Tag Archives: university

#437695 Video Friday: Even Robots Know That You ...

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]
Other Than Human – September 3-10, 2020 – Stockholm, Sweden
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.

From the Robotics and Perception Group at UZH comes Flightmare, a simulation environment for drones that combines a slick rendering engine with a robust physics engine that can run as fast as your system can handle.

Flightmare is composed of two main components: a configurable rendering engine built on Unity and a flexible physics engine for dynamics simulation. Those two components are totally decoupled and can run independently from each other. Flightmare comes with several desirable features: (i) a large multi-modal sensor suite, including an interface to extract the 3D point-cloud of the scene; (ii) an API for reinforcement learning which can simulate hundreds of quadrotors in parallel; and (iii) an integration with a virtual-reality headset for interaction with the simulated environment. Flightmare can be used for various applications, including path-planning, reinforcement learning, visual-inertial odometry, deep learning, human-robot interaction, etc.

[ Flightmare ]

Quadruped robots yelling at people to maintain social distancing is really starting to become a thing, for better or worse.

We introduce a fully autonomous surveillance robot based on a quadruped platform that can promote social distancing in complex urban environments. Specifically, to achieve autonomy, we mount multiple cameras and a 3D LiDAR on the legged robot. The robot then uses an onboard real-time social distancing detection system to track nearby pedestrian groups. Next, the robot uses a crowd-aware navigation algorithm to move freely in highly dynamic scenarios. The robot finally uses a crowd aware routing algorithm to effectively promote social distancing by using human-friendly verbal cues to send suggestions to overcrowded pedestrians.

[ Project ]

Thanks Fan!

The Personal Robotics Group at Oregon State University is looking at UV germicidal irradiation for surface disinfection with a Fetch Manipulator Robot.

Fetch Robot disinfecting dance party woo!

[ Oregon State ]

How could you not take a mask from this robot?

[ Reachy ]

This work presents the design, development and autonomous navigation of the alpha-version of our Resilient Micro Flyer, a new type of collision-tolerant small aerial robot tailored to traversing and searching within highly confined environments including manhole-sized tubes. The robot is particularly lightweight and agile, while it implements a rigid collision-tolerant design which renders it resilient during forcible interaction with the environment. Furthermore, the design of the system is enhanced through passive flaps ensuring smoother and more compliant collision which was identified to be especially useful in very confined settings.

[ ARL ]

Pepper can make maps and autonomously navigate, which is interesting, but not as interesting as its posture when it's wandering around.

Dat backing into the charging dock tho.

[ Pepper ]

RatChair a strategy for displacing big objects by attaching relatively small vibration sources. After learning how several random bursts of vibration affect its pose, an optimization algorithm discovers the optimal sequence of vibration patterns required to (slowly but surely) move the object to a specified position.

This is from 2015, why isn't all of my furniture autonomous yet?!

[ KAIST ]

The new SeaDrone Pro is designed to be the underwater equivalent of a quadrotor. This video is a rendering, but we've been assured that it does actually exist.

[ SeaDrone ]

Thanks Eduardo!

Porous Loops is a lightweight composite facade panel that shows the potential of 3D printing of mineral foams for building scale applications.

[ ETH ]

Thanks Fan!

Here's an interesting idea for a robotic gripper- it's what appears to be a snap bracelet coupled to a pneumatic actuator that allows the snap bracelet to be reset.

[ Georgia Tech ]

Graze is developing a commercial robotic lawnmower. They're also doing a sort of crowdfunded investment thing, which probably explains the painfully overproduced nature of the following video:

A couple things about this: the hard part, which the video skips over almost entirely, is the mapping, localization, and understanding where to mow and where not to mow. The pitch deck seems to suggest that this is mostly done through computer vision, a thing that's perhaps easy to do under controlled ideal conditions, but difficult to apply to a world full lawns that are all different. The commercial aspect is interesting because golf courses are likely as standardized as you can get, but the emphasis here on how much money they can make without really addressing any of the technical stuff makes me raise an eyebrow or two.

[ Graze ]

The record & playback X-series arm demo allows the user to record the arm's movements while motors are torqued off. Then, the user may torque the motor's on and watch the movements they just made playback!

[ Interbotix ]

Shadow Robot has a new teleop system for its hand. I'm guessing that it's even trickier to use than it looks.

[ Shadow Robot ]

Quanser Interactive Labs is a collection of virtual hardware-based laboratory activities that supplement traditional or online courses. Same as working with physical systems in the lab, students work with virtual twins of Quanser's most popular plants, develop their mathematical models, implement and simulate the dynamic behavior of these systems, design controllers, and validate them on a high-fidelity 3D real-time virtual models. The virtual systems not only look like the real ones, they also behave, can be manipulated, measured, and controlled like real devices. And finally, when students go to the lab, they can deploy their virtually-validated designs on actual physical equipment.

[ Quanser ]

This video shows robot-assisted heart surgery. It's amazing to watch if you haven't seen this sort of thing before, but be aware that there is a lot of blood.

This video demonstrates a fascinating case of robotic left atrial myxoma excision, narrated by Joel Dunning, Middlesbrough, UK. The Robotic platform provides superior visualisation and enhanced dexterity, through keyhole incisions. Robotic surgery is an integral part of our Minimally Invasive Cardiothoracic Surgery Program.

[ Tristan D. Yan ]

Thanks Fan!

In this talk, we present our work on learning control policies directly in simulation that are deployed onto real drones without any fine tuning. The presentation covers autonomous drone racing, drone acrobatics, and uncertainty estimation in deep networks.

[ RPG ] Continue reading

Posted in Human Robots

#437689 GITAI Sending Autonomous Robot to Space ...

We’ve been keeping a close watch on GITAI since early last year—what caught our interest initially is the history of the company, which includes a bunch of folks who started in the JSK Lab at the University of Tokyo, won the DARPA Robotics Challenge Trials as SCHAFT, got swallowed by Google, narrowly avoided being swallowed by SoftBank, and are now designing robots that can work in space.

The GITAI YouTube channel has kept us more to less up to date on their progress so far, and GITAI has recently announced the next step in this effort: The deployment of one of their robots on board the International Space Station in 2021.

Photo: GITAI

GITAI’s S1 is a task-specific 8-degrees-of-freedom arm with an integrated sensing and computing system and 1-meter reach.

GITAI has been working on a variety of robots for space operations, the most sophisticated of which is a humanoid torso called G1, which is controlled through an immersive telepresence system. What will be launching into space next year is a more task-specific system called the S1, which is an 8-degrees-of-freedom arm with an integrated sensing and computing system that can be wall-mounted and has a 1-meter reach.

The S1 will be living on board a commercially funded, pressurized airlock-extension module called Bishop, developed by NanoRacks. Mounted on the inside of the Bishop module, the S1 will have access to a task board and a small assembly area, where it will demonstrate common crew intra-vehicular activity, or IVA—tasks like flipping switches, turning knobs, and managing cables. It’ll also do some in-space assembly, or ISA, attaching panels to create a solar array.

Here’s a demonstration of some task board activities, conducted on Earth in a mockup of Bishop:

GITAI says that “all operations conducted by the S1 GITAI robotic arm will be autonomous, followed by some teleoperations from Nanoracks’ in-house mission control.” This is interesting, because from what we’ve seen until now, GITAI has had a heavy emphasis on telepresence, with a human in the loop to get stuff done. As GITAI’s founder and CEO Sho Nakanose commented to us a year ago, “Telepresence robots have far better performance and can be made practical much quicker than autonomous robots, so first we are working on making telepresence robots practical.”

So what’s changed? “GITAI has been concentrating on teleoperations to demonstrate the dexterity of our robot, but now it’s time to show our capabilities to do the same this time with autonomy,” Nakanose told us last week. “In an environment with minimum communication latency, it would be preferable to operate a robot more with teleoperations to enhance the capability of the robot, since with the current technology level of AI, what a robot can do autonomously is very limited. However, in an environment where the latency becomes noticeable, it would become more efficient to have a mixture of autonomy and teleoperations depending on the application. Eventually, in an ideal world, a robot will operate almost fully autonomously with minimum human cognizance.”

“In an environment where the latency becomes noticeable, it would become more efficient to have a mixture of autonomy and teleoperations depending on the application. Eventually, in an ideal world, a robot will operate almost fully autonomously with minimum human cognizance.”
—Sho Nakanose, GITAI founder and CEO

Nakanose says that this mission will help GITAI to “acquire the skills, know-how, and experience necessary to prepare a robot to be ISS compatible, prov[ing] the maturity of our technology in the microgravity environment.” Success would mean conducting both IVA and ISA experiments as planned (autonomous and teleop for IVA, fully autonomous for ISA), which would be pretty awesome, but we’re told that GITAI has already received a research and development order for space robots from a private space company, and Nakanose expects that “by the mid-2020s, we will be able to show GITAI's robots working in space on an actual mission.”

NanoRacks is schedule to launch the Bishop module on SpaceX CRS-21 in November. The S1 will be launched separately in 2021, and a NASA astronaut will install the robot and then leave it alone to let it start demonstrating how work in space can be made both safer and cheaper once the humans have gotten out of the way. Continue reading

Posted in Human Robots

#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

#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

#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