which will block previously accessible passages in the cave environment. We think our controllers are well-poised to handle this new challenge, and we’re eager to find out if that’s the case.

As of now, the biggest worries for us are time and team composition. The Cave Circuit deadline has been postponed to October 15 due to COVID-19 delays, with the award ceremony in mid-November, but there have also been several very compelling additions to the testbed that we would like to experiment with before submission, including droppable networking ‘breadcrumbs’ and new simulated platforms. There are design trade-offs when balancing general versus specialist approaches to the controllers for these robots—since we are adding UAVs to our team for the first time, there are new decisions that will have to be made. For example, the UAVs can ascend into vertical spaces, but only have a battery life of 20 minutes. The UGVs by contrast have 90 minute battery life. One of our strategies is to do an early return to base with one or more agents to buy down risk on making any artifact reports at all for the run, hedging against our other robots not making it back in time, a lesson learned from the Tunnel Circuit. Should a UAV take on this role, or is it better to have them explore deeper into the environment and instead report their artifacts to a UGV or network node, which comes with its own risks? Testing and experimentation to determine the best options takes time, which is always a worry when preparing for a competition! We also anticipate new competitors and stiffer competition all around.

Image: Michigan Tech Research Institute

Team BARCS has now a year to prepare for the final DARPA SubT Challenge event, expected to take place in late 2021.

Going forward from the Cave Circuit, we will have a year to prepare for the final DARPA SubT Challenge event, expected to take place in late 2021. What we are most excited about is increasing the level of intelligence of the agents in their teamwork and joint exploration of the environment. Since we will have (hopefully) built up robust approaches to handling each of the specific types of environments in the Tunnel, Urban, and Cave circuits, we will be aiming to push the limits on collaboration and efficiency among the agents in our team. We view this as a central research contribution of the Virtual Track to the Subterranean Challenge because intelligent, adaptive, multi-robot collaboration is an upcoming stage of development for integration of robots into our lives.

The Subterranean Challenge Virtual Track gives us a bridge for transitioning our more abstract research ideas and algorithms relevant to this degree of autonomy and collaboration onto physical systems, and exploring the tangible outcomes of implementing our work in the real world. And the next time there’s an incident in the basement of our building, the robots (and humans) of Team BARCS will be ready to respond.

Richard Chase, Ph.D., P.E., is a research scientist at Michigan Tech Research Institute (MTRI) and has 20 years of experience developing robotics and cyber physical systems in areas from remote sensing to autonomous vehicles. At MTRI, he works on a variety of topics such as swarm autonomy, human-swarm teaming, and autonomous vehicles. His research interests are the intersection of design, robotics, and embedded systems.

Sarah Kitchen is a Ph.D. mathematician working as a research scientist and an AI/Robotics focus area leader at MTRI. Her research interests include intelligent autonomous agents and multi-agent collaborative teams, as well as applications of autonomous robots to sensing systems.

This material is based upon work supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. HR001118C0124 and is released under Distribution Statement (Approved for Public Release, Distribution Unlimited). Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of DARPA. Continue reading →

Posted in Human Robots