Tag Archives: material

#437721 Video Friday: Child Robot Learning to ...

Video Friday is your weekly selection of awesome robotics videos, collected by your Automaton bloggers. We’ll also be posting a weekly calendar of upcoming robotics events for the next few months; here’s what we have so far (send us your events!):

CLAWAR 2020 – August 24-26, 2020 – [Online Conference]
ICUAS 2020 – September 1-4, 2020 – Athens, Greece
ICRES 2020 – September 28-29, 2020 – Taipei, Taiwan
AUVSI EXPONENTIAL 2020 – October 5-8, 2020 – [Online Conference]
IROS 2020 – October 25-29, 2020 – Las Vegas, Nev., USA
CYBATHLON 2020 – November 13-14, 2020 – [Online Event]
ICSR 2020 – November 14-16, 2020 – Golden, Colo., USA
Let us know if you have suggestions for next week, and enjoy today’s videos.

We first met Ibuki, Hiroshi Ishiguro’s latest humanoid robot, a couple of years ago. A recent video shows how Ishiguro and his team are teaching the robot to express its emotional state through gait and body posture while moving.

This paper presents a subjective evaluation of the emotions of a wheeled mobile humanoid robot expressing emotions during movement by replicating human gait-induced upper body motion. For this purpose, we proposed the robot equipped with a vertical oscillation mechanism that generates such motion by focusing on human center-of-mass trajectory. In the experiment, participants watched videos of the robot’s different emotional gait-induced upper body motions, and assess the type of emotion shown, and their confidence level in their answer.

[ Hiroshi Ishiguro Lab ] via [ RobotStart ]

ICYMI: This is a zinc-air battery made partly of Kevlar that can be used to support weight, not just add to it.

Like biological fat reserves store energy in animals, a new rechargeable zinc battery integrates into the structure of a robot to provide much more energy, a team led by the University of Michigan has shown.

The new battery works by passing hydroxide ions between a zinc electrode and the air side through an electrolyte membrane. That membrane is partly a network of aramid nanofibers—the carbon-based fibers found in Kevlar vests—and a new water-based polymer gel. The gel helps shuttle the hydroxide ions between the electrodes. Made with cheap, abundant and largely nontoxic materials, the battery is more environmentally friendly than those currently in use. The gel and aramid nanofibers will not catch fire if the battery is damaged, unlike the flammable electrolyte in lithium ion batteries. The aramid nanofibers could be upcycled from retired body armor.

[ University of Michigan ]

In what they say is the first large-scale study of the interactions between sound and robotic action, researchers at CMU’s Robotics Institute found that sounds could help a robot differentiate between objects, such as a metal screwdriver and a metal wrench. Hearing also could help robots determine what type of action caused a sound and help them use sounds to predict the physical properties of new objects.

[ CMU ]

Captured on Aug. 11 during the second rehearsal of the OSIRIS-REx mission’s sample collection event, this series of images shows the SamCam imager’s field of view as the NASA spacecraft approaches asteroid Bennu’s surface. The rehearsal brought the spacecraft through the first three maneuvers of the sampling sequence to a point approximately 131 feet (40 meters) above the surface, after which the spacecraft performed a back-away burn.

These images were captured over a 13.5-minute period. The imaging sequence begins at approximately 420 feet (128 meters) above the surface – before the spacecraft executes the “Checkpoint” maneuver – and runs through to the “Matchpoint” maneuver, with the last image taken approximately 144 feet (44 meters) above the surface of Bennu.

[ NASA ]

The DARPA AlphaDogfight Trials Final Event took place yesterday; the livestream is like 5 hours long, but you can skip ahead to 4:39 ish to see the AI winner take on a human F-16 pilot in simulation.

Some things to keep in mind about the result: The AI had perfect situational knowledge while the human pilot had to use eyeballs, and in particular, the AI did very well at lining up its (virtual) gun with the human during fast passing maneuvers, which is the sort of thing that autonomous systems excel at but is not necessarily reflective of better strategy.

[ DARPA ]

Coming soon from Clearpath Robotics!

[ Clearpath ]

This video introduces Preferred Networks’ Hand type A, a tendon-driven robot gripper with passively switchable underactuated surface.

[ Preferred Networks ]

CYBATHLON 2020 will take place on 13 – 14 November 2020 – at the teams’ home bases. They will set up their infrastructure for the competition and film their races. Instead of starting directly next to each other, the pilots will start individually and under the supervision of CYBATHLON officials. From Zurich, the competitions will be broadcast through a new platform in a unique live programme.

[ Cybathlon ]

In this project, we consider the task of autonomous car racing in the top-selling car racing game Gran Turismo Sport. Gran Turismo Sport is known for its detailed physics simulation of various cars and tracks. Our approach makes use of maximum-entropy deep reinforcement learning and a new reward design to train a sensorimotor policy to complete a given race track as fast as possible. We evaluate our approach in three different time trial settings with different cars and tracks. Our results show that the obtained controllers not only beat the built-in non-player character of Gran Turismo Sport, but also outperform the fastest known times in a dataset of personal best lap times of over 50,000 human drivers.

[ UZH ]

With the help of the software pitasc from Fraunhofer IPA, an assembly task is no longer programmed point by point, but workpiece-related. Thus, pitasc adapts the assembly process itself for new product variants with the help of updated parameters.

[ Fraunhofer ]

In this video, a multi-material robot simulator is used to design a shape-changing robot, which is then transferred to physical hardware. The simulated and real robots can use shape change to switch between rolling gaits and inchworm gaits, to locomote in multiple environments.

[ Yale ]

This work presents a novel loco-manipulation control framework for the execution of complex tasks with kinodynamic constraints using mobile manipulators. As a representative example, we consider the handling and re-positioning of pallet jacks in unstructured environments. While these results reveal with a proof-of- concept the effectiveness of the proposed framework, they also demonstrate the high potential of mobile manipulators for relieving human workers from such repetitive and labor intensive tasks. We believe that this extended functionality can contribute to increasing the usability of mobile manipulators in different application scenarios.

[ Paper ] via [ IIT ]

I don’t know why this dinosaur ice cream serving robot needs to blow smoke out of its nose, but I like it.

[ Connected Robotics ] via [ RobotStart ]

Guardian S remote visual inspection and surveillance robots make laying cable runs in confined or hard to reach spaces easy. With advanced maneuverability and the ability to climb vertical, ferrous surfaces, the robot reaches areas that are not always easily accessible.

[ Sarcos ]

Looks like the company that bought Anki is working on an add-on to let cars charge while they drive.

[ Digital Dream Labs ]

Chris Atkeson gives a brief talk for the CMU Robotics Institute orientation.

[ CMU RI ]

A UofT Robotics Seminar, featuring Russ Tedrake from MIT and TRI on “Feedback Control for Manipulation.”

Control theory has an answer for just about everything, but seems to fall short when it comes to closing a feedback loop using a camera, dealing with the dynamics of contact, and reasoning about robustness over the distribution of tasks one might find in the kitchen. Recent examples from RL and imitation learning demonstrate great promise, but don’t leverage the rigorous tools from systems theory. I’d like to discuss why, and describe some recent results of closing feedback loops from pixels for “category-level” robot manipulation.

[ UofT ] 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.

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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.

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

Previous
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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

#437671 Video Friday: Researchers 3D Print ...

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]
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.

The Giant Gundam in Yokohama is actually way cooler than I thought it was going to be.

[ Gundam Factory ] via [ YouTube ]

A new 3D-printing method will make it easier to manufacture and control the shape of soft robots, artificial muscles and wearable devices. Researchers at UC San Diego show that by controlling the printing temperature of liquid crystal elastomer, or LCE, they can control the material’s degree of stiffness and ability to contract—also known as degree of actuation. What’s more, they are able to change the stiffness of different areas in the same material by exposing it to heat.

[ UCSD ]

Thanks Ioana!

This is the first successful reactive stepping test on our new torque-controlled biped robot named Bolt. The robot has 3 active degrees of freedom per leg and one passive joint in ankle. Since there is no active joint in ankle, the robot only relies on step location and timing adaptation to stabilize its motion. Not only can the robot perform stepping without active ankles, but it is also capable of rejecting external disturbances as we showed in this video.

[ ODRI ]

The curling robot “Curly” is the first AI-based robot to demonstrate competitive curling skills in an icy real environment with its high uncertainties. Scientists from seven different Korean research institutions including Prof. Klaus-Robert Müller, head of the machine-learning group at TU Berlin and guest professor at Korea University, have developed an AI-based curling robot.

[ TU Berlin ]

MoonRanger, a small robotic rover being developed by Carnegie Mellon University and its spinoff Astrobotic, has completed its preliminary design review in preparation for a 2022 mission to search for signs of water at the moon’s south pole. Red Whittaker explains why the new MoonRanger Lunar Explorer design is innovative and different from prior planetary rovers.

[ CMU ]

Cobalt’s security robot can now navigate unmodified elevators, which is an impressive feat.

Also, EXTERMINATE!

[ Cobalt ]

OrionStar, the robotics company invested in by Cheetah Mobile, announced the Robotic Coffee Master. Incorporating 3,000 hours of AI learning, 30,000 hours of robotic arm testing and machine vision training, the Robotic Coffee Master can perform complex brewing techniques, such as curves and spirals, with millimeter-level stability and accuracy (reset error ≤ 0.1mm).

[ Cheetah Mobile ]

DARPA OFFensive Swarm-Enabled Tactics (OFFSET) researchers recently tested swarms of autonomous air and ground vehicles at the Leschi Town Combined Arms Collective Training Facility (CACTF), located at Joint Base Lewis-McChord (JBLM) in Washington. The Leschi Town field experiment is the fourth of six planned experiments for the OFFSET program, which seeks to develop large-scale teams of collaborative autonomous systems capable of supporting ground forces operating in urban environments.

[ DARPA ]

Here are some highlights from Team Explorer’s SubT Urban competition back in February.

[ Team Explorer ]

Researchers with the Skoltech Intelligent Space Robotics Laboratory have developed a system that allows easy interaction with a micro-quadcopter with LEDs that can be used for light-painting. The researchers used a 92x92x29 mm Crazyflie 2.0 quadrotor that weighs just 27 grams, equipped with a light reflector and an array of controllable RGB LEDs. The control system consists of a glove equipped with an inertial measurement unit (IMU; an electronic device that tracks the movement of a user’s hand), and a base station that runs a machine learning algorithm.

[ Skoltech ]

“DeKonBot” is the prototype of a cleaning and disinfection robot for potentially contaminated surfaces in buildings such as door handles, light switches or elevator buttons. While other cleaning robots often spray the cleaning agents over a large area, DeKonBot autonomously identifies the surface to be cleaned.

[ Fraunhofer IPA ]

On Oct. 20, the OSIRIS-REx mission will perform the first attempt of its Touch-And-Go (TAG) sample collection event. Not only will the spacecraft navigate to the surface using innovative navigation techniques, but it could also collect the largest sample since the Apollo missions.

[ NASA ]

With all the robotics research that seems to happen in places where snow is more of an occasional novelty or annoyance, it’s good to see NORLAB taking things more seriously

[ NORLAB ]

Telexistence’s Model-T robot works very slowly, but very safely, restocking shelves.

[ Telexistence ] via [ YouTube ]

Roboy 3.0 will be unveiled next month!

[ Roboy ]

KUKA ready2_educate is your training cell for hands-on education in robotics. It is especially aimed at schools, universities and company training facilities. The training cell is a complete starter package and your perfect partner for entry into robotics.

[ KUKA ]

A UPenn GRASP Lab Special Seminar on Data Driven Perception for Autonomy, presented by Dapo Afolabi from UC Berkeley.

Perception systems form a crucial part of autonomous and artificial intelligence systems since they convert data about the relationship between an autonomous system and its environment into meaningful information. Perception systems can be difficult to build since they may involve modeling complex physical systems or other autonomous agents. In such scenarios, data driven models may be used to augment physics based models for perception. In this talk, I will present work making use of data driven models for perception tasks, highlighting the benefit of such approaches for autonomous systems.

[ GRASP Lab ]

A Maryland Robotics Center Special Robotics Seminar on Underwater Autonomy, presented by Ioannis Rekleitis from the University of South Carolina.

This talk presents an overview of algorithmic problems related to marine robotics, with a particular focus on increasing the autonomy of robotic systems in challenging environments. I will talk about vision-based state estimation and mapping of underwater caves. An application of monitoring coral reefs is going to be discussed. I will also talk about several vehicles used at the University of South Carolina such as drifters, underwater, and surface vehicles. In addition, a short overview of the current projects will be discussed. The work that I will present has a strong algorithmic flavour, while it is validated in real hardware. Experimental results from several testing campaigns will be presented.

[ MRC ]

This week’s CMU RI Seminar comes from Scott Niekum at UT Austin, on Scaling Probabilistically Safe Learning to Robotics.

Before learning robots can be deployed in the real world, it is critical that probabilistic guarantees can be made about the safety and performance of such systems. This talk focuses on new developments in three key areas for scaling safe learning to robotics: (1) a theory of safe imitation learning; (2) scalable reward inference in the absence of models; (3) efficient off-policy policy evaluation. The proposed algorithms offer a blend of safety and practicality, making a significant step towards safe robot learning with modest amounts of real-world data.

[ CMU RI ] 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