Tag Archives: future

#437687 Video Friday: Bittle Is a Palm-Sized ...

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!):

ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online]
IROS 2020 – October 25-29, 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.

Rongzhong Li, who is responsible for the adorable robotic cat Nybble, has an updated and even more adorable quadruped that's more robust and agile but only costs around US $200 in kit form on Kickstarter.

Looks like the early bird options are sold out, but a full kit is a $225 pledge, for delivery in December.

[ Kickstarter ]

Thanks Rz!

I still maintain that Stickybot was one of the most elegantly designed robots ever.

[ Stanford ]

With the unpredictable health crisis of COVID-19 continuing to place high demands on hospitals, PAL Robotics have successfully completed testing of their delivery robots in Barcelona hospitals this summer. The TIAGo Delivery and TIAGo Conveyor robots were deployed in Hospital Municipal of Badalona and Hospital Clínic Barcelona following a winning proposal submitted to the European DIH-Hero project. Accerion sensors were integrated onto the TIAGo Delivery Robot and TIAGo Conveyor Robot for use in this project.

[ PAL Robotics ]

Energy Robotics, a leading developer of software solutions for mobile robots used in industrial applications, announced that its remote sensing and inspection solution for Boston Dynamics’s agile mobile robot Spot was successfully deployed at Merck’s thermal exhaust treatment plant at its headquarters in Darmstadt, Germany. Energy Robotics equipped Spot with sensor technology and remote supervision functions to support the inspection mission.

Combining Boston Dynamics’ intuitive controls, robotic intelligence and open interface with Energy Robotics’ control and autonomy software, user interface and encrypted cloud connection, Spot can be taught to autonomously perform a specific inspection round while being supervised remotely from anywhere with internet connectivity. Multiple cameras and industrial sensors enable the robot to find its way around while recording and transmitting information about the facility’s onsite equipment operations.

Spot reads the displays of gauges in its immediate vicinity and can also zoom in on distant objects using an externally-mounted optical zoom lens. In the thermal exhaust treatment facility, for instance, it monitors cooling water levels and notes whether condensation water has accumulated. Outside the facility, Spot monitors pipe bridges for anomalies.

Among the robot’s many abilities, it can detect defects of wires or the temperature of pump components using thermal imaging. The robot was put through its paces on a comprehensive course that tested its ability to handle special challenges such as climbing stairs, scaling embankments and walking over grating.

[ Energy Robotics ]

Thanks Stefan!

Boston Dynamics really should give Dr. Guero an Atlas just to see what he can do with it.

[ DrGuero ]

World's First Socially Distanced Birthday Party: Located in London, the robotic arm was piloted in real time to light the candles on the cake by the founder of Extend Robotics, Chang Liu, who was sat 50 miles away in Reading. Other team members in Manchester and Reading were also able to join in the celebration as the robot was used to accurately light the candles on the birthday cake.

[ Extend Robotics ]

The Robocon in-person competition was canceled this year, but check out Tokyo University's robots in action:

[ Robocon ]

Sphero has managed to pack an entire Sphero into a much smaller sphere.

[ Sphero ]

Squishy Robotics, a small business funded by the National Science Foundation (NSF), is developing mobile sensor robots for use in disaster rescue, remote monitoring, and space exploration. The shape-shifting, mobile, senor robots from UC-Berkeley spin-off Squishy Robotics can be dropped from airplanes or drones and can provide first responders with ground-based situational awareness during fires, hazardous materials (HazMat) release, and natural and man-made disasters.

[ Squishy Robotics ]

Meet Jasper, the small girl with big dreams to FLY. Created by UTS Animal Logic Academy in partnership with the Royal Australian Air Force to encourage girls to soar above the clouds. Jasper was created using a hybrid of traditional animation techniques and technology such as robotics and 3D printing. A KUKA QUANTEC robot is used during the film making to help the Australian Royal Airforce tell their story in a unique way. UTS adapted their High Accurate robot to film consistent paths, creating a video with physical sets and digital characters.

[ AU AF ]

Impressive what the Ghost Robotics V60 can do without any vision sensors on it.

[ Ghost Robotics ]

Is your job moving tiny amounts of liquid around? Would you rather be doing something else? ABB’s YuMi got you.

[ Yumi ]

For his PhD work at the Media Lab, Biomechatronics researcher Roman Stolyarov developed a terrain-adaptive control system for robotic leg prostheses. as a way to help people with amputations feel as able-bodied and mobile as possible, by allowing them to walk seamlessly regardless of the ground terrain.

[ MIT ]

This robot collects data on each cow when she enters to be milked. Milk samples and 3D photos can be taken to monitor the cow’s health status. The Ontario Dairy Research Centre in Elora, Ontario, is leading dairy innovation through education and collaboration. It is a state-of-the-art 175,000 square foot facility for discovery, learning and outreach. This centre is a partnership between the Agricultural Research Institute of Ontario, OMAFRA, the University of Guelph and the Ontario dairy industry.

[ University of Guleph ]

Australia has one of these now, should the rest of us panic?

[ Boeing ]

Daimler and Torc are developing Level 4 automated trucks for the real world. Here is a glimpse into our closed-course testing, routes on public highways in Virginia, and self-driving capabilities development. Our year of collaborating on the future of transportation culminated in the announcement of our new truck testing center in New Mexico.

[ Torc Robotics ] Continue reading

Posted in Human Robots

#437683 iRobot Remembers That Robots Are ...

iRobot has released several new robots over the last few years, including the i7 and s9 vacuums. Both of these models are very fancy and very capable, packed with innovative and useful features that we’ve been impressed by. They’re both also quite expensive—with dirt docks included, you’re looking at US $800 for the i7+, and a whopping $1,100 for the s9+. You can knock a couple hundred bucks off of those prices if you don’t want the docks, but still, these vacuums are absolutely luxury items.

If you just want something that’ll do some vacuuming so that you don’t have to, iRobot has recently announced a new Roomba option. The Roomba i3 is iRobot’s new low to midrange vacuum, starting at $400. It’s not nearly as smart as the i7 or the s9, but it can navigate (sort of) and make maps (sort of) and do some basic smart home integration. If that sounds like all you need, the i3 could be the robot vacuum for you.

iRobot calls the i3 “stylish,” and it does look pretty neat with that fabric top. Underneath, you get dual rubber primary brushes plus a side brush. There’s limited compatibility with the iRobot Home app and IFTTT, along with Alexa and Google Home. The i3 is also compatible with iRobot’s Clean Base, but that’ll cost you an extra $200, and iRobot refers to this bundle as the i3+.

The reason that the i3 only offers limited compatibility with iRobot’s app is that the i3 is missing the top-mounted camera that you’ll find in more expensive models. Instead, it relies on a downward-looking optical sensor to help it navigate, and it builds up a map as it’s cleaning by keeping track of when it bumps into obstacles and paying attention to internal sensors like a gyro and wheel odometers. The i3 can localize directly on its charging station or Clean Base (which have beacons on them that the robot can see if it’s close enough), which allows it to resume cleaning after emptying it’s bin or recharging. You’ll get a map of the area that the i3 has cleaned once it’s finished, but that map won’t persist between cleaning sessions, meaning that you can’t do things like set keep-out zones or identify specific rooms for the robot to clean. Many of the more useful features that iRobot’s app offers are based on persistent maps, and this is probably the biggest gap in functionality between the i3 and its more expensive siblings.

According to iRobot senior global product manager Sarah Wang, the kind of augmented dead-reckoning-based mapping that the i3 uses actually works really well: “Based on our internal and external testing, the performance is equivalent with our products that have cameras, like the Roomba 960,” she says. To get this level of performance, though, you do have to be careful, Wang adds. “If you kidnap i3, then it will be very confused, because it doesn’t have a reference to know where it is.” “Kidnapping” is a term that’s used often in robotics to refer to a situation in which an autonomous robot gets moved to an unmapped location, and in the context of a home robot, the best example of this is if you decide that you want your robot to vacuum a different room instead, so you pick it up and move it there.

iRobot used to make this easy by giving all of its robots carrying handles, but not anymore, because getting moved around makes things really difficult for any robot trying to keep track of where it is. While robots like the i7 can recover using their cameras to look for unique features that they recognize, the only permanent, unique landmark that the i3 can for sure identify is the beacon on its dock. What this means is that when it comes to the i3, even more than other Roomba models, the best strategy, is to just “let it do its thing,” says iRobot senior principal system engineer Landon Unninayar.

Photo: iRobot

The Roomba i3 is iRobot’s new low to midrange vacuum, starting at $400.

If you’re looking to spend a bit less than the $400 starting price of the i3, there are other options to be aware of as well. The Roomba 614, for example, does a totally decent job and costs $250. It’s scheduling isn’t very clever, it doesn’t make maps, and it won’t empty itself, but it will absolutely help keep your floors clean as long as you don’t mind being a little bit more hands-on. (And there’s also Neato’s D4, which offers basic persistent maps—and lasers!—for $330.)

The other thing to consider if you’re trying to decide between the i3 and a more expensive Roomba is that without the camera, the i3 likely won’t be able to take advantage of nearly as many of the future improvements that iRobot has said it’s working on. Spending more money on a robot with additional sensors isn’t just buying what it can do now, but also investing in what it may be able to do later on, with its more sophisticated localization and ability to recognize objects. iRobot has promised major app updates every six months, and our guess is that most of the cool new stuff is going to show in the i7 and s9. So, if your top priority is just cleaner floors, the i3 is a solid choice. But if you want a part of what iRobot is working on next, the i3 might end up holding you back. Continue reading

Posted in Human Robots

#437673 Can AI and Automation Deliver a COVID-19 ...

Illustration: Marysia Machulska

Within moments of meeting each other at a conference last year, Nathan Collins and Yann Gaston-Mathé began devising a plan to work together. Gaston-Mathé runs a startup that applies automated software to the design of new drug candidates. Collins leads a team that uses an automated chemistry platform to synthesize new drug candidates.

“There was an obvious synergy between their technology and ours,” recalls Gaston-Mathé, CEO and cofounder of Paris-based Iktos.

In late 2019, the pair launched a project to create a brand-new antiviral drug that would block a specific protein exploited by influenza viruses. Then the COVID-19 pandemic erupted across the world stage, and Gaston-Mathé and Collins learned that the viral culprit, SARS-CoV-2, relied on a protein that was 97 percent similar to their influenza protein. The partners pivoted.

Their companies are just two of hundreds of biotech firms eager to overhaul the drug-discovery process, often with the aid of artificial intelligence (AI) tools. The first set of antiviral drugs to treat COVID-19 will likely come from sifting through existing drugs. Remdesivir, for example, was originally developed to treat Ebola, and it has been shown to speed the recovery of hospitalized COVID-19 patients. But a drug made for one condition often has side effects and limited potency when applied to another. If researchers can produce an ­antiviral that specifically targets SARS-CoV-2, the drug would likely be safer and more effective than a repurposed drug.

There’s one big problem: Traditional drug discovery is far too slow to react to a pandemic. Designing a drug from scratch typically takes three to five years—and that’s before human clinical trials. “Our goal, with the combination of AI and automation, is to reduce that down to six months or less,” says Collins, who is chief strategy officer at SRI Biosciences, a division of the Silicon Valley research nonprofit SRI International. “We want to get this to be very, very fast.”

That sentiment is shared by small biotech firms and big pharmaceutical companies alike, many of which are now ramping up automated technologies backed by supercomputing power to predict, design, and test new antivirals—for this pandemic as well as the next—with unprecedented speed and scope.

“The entire industry is embracing these tools,” says Kara Carter, president of the International Society for Antiviral Research and executive vice president of infectious disease at Evotec, a drug-discovery company in Hamburg. “Not only do we need [new antivirals] to treat the SARS-CoV-2 infection in the population, which is probably here to stay, but we’ll also need them to treat future agents that arrive.”

There are currentlyabout 200 known viruses that infect humans. Although viruses represent less than 14 percent of all known human pathogens, they make up two-thirds of all new human pathogens discovered since 1980.

Antiviral drugs are fundamentally different from vaccines, which teach a person’s immune system to mount a defense against a viral invader, and antibody treatments, which enhance the body’s immune response. By contrast, anti­virals are chemical compounds that directly block a virus after a person has become infected. They do this by binding to specific proteins and preventing them from functioning, so that the virus cannot copy itself or enter or exit a cell.

The SARS-CoV-2 virus has an estimated 25 to 29 proteins, but not all of them are suitable drug targets. Researchers are investigating, among other targets, the virus’s exterior spike protein, which binds to a receptor on a human cell; two scissorlike enzymes, called proteases, that cut up long strings of viral proteins into functional pieces inside the cell; and a polymerase complex that makes the cell churn out copies of the virus’s genetic material, in the form of single-stranded RNA.

But it’s not enough for a drug candidate to simply attach to a target protein. Chemists also consider how tightly the compound binds to its target, whether it binds to other things as well, how quickly it metabolizes in the body, and so on. A drug candidate may have 10 to 20 such objectives. “Very often those objectives can appear to be anticorrelated or contradictory with each other,” says Gaston-Mathé.

Compared with antibiotics, antiviral drug discovery has proceeded at a snail’s pace. Scientists advanced from isolating the first antibacterial molecules in 1910 to developing an arsenal of powerful antibiotics by 1944. By contrast, it took until 1951 for researchers to be able to routinely grow large amounts of virus particles in cells in a dish, a breakthrough that earned the inventors a Nobel Prize in Medicine in 1954.

And the lag between the discovery of a virus and the creation of a treatment can be heartbreaking. According to the World Health Organization, 71 million people worldwide have chronic hepatitis C, a major cause of liver cancer. The virus that causes the infection was discovered in 1989, but effective antiviral drugs didn’t hit the market until 2014.

While many antibiotics work on a range of microbes, most antivirals are highly specific to a single virus—what those in the business call “one bug, one drug.” It takes a detailed understanding of a virus to develop an antiviral against it, says Che Colpitts, a virologist at Queen’s University, in Canada, who works on antivirals against RNA viruses. “When a new virus emerges, like SARS-CoV-2, we’re at a big disadvantage.”

Making drugs to stop viruses is hard for three main reasons. First, viruses are the Spartans of the pathogen world: They’re frugal, brutal, and expert at evading the human immune system. About 20 to 250 nanometers in diameter, viruses rely on just a few parts to operate, hijacking host cells to reproduce and often destroying those cells upon departure. They employ tricks to camouflage their presence from the host’s immune system, including preventing infected cells from sending out molecular distress beacons. “Viruses are really small, so they only have a few components, so there’s not that many drug targets available to start with,” says Colpitts.

Second, viruses replicate quickly, typically doubling in number in hours or days. This constant copying of their genetic material enables viruses to evolve quickly, producing mutations able to sidestep drug effects. The virus that causes AIDS soon develops resistance when exposed to a single drug. That’s why a cocktail of antiviral drugs is used to treat HIV infection.

Finally, unlike bacteria, which can exist independently outside human cells, viruses invade human cells to propagate, so any drug designed to eliminate a virus needs to spare the host cell. A drug that fails to distinguish between a virus and a cell can cause serious side effects. “Discriminating between the two is really quite difficult,” says Evotec’s Carter, who has worked in antiviral drug discovery for over three decades.

And then there’s the money barrier. Developing antivirals is rarely profitable. Health-policy researchers at the London School of Economics recently estimated that the average cost of developing a new drug is US $1 billion, and up to $2.8 billion for cancer and other specialty drugs. Because antivirals are usually taken for only short periods of time or during short outbreaks of disease, companies rarely recoup what they spent developing the drug, much less turn a profit, says Carter.

To change the status quo, drug discovery needs fresh approaches that leverage new technologies, rather than incremental improvements, says Christian Tidona, managing director of BioMed X, an independent research institute in Heidelberg, Germany. “We need breakthroughs.”

Putting Drug Development on Autopilot
Earlier this year, SRI Biosciences and Iktos began collaborating on a way to use artificial intelligence and automated chemistry to rapidly identify new drugs to target the COVID-19 virus. Within four months, they had designed and synthesized a first round of antiviral candidates. Here’s how they’re doing it.

1/5

STEP 1: Iktos’s AI platform uses deep-learning algorithms in an iterative process to come up with new molecular structures likely to bind to and disable a specific coronavirus protein. Illustrations: Chris Philpot

2/5

STEP 2: SRI Biosciences’s SynFini system is a three-part automated chemistry suite for producing new compounds. Starting with a target compound from Iktos, SynRoute uses machine learning to analyze and optimize routes for creating that compound, with results in about 10 seconds. It prioritizes routes based on cost, likelihood of success, and ease of implementation.

3/5

STEP 3: SynJet, an automated inkjet printer platform, tests the routes by printing out tiny quantities of chemical ingredients to see how they react. If the right compound is produced, the platform tests it.

4/5

STEP 4: AutoSyn, an automated tabletop chemical plant, synthesizes milligrams to grams of the desired compound for further testing. Computer-selected “maps” dictate paths through the plant’s modular components.

5/5

STEP 5: The most promising compounds are tested against live virus samples.

Previous
Next

Iktos’s AI platform was created by a medicinal chemist and an AI expert. To tackle SARS-CoV-2, the company used generative models—deep-learning algorithms that generate new data—to “imagine” molecular structures with a good chance of disabling a key coronavirus protein.

For a new drug target, the software proposes and evaluates roughly 1 million compounds, says Gaston-Mathé. It’s an iterative process: At each step, the system generates 100 virtual compounds, which are tested in silico with predictive models to see how closely they meet the objectives. The test results are then used to design the next batch of compounds. “It’s like we have a very, very fast chemist who is designing compounds, testing compounds, getting back the data, then designing another batch of compounds,” he says.

The computer isn’t as smart as a human chemist, Gaston-Mathé notes, but it’s much faster, so it can explore far more of what people in the field call “chemical space”—the set of all possible organic compounds. Unexplored chemical space is huge: Biochemists estimate that there are at least 1063 possible druglike molecules, and that 99.9 percent of all possible small molecules or compounds have never been synthesized.

Still, designing a chemical compound isn’t the hardest part of creating a new drug. After a drug candidate is designed, it must be synthesized, and the highly manual process for synthesizing a new chemical hasn’t changed much in 200 years. It can take days to plan a synthesis process and then months to years to optimize it for manufacture.

That’s why Gaston-Mathé was eager to send Iktos’s AI-generated designs to Collins’s team at SRI Biosciences. With $13.8 million from the Defense Advanced Research Projects Agency, SRI Biosciences spent the last four years automating the synthesis process. The company’s automated suite of three technologies, called SynFini, can produce new chemical compounds in just hours or days, says Collins.

First, machine-learning software devises possible routes for making a desired molecule. Next, an inkjet printer platform tests the routes by printing out and mixing tiny quantities of chemical ingredients to see how they react with one another; if the right compound is produced, the platform runs tests on it. Finally, a tabletop chemical plant synthesizes milligrams to grams of the desired compound.

Less than four months after Iktos and SRI Biosciences announced their collaboration, they had designed and synthesized a first round of antiviral candidates for SARS-CoV-2. Now they’re testing how well the compounds work on actual samples of the virus.

Out of 10
63 possible druglike molecules, 99.9 percent have never been synthesized.

Theirs isn’t the only collaborationapplying new tools to drug discovery. In late March, Alex Zhavoronkov, CEO of Hong Kong–based Insilico Medicine, came across a YouTube video showing three virtual-reality avatars positioning colorful, sticklike fragments in the side of a bulbous blue protein. The three researchers were using VR to explore how compounds might bind to a SARS-CoV-2 enzyme. Zhavoronkov contacted the startup that created the simulation—Nanome, in San Diego—and invited it to examine Insilico’s ­AI-generated molecules in virtual reality.

Insilico runs an AI platform that uses biological data to train deep-learning algorithms, then uses those algorithms to identify molecules with druglike features that will likely bind to a protein target. A four-day training sprint in late January yielded 100 molecules that appear to bind to an important SARS-CoV-2 protease. The company recently began synthesizing some of those molecules for laboratory testing.

Nanome’s VR software, meanwhile, allows researchers to import a molecular structure, then view and manipulate it on the scale of individual atoms. Like human chess players who use computer programs to explore potential moves, chemists can use VR to predict how to make molecules more druglike, says Nanome CEO Steve McCloskey. “The tighter the interface between the human and the computer, the more information goes both ways,” he says.

Zhavoronkov sent data about several of Insilico’s compounds to Nanome, which re-created them in VR. Nanome’s chemist demonstrated chemical tweaks to potentially improve each compound. “It was a very good experience,” says Zhavoronkov.

Meanwhile, in March, Takeda Pharmaceutical Co., of Japan, invited Schrödinger, a New York–based company that develops chemical-simulation software, to join an alliance working on antivirals. Schrödinger’s AI focuses on the physics of how proteins interact with small molecules and one another.

The software sifts through billions of molecules per week to predict a compound’s properties, and it optimizes for multiple desired properties simultaneously, says Karen Akinsanya, chief biomedical scientist and head of discovery R&D at Schrödinger. “There’s a huge sense of urgency here to come up with a potent molecule, but also to come up with molecules that are going to be well tolerated” by the body, she says. Drug developers are seeking compounds that can be broadly used and easily administered, such as an oral drug rather than an intravenous drug, she adds.

Schrödinger evaluated four protein targets and performed virtual screens for two of them, a computing-intensive process. In June, Google Cloud donated the equivalent of 16 million hours of Nvidia GPU time for the company’s calculations. Next, the alliance’s drug companies will synthesize and test the most promising compounds identified by the virtual screens.

Other companies, including Amazon Web Services, IBM, and Intel, as well as several U.S. national labs are also donating time and resources to the Covid-19 High Performance Computing Consortium. The consortium is supporting 87 projects, which now have access to 6.8 million CPU cores, 50,000 GPUs, and 600 petaflops of computational resources.

While advanced technologies could transform early drug discovery, any new drug candidate still has a long road after that. It must be tested in animals, manufactured in large batches for clinical trials, then tested in a series of trials that, for antivirals, lasts an average of seven years.

In May, the BioMed X Institute in Germany launched a five-year project to build a Rapid Antiviral Response Platform, which would speed drug discovery all the way through manufacturing for clinical trials. The €40 million ($47 million) project, backed by drug companies, will identify ­outside-the-box proposals from young scientists, then provide space and funding to develop their ideas.

“We’ll focus on technologies that allow us to go from identification of a new virus to 10,000 doses of a novel potential therapeutic ready for trials in less than six months,” says BioMed X’s Tidona, who leads the project.

While a vaccine will likely arrive long before a bespoke antiviral does, experts expect COVID-19 to be with us for a long time, so the effort to develop a direct-acting, potent antiviral continues. Plus, having new antivirals—and tools to rapidly create more—can only help us prepare for the next pandemic, whether it comes next month or in another 102 years.

“We’ve got to start thinking differently about how to be more responsive to these kinds of threats,” says Collins. “It’s pushing us out of our comfort zones.”

This article appears in the October 2020 print issue as “Automating Antivirals.” Continue reading

Posted in Human Robots

#437645 How Robots Became Essential Workers in ...

Photo: Sivaram V/Reuters

A robot, developed by Asimov Robotics to spread awareness about the coronavirus, holds a tray with face masks and sanitizer.

As the coronavirus emergency exploded into a full-blown pandemic in early 2020, forcing countless businesses to shutter, robot-making companies found themselves in an unusual situation: Many saw a surge in orders. Robots don’t need masks, can be easily disinfected, and, of course, they don’t get sick.

An army of automatons has since been deployed all over the world to help with the crisis: They are monitoring patients, sanitizing hospitals, making deliveries, and helping frontline medical workers reduce their exposure to the virus. Not all robots operate autonomously—many, in fact, require direct human supervision, and most are limited to simple, repetitive tasks. But robot makers say the experience they’ve gained during this trial-by-fire deployment will make their future machines smarter and more capable. These photos illustrate how robots are helping us fight this pandemic—and how they might be able to assist with the next one.

DROID TEAM

Photo: Clement Uwiringiyimana/Reuters

A squad of robots serves as the first line of defense against person-to-person transmission at a medical center in Kigali, Rwanda. Patients walking into the facility get their temperature checked by the machines, which are equipped with thermal cameras atop their heads. Developed by UBTech Robotics, in China, the robots also use their distinctive appearance—they resemble characters out of a Star Wars movie—to get people’s attention and remind them to wash their hands and wear masks.

Photo: Clement Uwiringiyimana/Reuters

SAY “AAH”
To speed up COVID-19 testing, a team of Danish doctors and engineers at the University of Southern Denmark and at Lifeline Robotics is developing a fully automated swab robot. It uses computer vision and machine learning to identify the perfect target spot inside the person’s throat; then a robotic arm with a long swab reaches in to collect the sample—all done with a swiftness and consistency that humans can’t match. In this photo, one of the creators, Esben Østergaard, puts his neck on the line to demonstrate that the robot is safe.

Photo: University of Southern Denmark

GERM ZAPPER
After six of its doctors became infected with the coronavirus, the Sassarese hospital in Sardinia, Italy, tightened its safety measures. It also brought in the robots. The machines, developed by UVD Robots, use lidar to navigate autonomously. Each bot carries an array of powerful short-wavelength ultraviolet-C lights that destroy the genetic material of viruses and other pathogens after a few minutes of exposure. Now there is a spike in demand for UV-disinfection robots as hospitals worldwide deploy them to sterilize intensive care units and operating theaters.

Photo: UVD Robots

RUNNING ERRANDS

In medical facilities, an ideal role for robots is taking over repetitive chores so that nurses and physicians can spend their time doing more important tasks. At Shenzhen Third People’s Hospital, in China, a robot called Aimbot drives down the hallways, enforcing face-mask and social-distancing rules and spraying disinfectant. At a hospital near Austin, Texas, a humanoid robot developed by Diligent Robotics fetches supplies and brings them to patients’ rooms. It repeats this task day and night, tirelessly, allowing the hospital staff to spend more time interacting with patients.

Photos, left: Diligent Robotics; Right: UBTech Robotics

THE DOCTOR IS IN
Nurses and doctors at Circolo Hospital in Varese, in northern Italy—the country’s hardest-hit region—use robots as their avatars, enabling them to check on their patients around the clock while minimizing exposure and conserving protective equipment. The robots, developed by Chinese firm Sanbot, are equipped with cameras and microphones and can also access patient data like blood oxygen levels. Telepresence robots, originally designed for offices, are becoming an invaluable tool for medical workers treating highly infectious diseases like COVID-19, reducing the risk that they’ll contract the pathogen they’re fighting against.

Photo: Miguel Medina/AFP/Getty Images

HELP FROM ABOVE

Photo: Zipline

Authorities in several countries attempted to use drones to enforce lockdowns and social-distancing rules, but the effectiveness of such measures remains unclear. A better use of drones was for making deliveries. In the United States, startup Zipline deployed its fixed-wing autonomous aircraft to connect two medical facilities 17 kilometers apart. For the staff at the Huntersville Medical Center, in North Carolina, masks, gowns, and gloves literally fell from the skies. The hope is that drones like Zipline’s will one day be able to deliver other kinds of critical materials, transport test samples, and distribute drugs and vaccines.

Photos: Zipline

SPECIAL DELIVERY
It’s not quite a robot takeover, but the streets and sidewalks of dozens of cities around the world have seen a proliferation of hurrying wheeled machines. Delivery robots are now in high demand as online orders continue to skyrocket.

In Hamburg, the six-wheeled robots developed by Starship Technologies navigate using cameras, GPS, and radar to bring groceries to customers.

Photo: Christian Charisius/Picture Alliance/Getty Images

In Medellín, Colombia, a startup called Rappi deployed a fleet of robots, built by Kiwibot, to deliver takeout to people in lockdown.

Photo: Joaquin Sarmiento/AFP/Getty Images

China’s JD.com, one of the country’s largest e-commerce companies, is using 20 robots to transport goods in Changsha, Hunan province; each vehicle has 22 separate compartments, which customers unlock using face authentication.

Photos: TPG/Getty Images

LIFE THROUGH ROBOTS
Robots can’t replace real human interaction, of course, but they can help people feel more connected at a time when meetings and other social activities are mostly on hold.

In Ostend, Belgium, ZoraBots brought one of its waist-high robots, equipped with cameras, microphones, and a screen, to a nursing home, allowing residents like Jozef Gouwy to virtually communicate with loved ones despite a ban on in-person visits.

Photo: Yves Herman/Reuters

In Manila, nearly 200 high school students took turns “teleporting” into a tall wheeled robot, developed by the school’s robotics club, to walk on stage during their graduation ceremony.

Photo: Ezra Acayan/Getty Images

And while Japan’s Chiba Zoological Park was temporarily closed due to the pandemic, the zoo used an autonomous robotic vehicle called RakuRo, equipped with 360-degree cameras, to offer virtual tours to children quarantined at home.

Photo: Tomohiro Ohsumi/Getty Images

SENTRY ROBOTS
Offices, stores, and medical centers are adopting robots as enforcers of a new coronavirus code.

At Fortis Hospital in Bangalore, India, a robot called Mitra uses a thermal camera to perform a preliminary screening of patients.

Photo: Manjunath Kiran/AFP/Getty Images

In Tunisia, the police use a tanklike robot to patrol the streets of its capital city, Tunis, verifying that citizens have permission to go out during curfew hours.

Photo: Khaled Nasraoui/Picture Alliance/Getty Images

And in Singapore, the Bishan-Ang Moh Kio Park unleashed a Spot robot dog, developed by Boston Dynamics, to search for social-distancing violators. Spot won’t bark at them but will rather play a recorded message reminding park-goers to keep their distance.

Photo: Roslan Rahman/AFP/Getty Images

This article appears in the October 2020 print issue as “How Robots Became Essential Workers.” Continue reading

Posted in Human Robots

#437639 Boston Dynamics’ Spot Is Helping ...

In terms of places where you absolutely want a robot to go instead of you, what remains of the utterly destroyed Chernobyl Reactor 4 should be very near the top of your list. The reactor, which suffered a catastrophic meltdown in 1986, has been covered up in almost every way possible in an effort to keep its nuclear core contained. But eventually, that nuclear material is going to have to be dealt with somehow, and in order to do that, it’s important to understand which bits of it are just really bad, and which bits are the actual worst. And this is where Spot is stepping in to help.

The big open space that Spot is walking through is right next to what’s left of Reactor 4. Within six months of the disaster, Reactor 4 was covered in a sarcophagus made of concrete and steel to try and keep all the nasty nuclear fuel from leaking out more than it already had, and it still contains “30 tons of highly contaminated dust, 16 tons of uranium and plutonium, and 200 tons of radioactive lava.” Oof. Over the next 10 years, the sarcophagus slowly deteriorated, and despite the addition of that gigantic network of steel support beams that you can see in the video, in the late 1990s it was decided to erect an enormous building over the entire mess to try and stabilize it for as long as possible.

Reactor 4 is now snugly inside the massive New Safe Confinement (NSC) structure, and the idea is that eventually, the structure will allow for the safe disassembly of what’s left of the reactor, although nobody is quite sure how to do that. This is all just to say that the area inside of the containment structure offers a lot of good opportunities for robots to take over from humans.

This particular Spot is owned by the U.K. Atomic Energy Authority, and was packed off to Russia with the assistance of the Robotics and Artificial Intelligence in Nuclear (RAIN) initiative and the National Centre for Nuclear Robotics. Dr. Dave Megson-Smith, who is a researcher at the University of Bristol, in the U.K., and part of the Hot Robotics Facility at the National Nuclear User Facility, was one of the scientists lucky enough to accompany Spot on its adventure. Megson-Smith specializes in sensor development, and he equipped Spot with a collimated radiation sensor in addition to its mapping payload. “We actually built a map of the radiation coming out of the front wall of Chernobyl power plant as we were in there with it,” Megson-Smith told us, and was able to share this picture, which shows a map of gamma photon count rate:

Image: University of Bristol

Researchers equipped Spot with a collimated radiation sensor and use one of the data readings (gamma photon count rate) to create a map of the radiation coming out of the front wall of the Chernobyl power plant.

So what’s the reason you’d want to use a very expensive legged robot to wander around what looks like a very flat and robot friendly floor? As it turns out, the floor is very dusty in there, and a priority inside the NSC is to keep dust down as much as possible, since the dust is radioactive and gets on everything and is consequently the easiest way for radioactivity to escape the NSC. “You want to minimize picking up material, so we consider the total contact surface area,” says Megson-Smith. “If you use a legged system rather than a wheeled or tracked system, you have a much smaller footprint and you disturb the environment a lot less.” While it’s nice that Spot is nimble and can climb stairs and stuff, tracked vehicles can do that as well, so in this case, the primary driving factor of choosing a robot to work inside Chernobyl is minimizing those contact points.

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker”

Right now, routine weekly measurements in contaminated spaces at Chernobyl are done by humans, which puts those humans at risk. Spot, or a robot like it, could potentially take over from those humans, as a sort of “automated safety checker” able to work in medium level contaminated environments.” As far as more dangerous areas go, there’s a lot of uncertainty about what Spot is actually capable of, according to Megson-Smith. “What you think the problems are, and what the industry thinks the problems are, are subtly different things.

We were thinking that we’d have to make robots incredibly radiation proof to go into these contaminated environments, but they said, “can you just give us a system that we can send into places where humans already can go, but where we just don’t want to send humans.” Making robots incredibly radiation proof is challenging, and without extensive testing and ruggedizing, failures can be frequent, as many robots discovered at Fukushima. Indeed, Megson-Smith that in Fukushima there’s a particular section that’s known as a “robot graveyard” where robots just go to die, and they’ve had to up their standards again and again to keep the robots from failing. “So the thing they’re worried about with Spot is, what is its tolerance? What components will fail, and what can we do to harden it?” he says. “We’re approaching Boston Dynamics at the moment to see if they’ll work with us to address some of those questions.

There’s been a small amount of testing of how robots fair under harsh radiation, Megson-Smith told us, including (relatively recently) a KUKA LBR800 arm, which “stopped operating after a large radiation dose of 164.55(±1.09) Gy to its end effector, and the component causing the failure was an optical encoder.” And in case you’re wondering how much radiation that is, a 1 to 2 Gy dose to the entire body gets you acute radiation sickness and possibly death, while 8 Gy is usually just straight-up death. The goal here is not to kill robots (I mean, it sort of is), but as Megson-Smith says, “if we can work out what the weak points are in a robotic system, can we address those, can we redesign those, or at least understand when they might start to fail?” Now all he has to do is convince Boston Dynamics to send them a Spot that they can zap until it keels over.

The goal for Spot in the short term is fully autonomous radiation mapping, which seems very possible. It’ll also get tested with a wider range of sensor packages, and (happily for the robot) this will all take place safely back at home in the U.K. As far as Chernobyl is concerned, robots will likely have a substantial role to play in the near future. “Ultimately, Chernobyl has to be taken apart and decommissioned. That’s the long-term plan for the facility. To do that, you first need to understand everything, which is where we come in with our sensor systems and robotic platforms,” Megson-Smith tells us. “Since there are entire swathes of the Chernobyl nuclear plant where people can’t go in, we’d need robots like Spot to do those environmental characterizations.” Continue reading

Posted in Human Robots