Tag Archives: time

#437896 Solar-based Electronic Skin Generates ...

Posted on December 13, 2020 by Android

Replicating the human sense of touch is complicated—electronic skins need to be flexible, stretchable, and sensitive to temperature, pressure and texture; they need to be able to read biological data and provide electronic readouts. Therefore, how to power electronic skin for continuous, real-time use is a big challenge.

To address this, researchers from Glasgow University have developed an energy-generating e-skin made out of miniaturized solar cells, without dedicated touch sensors. The solar cells not only generate their own power—and some surplus—but also provide tactile capabilities for touch and proximity sensing. An early-view paper of their findings was published in IEEE Transactions on Robotics.

When exposed to a light source, the solar cells on the s-skin generate energy. If a cell is shadowed by an approaching object, the intensity of the light, and therefore the energy generated, reduces, dropping to zero when the cell makes contact with the object, confirming touch. In proximity mode, the light intensity tells you how far the object is with respect to the cell. “In real time, you can then compare the light intensity…and after calibration find out the distances,” says Ravinder Dahiya of the Bendable Electronics and Sensing Technologies (BEST) Group, James Watt School of Engineering, University of Glasgow, where the study was carried out. The team used infra-red LEDs with the solar cells for proximity sensing for better results.

To demonstrate their concept, the researchers wrapped a generic 3D-printed robotic hand in their solar skin, which was then recorded interacting with its environment. The proof-of-concept tests showed an energy surplus of 383.3 mW from the palm of the robotic arm. “The eSkin could generate more than 100 W if present over the whole body area,” they reported in their paper.

“If you look at autonomous, battery-powered robots, putting an electronic skin [that] is consuming energy is a big problem because then it leads to reduced operational time,” says Dahiya. “On the other hand, if you have a skin which generates energy, then…it improves the operational time because you can continue to charge [during operation].” In essence, he says, they turned a challenge—how to power the large surface area of the skin—into an opportunity—by turning it into an energy-generating resource.

Dahiya envisages numerous applications for BEST’s innovative e-skin, given its material-integrated sensing capabilities, apart from the obvious use in robotics. For instance, in prosthetics: “[As] we are using [a] solar cell as a touch sensor itself…we are also [making it] less bulkier than other electronic skins.” This, he adds, will help create prosthetics that are of optimal weight and size, thus making it easier for prosthetics users. “If you look at electronic skin research, the the real action starts after it makes contact… Solar skin is a step ahead, because it will start to work when the object is approaching…[and] have more time to prepare for action.” This could effectively reduce the time lag that is often seen in brain–computer interfaces.

There are also possibilities in the automation sector, particularly in electrical and interactive vehicles. A car covered with solar e-skin, because of its proximity-sensing capabilities, would be able to “see” an approaching obstacle or a person. It isn’t “seeing” in the biological sense, Dahiya clarifies, but from the point of view of a machine. This can be integrated with other objects, not just cars, for a variety of uses. “Gestures can be recognized as well…[which] could be used for gesture-based control…in gaming or in other sectors.”

In the lab, tests were conducted with a single source of white light at 650 lux, but Dahiya feels there are interesting possibilities if they could work with multiple light sources that the e-skin could differentiate between. “We are exploring different AI techniques [for that],” he says, “processing the data in an innovative way [so] that we can identify the the directions of the light sources as well as the object.”

The BEST team’s achievement brings us closer to a flexible, self-powered, cost-effective electronic skin that can touch as well as “see.” At the moment, however, there are still some challenges. One of them is flexibility. In their prototype, they used commercial solar cells made of amorphous silicon, each 1cm x 1cm. “They are not flexible, but they are integrated on a flexible substrate,” Dahiya says. “We are currently exploring nanowire-based solar cells…[with which] we we hope to achieve good performance in terms of energy as well as sensing functionality.” Another shortcoming is what Dahiya calls “the integration challenge”—how to make the solar skin work with different materials. Continue reading →

Posted in Human Robots

#437884 Hyundai Buys Boston Dynamics for Nearly ...

Posted on December 11, 2020 by Android

This morning just after 3 a.m. ET, Boston Dynamics sent out a media release confirming that Hyundai Motor Group has acquired a controlling interest in the company that values Boston Dynamics at US $1.1 billion:

Under the agreement, Hyundai Motor Group will hold an approximately 80 percent stake in Boston Dynamics and SoftBank, through one of its affiliates, will retain an approximately 20 percent stake in Boston Dynamics after the closing of the transaction.

The release is very long, but does have some interesting bits—we’ll go through them, and talk about what this might mean for both Boston Dynamics and Hyundai.

We’ve asked Boston Dynamics for comment, but they’ve been unusually quiet for the last few days (I wonder why!). So at this point just keep in mind that the only things we know for sure are the ones in the release. If (when?) we hear anything from either Boston Dynamics or Hyundai, we’ll update this post.

The first thing to be clear on is that the acquisition is split between Hyundai Motor Group’s affiliates, including Hyundai Motor, Hyundai Mobis, and Hyundai Glovis. Hyundai Motor makes cars, Hyundai Mobis makes car parts and seems to be doing some autonomous stuff as well, and Hyundai Glovis does logistics. There are many other groups that share the Hyundai name, but they’re separate entities, at least on paper. For example, there’s a Hyundai Robotics, but that’s part of Hyundai Heavy Industries, a different company than Hyundai Motor Group. But for this article, when we say “Hyundai,” we’re talking about Hyundai Motor Group.

What’s in it for Hyundai?
Let’s get into the press release, which is filled with press release-y terms like “synergies” and “working together”—you can view the whole thing here—but still has some parts that convey useful info.

By establishing a leading presence in the field of robotics, the acquisition will mark another major step for Hyundai Motor Group toward its strategic transformation into a Smart Mobility Solution Provider. To propel this transformation, Hyundai Motor Group has invested substantially in development of future technologies, including in fields such as autonomous driving technology, connectivity, eco-friendly vehicles, smart factories, advanced materials, artificial intelligence (AI), and robots.

If Hyundai wants to be a “Smart Mobility Solution Provider” with a focus on vehicles, it really seems like there’s a whole bunch of other ways they could have spent most of a billion dollars that would get them there quicker. Will Boston Dynamics’ expertise help them develop autonomous driving technology? Sure, I guess, but why not just buy an autonomous car startup instead? Boston Dynamics is more about “robots,” which happens to be dead last on the list above.

There was some speculation a couple of weeks ago that Hyundai was going to try and leverage Boston Dynamics to make a real version of this hybrid wheeled/legged concept car, so if that’s what Hyundai means by “Smart Mobility Solution Provider,” then I suppose the Boston Dynamics acquisition makes more sense. Still, I think that’s unlikely, because it’s just a concept car, after all.

In addition to “smart mobility,” which seems like a longer-term goal for Hyundai, the company also mentions other, more immediate benefits from the acquisition:

Advanced robotics offer opportunities for rapid growth with the potential to positively impact society in multiple ways. Boston Dynamics is the established leader in developing agile, mobile robots that have been successfully integrated into various business operations. The deal is also expected to allow Hyundai Motor Group and Boston Dynamics to leverage each other’s respective strengths in manufacturing, logistics, construction and automation.

“Successfully integrated” might be a little optimistic here. They’re talking about Spot, of course, but I think the best you could say at this point is that Spot is in the middle of some promising pilot projects. Whether it’ll be successfully integrated in the sense that it’ll have long-term commercial usefulness and value remains to be seen. I’m optimistic about this as well, but Spot is definitely not there yet.

What does probably hold a lot of value for Hyundai is getting Spot, Pick, and perhaps even Handle into that “manufacturing, logistics, construction” stuff. This is the bread and butter for robots right now, and Boston Dynamics has plenty of valuable technology to offer in those spaces.

Photo: Bob O’Connor

Boston Dynamics is selling Spot for $74,500, shipping included.

Betting on Spot and Pick
With Boston Dynamics founder Marc Raibert’s transition to Chairman of the company, the CEO position is now occupied by Robert Playter, the long-time VP of engineering and more recently COO at Boston Dynamics. Here’s his statement from the release:

“Boston Dynamics’ commercial business has grown rapidly as we’ve brought to market the first robot that can automate repetitive and dangerous tasks in workplaces designed for human-level mobility. We and Hyundai share a view of the transformational power of mobility and look forward to working together to accelerate our plans to enable the world with cutting edge automation, and to continue to solve the world’s hardest robotics challenges for our customers.”

Whether Spot is in fact “the first robot that can automate repetitive and dangerous tasks in workplaces designed for human-level mobility” on the market is perhaps something that could be argued against, although I won’t. Whether or not it was the first robot that can do these kinds of things, it’s definitely not the only robot that do these kinds of things, and going forward, it’s going to be increasingly challenging for Spot to maintain its uniqueness.

For a long time, Boston Dynamics totally owned the quadruped space. Now, they’re one company among many—ANYbotics and Unitree are just two examples of other quadrupeds that are being successfully commercialized. Spot is certainly very capable and easy to use, and we shouldn’t underestimate the effort required to create a robot as complex as Spot that can be commercially used and supported. But it’s not clear how long they’ll maintain that advantage, with much more affordable platforms coming out of Asia, and other companies offering some unique new capabilities.

Photo: Boston Dynamics

Boston Dynamics’ Handle is an all-electric robot featuring a leg-wheel hybrid mobility system, a manipulator arm with a vacuum gripper, and a counterbalancing tail.

Boston Dynamics’ picking system, which stemmed from their 2019 acquisition of Kinema Systems, faces the same kinds of challenges—it’s very good, but it’s not totally unique.

Boston Dynamics produces highly capable mobile robots with advanced mobility, dexterity and intelligence, enabling automation in difficult, dangerous, or unstructured environments. The company launched sales of its first commercial robot, Spot in June of 2020 and has since sold hundreds of robots in a variety of industries, such as power utilities, construction, manufacturing, oil and gas, and mining. Boston Dynamics plans to expand the Spot product line early next year with an enterprise version of the robot with greater levels of autonomy and remote inspection capabilities, and the release of a robotic arm, which will be a breakthrough in mobile manipulation.

Boston Dynamics is also entering the logistics automation market with the industry leading Pick, a computer vision-based depalletizing solution, and will introduce a mobile robot for warehouses in 2021.

Huh. We’ll be trying to figure out what “greater levels of autonomy” means, as well as whether the “mobile robot for warehouses” is Handle, or something more like an autonomous mobile robot (AMR) platform. I’d honestly be surprised if Handle was ready for work outside of Boston Dynamics next year, and it’s hard to imagine how Boston Dynamics could leverage their expertise into the AMR space with something that wouldn’t just seem… Dull, compared to what they usually do. I hope to be surprised, though!

A new deep-pocketed benefactor

Hyundai Motor Group’s decision to acquire Boston Dynamics is based on its growth potential and wide range of capabilities.

“Wide range of capabilities” we get, but that other phrase, “growth potential,” has a heck of a lot wrapped up in it. At the moment, Boston Dynamics is nowhere near profitable, as far as we know. SoftBank acquired Boston Dynamics in 2017 for between one hundred and two hundred million, and over the last three years they’ve poured hundreds of millions more into Boston Dynamics.

Hyundai’s 80 percent stake just means that they’ll need to take over the majority of that support, and perhaps even increase it if Boston Dynamics’ growth is one of their primary goals. Hyundai can’t have a reasonable expectation that Boston Dynamics will be profitable any time soon; they’re selling Spots now, but it’s an open question whether Spot will manage to find a scalable niche in which it’ll be useful in the sort of volume that will make it a sustainable commercial success. And even if it does become a success, it seems unlikely that Spot by itself will make a significant dent in Boston Dynamics’ burn rate anytime soon. Boston Dynamics will have more products of course, but it’s going to take a while, and Hyundai will need to support them in the interim.

Depending on whether Hyundai views Boston Dynamics as a company that does research or a company that makes robots that are useful and profitable, it may be difficult for Boston Dynamics to justify the cost to develop the
next Atlas, when the
current one still seems so far from commercialization

It’s become clear that to sustain itself, Boston Dynamics needs a benefactor with very deep pockets and a long time horizon. Initially, Boston Dynamics’ business model (or whatever you want to call it) was to do bespoke projects for defense-ish folks like DARPA, but from what we understand Boston Dynamics stopped that sort of work after Google acquired them back in 2013. From one perspective, that government funding did exactly what it was supposed to do, which was to fund the development of legged robots through low TRLs (technology readiness levels) to the point where they could start to explore commercialization.

The question now, though, is whether Hyundai is willing to let Boston Dynamics undertake the kinds of low-TRL, high-risk projects that led from BigDog to LS3 to Spot, and from PETMAN to DRC Atlas to the current Atlas. So will Hyundai be cool about the whole thing and be the sort of benefactor that’s willing to give Boston Dynamics the resources that they need to keep doing what they’re doing, without having to answer too many awkward questions about things like practicality and profitability? Hyundai can certainly afford to do this, but so could SoftBank, and Google—the question is whether Hyundai will want to, over the length of time that’s required for the development of the kind of ultra-sophisticated robotics hardware that Boston Dynamics specializes in.

To put it another way: Depending whether Hyundai’s perspective on Boston Dynamics is as a company that does research or a company that makes robots that are useful and profitable, it may be difficult for Boston Dynamics to justify the cost to develop the next Atlas, when the current one still seems so far from commercialization.

Google, SoftBank, now Hyundai

Boston Dynamics possesses multiple key technologies for high-performance robots equipped with perception, navigation, and intelligence.

Hyundai Motor Group’s AI and Human Robot Interaction (HRI) expertise is highly synergistic with Boston Dynamics’s 3D vision, manipulation, and bipedal/quadruped expertise.

As it turns out, Hyundai Motors does have its own robotics lab, called Hyundai Motors Robotics Lab. Their website is not all that great, but here’s a video from last year:

I’m not entirely clear on what Hyundai means when they use the word “synergistic” when they talk about their robotics lab and Boston Dynamics, but it’s a little bit concerning. Usually, when a big company buys a little company that specializes in something that the big company is interested in, the idea is that the little company, to some extent, will be absorbed into the big company to give them some expertise in that area. Historically, however, Boston Dynamics has been highly resistant to this, maintaining its post-acquisition independence and appearing to be very reluctant to do anything besides what it wants to do, at whatever pace it wants to do it, and as by itself as possible.

From what we understand, Boston Dynamics didn’t integrate particularly well with Google’s robotics push in 2013, and we haven’t seen much evidence that SoftBank’s experience was much different. The most direct benefit to SoftBank (or at least the most visible one) was the addition of a fleet of Spot robots to the SoftBank Hawks baseball team cheerleading squad, along with a single (that we know about) choreographed gymnastics routine from an Atlas robot that was only shown on video.

And honestly, if you were a big manufacturing company with a bunch of money and you wanted to build up your own robotics program quickly, you’d probably have much better luck picking up some smaller robotics companies who were a bit less individualistic and would probably be more amenable to integration and would cost way less than a billion dollars-ish. And if integration is ultimately Hyundai’s goal, we’ll be very sad, because it’ll likely signal the end of Boston Dynamics doing the unfettered crazy stuff that we’ve grown to love.

Photo: Bob O’Connor

Possibly the most agile humanoid robot ever built, Atlas can run, climb, jump over obstacles, and even get up after a fall.

Boston Dynamics contemplates its future

The release ends by saying that the transaction is “subject to regulatory approvals and other customary closing conditions” and “is expected to close by June of 2021.” Again, you can read the whole thing here.

My initial reaction is that, despite the “synergies” described by Hyundai, it’s certainly not immediately obvious why the company wants to own 80 percent of Boston Dynamics. I’d also like a better understanding of how they arrived at the $1.1 billion valuation. I’m not saying this because I don’t believe in what Boston Dynamics is doing or in the inherent value of the company, because I absolutely do, albeit perhaps in a slightly less tangible sense. But when you start tossing around numbers like these, a big pile of expectations inevitably comes along with them. I hope that Boston Dynamics is unique enough that the kinds of rules that normally apply to robotics companies (or companies in general) can be set aside, at least somewhat, but I also worry that what made Boston Dynamics great was the explicit funding for the kinds of radical ideas that eventually resulted in robots like Atlas and Spot.

Can Hyundai continue giving Boston Dynamics the support and freedom that they need to keep doing the kinds of things that have made them legendary? I certainly hope so. Continue reading →

Posted in Human Robots

#437882 Video Friday: MIT Mini-Cheetah Robots ...

Posted on December 11, 2020 by Android

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

ICCR 2020 – December 26-29, 2020 – [Online Conference]
HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
Let us know if you have suggestions for next week, and enjoy today's videos.

What a lovely Christmas video from Norlab.

[ Norlab ]

Thanks Francois!

MIT Mini-Cheetahs are looking for a new home. Our new cheetah cubs, born at NAVER LABS, are for the MIT Mini-Cheetah workshop. MIT professor Sangbae Kim and his research team are supporting joint research by distributing Mini-Cheetahs to researchers all around the world.

[ NAVER Labs ]

For several years, NVIDIA’s research teams have been working to leverage GPU technology to accelerate reinforcement learning (RL). As a result of this promising research, NVIDIA is pleased to announce a preview release of Isaac Gym – NVIDIA’s physics simulation environment for reinforcement learning research. RL-based training is now more accessible as tasks that once required thousands of CPU cores can now instead be trained using a single GPU.

[ NVIDIA ]

At SINTEF in Norway, they're working on ways of using robots to keep tabs on giant floating cages of tasty fish:

One of the tricky things about operating robots in an environment like this is localization, so SINTEF is working on a solution that uses beacons:

While that video shows a lot of simulation (because otherwise there are tons of fish in the way), we're told that the autonomous navigation has been successfully demonstrated with an ROV in “a full scale fish farm with up to 200.000 salmon swimming around the robot.”

[ SINTEF ]

Thanks Eleni!

We’ve been getting ready for the snow in the most BG way possible. Wishing all of you a happy and healthy holiday season.

[ Berkshire Grey ]

ANYbotics doesn’t care what time of the year it is, so Happy Easter!

And here's a little bit about why ANYmal C looks the way it does.

[ ANYbotics ]

Robert “Buz” Chmielewski is using two modular prosthetic limbs developed by APL to feed himself dessert. Smart software puts his utensils in roughly the right spot, and then Buz uses his brain signals to cut the food with knife and fork. Once he is done cutting, the software then brings the food near his mouth, where he again uses brain signals to bring the food the last several inches to his mouth so that he can eat it.

[ JHUAPL ]

Introducing VESPER: a new military-grade small drone that is designed, sourced and built in the United States. Vesper offers a 50-minutes flight time, with speeds up to 45 mph (72 kph) and a total flight range of 25 miles (45 km). The magnetic snap-together architecture enables extremely fast transitions: the battery, props and rotor set can each be swapped in <5 seconds.

[ Vantage Robotics ]

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 Faboratory ]

Get a preview of the cave environments that are being used to inspire the Final Event competition course of the DARPA Subterranean Challenge. In the Final Event, teams will deploy their robots to rapidly map, navigate, and search in competition courses that combine elements of man-made tunnel systems, urban underground, and natural cave networks!

The reason to pay attention this particular video is that it gives us some idea of what DARPA means when they say "cave."

[ SubT ]

MQ25 takes another step toward unmanned aerial refueling for the U.S. Navy. The MQ-25 test asset has flown for the first time with an aerial refueling pod containing the hose and basket that will make it an aerial refueler.

[ Boeing ]

We present a unified model-based and data-driven approach for quadrupedal planning and control to achieve dynamic locomotion over uneven terrain. We utilize on-board proprioceptive and exteroceptive feedback to map sensory information and desired base velocity commands into footstep plans using a reinforcement learning (RL) policy trained in simulation over a wide range of procedurally generated terrains.

[ DRS ]

The video shows the results of the German research project RoPHa. Within the project, the partners developed technologies for two application scenarios with the service robot Care-O-bot 4 in order to support people in need of help when eating.

[ RoPHa Project ]

Thanks Jenny!

This looks like it would be fun, if you are a crazy person.

[ Team BlackSheep ]

Robot accuracy is the limiting factor in many industrial applications. Manufacturers often only specify the pose repeatability values of their robotic systems. Fraunhofer IPA has set up a testing environment for automated measuring of accuracy performance criteria of industrial robots. Following the procedures defined in norm ISO 9283 allows generating reliable and repeatable results. They can be the basis for targeted measures increasing the robotic system’s accuracy.

[ Fraunhofer ]

Thanks Jenny!

The IEEE Women in Engineering – Robotics and Automation Society (WIE-RAS) hosted an online panel on best practices for teaching robotics. The diverse panel boasts experts in robotics education from a variety of disciplines, institutions, and areas of expertise.

[ IEEE RAS ]

Northwestern researchers have developed a first-of-its-kind soft, aquatic robot that is powered by light and rotating magnetic fields. These life-like robotic materials could someday be used as "smart" microscopic systems for production of fuels and drugs, environmental cleanup or transformative medical procedures.

[ Northwestern ]

Tech United Eindhoven's soccer robots now have eight wheels instead of four wheels, making them tweleve times better, if my math is right.

[ TU Eindhoven ] Continue reading →

Posted in Human Robots

#437872 AlphaFold Proves That AI Can Crack ...

Posted on December 7, 2020 by Android

Any successful implementation of artificial intelligence hinges on asking the right questions in the right way. That’s what the British AI company DeepMind (a subsidiary of Alphabet) accomplished when it used its neural network to tackle one of biology’s grand challenges, the protein-folding problem. Its neural net, known as AlphaFold, was able to predict the 3D structures of proteins based on their amino acid sequences with unprecedented accuracy.

AlphaFold’s predictions at the 14th Critical Assessment of protein Structure Prediction (CASP14) were accurate to within an atom’s width for most of the proteins. The competition consisted of blindly predicting the structure of proteins that have only recently been experimentally determined—with some still awaiting determination.

Called the building blocks of life, proteins consist of 20 different amino acids in various combinations and sequences. A protein's biological function is tied to its 3D structure. Therefore, knowledge of the final folded shape is essential to understanding how a specific protein works—such as how they interact with other biomolecules, how they may be controlled or modified, and so on. “Being able to predict structure from sequence is the first real step towards protein design,” says Janet M. Thornton, director emeritus of the European Bioinformatics Institute. It also has enormous benefits in understanding disease-causing pathogens. For instance, at the moment only about 18 of the 26 proteins in the SARS-CoV-2 virus are known.

Predicting a protein’s 3D structure is a computational nightmare. In 1969 Cyrus Levinthal estimated that there are 10300 possible conformational combinations for a single protein, which would take longer than the age of the known universe to evaluate by brute force calculation. AlphaFold can do it in a few days.

As scientific breakthroughs go, AlphaFold’s discovery is right up there with the likes of James Watson and Francis Crick’s DNA double-helix model, or, more recently, Jennifer Doudna and Emmanuelle Charpentier’s CRISPR-Cas9 genome editing technique.

How did a team that just a few years ago was teaching an AI to master a 3,000-year-old game end up training one to answer a question plaguing biologists for five decades? That, says Briana Brownell, data scientist and founder of the AI company PureStrategy, is the beauty of artificial intelligence: The same kind of algorithm can be used for very different things.

“Whenever you have a problem that you want to solve with AI,” she says, “you need to figure out how to get the right data into the model—and then the right sort of output that you can translate back into the real world.”

DeepMind’s success, she says, wasn’t so much a function of picking the right neural nets but rather “how they set up the problem in a sophisticated enough way that the neural network-based modeling [could] actually answer the question.”

AlphaFold showed promise in 2018, when DeepMind introduced a previous iteration of their AI at CASP13, achieving the highest accuracy among all participants. The team had trained its to model target shapes from scratch, without using previously solved proteins as templates.

For 2020 they deployed new deep learning architectures into the AI, using an attention-based model that was trained end-to-end. Attention in a deep learning network refers to a component that manages and quantifies the interdependence between the input and output elements, as well as between the input elements themselves.

The system was trained on public datasets of the approximately 170,000 known experimental protein structures in addition to databases with protein sequences of unknown structures.

“If you look at the difference between their entry two years ago and this one, the structure of the AI system was different,” says Brownell. “This time, they’ve figured out how to translate the real world into data … [and] created an output that could be translated back into the real world.”

Like any AI system, AlphaFold may need to contend with biases in the training data. For instance, Brownell says, AlphaFold is using available information about protein structure that has been measured in other ways. However, there are also many proteins with as yet unknown 3D structures. Therefore, she says, a bias could conceivably creep in toward those kinds of proteins that we have more structural data for.

Thornton says it’s difficult to predict how long it will take for AlphaFold’s breakthrough to translate into real-world applications.

“We only have experimental structures for about 10 per cent of the 20,000 proteins [in] the human body,” she says. “A powerful AI model could unveil the structures of the other 90 per cent.”

Apart from increasing our understanding of human biology and health, she adds, “it is the first real step toward… building proteins that fulfill a specific function. From protein therapeutics to biofuels or enzymes that eat plastic, the possibilities are endless.” Continue reading →

Posted in Human Robots

#437869 Video Friday: Japan’s Gundam Robot ...

Posted on December 5, 2020 by Android

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

ACRA 2020 – December 8-10, 2020 – [Online]
Let us know if you have suggestions for next week, and enjoy today’s videos.

Another BIG step for Japan’s Gundam project.

[ Gundam Factory ]

We present an interactive design system that allows users to create sculpting styles and fabricate clay models using a standard 6-axis robot arm. Given a general mesh as input, the user iteratively selects sub-areas of the mesh through decomposition and embeds the design expression into an initial set of toolpaths by modifying key parameters that affect the visual appearance of the sculpted surface finish. We demonstrate the versatility of our approach by designing and fabricating different sculpting styles over a wide range of clay models.

[ Disney Research ]

China’s Chang’e-5 completed the drilling, sampling and sealing of lunar soil at 04:53 BJT on Wednesday, marking the first automatic sampling on the Moon, the China National Space Administration (CNSA) announced Wednesday.

[ CCTV ]

Red Hat’s been putting together an excellent documentary on Willow Garage and ROS, and all five parts have just been released. We posted Part 1 a little while ago, so here’s Part 2 and Part 3.

Parts 4 and 5 are at the link below!

[ Red Hat ]

Congratulations to ANYbotics on a well-deserved raise!

ANYbotics has origins in the Robotic Systems Lab at ETH Zurich, and ANYmal’s heritage can be traced back at least as far as StarlETH, which we first met at ICRA 2013.

[ ANYbotics ]

Most conventional robots are working with 0.05-0.1mm accuracy. Such accuracy requires high-end components like low-backlash gears, high-resolution encoders, complicated CNC parts, powerful motor drives, etc. Those in combination end up an expensive solution, which is either unaffordable or unnecessary for many applications. As a result, we found the Apicoo Robotics to provide our customers solutions with a much lower cost and higher stability.

[ Apicoo Robotics ]

The Skydio 2 is an incredible drone that can take incredible footage fully autonomously, but it definitely helps if you do incredible things in incredible places.

[ Skydio ]

Jueying is the first domestic sensitive quadruped robot for industry applications and scenarios. It can coordinate (replace) humans to reach any place that can be reached. It has superior environmental adaptability, excellent dynamic balance capabilities and precise Environmental perception capabilities. By carrying functional modules for different application scenarios in the safe load area, the mobile superiority of the quadruped robot can be organically integrated with the commercialization of functional modules, providing smart factories, smart parks, scene display and public safety application solutions.

[ DeepRobotics ]

We have developed semi-autonomous quadruped robot, called LASER-D (Legged-Agile-Smart-Efficient Robot for Disinfection) for performing disinfection in cluttered environments. The robot is equipped with a spray-based disinfection system and leverages the body motion to controlling the spray action without the need for an extra stabilization mechanism. The system includes an image processing capability to verify disinfected regions with high accuracy. This system allows the robot to successfully carry out effective disinfection tasks while safely traversing through cluttered environments, climb stairs/slopes, and navigate on slippery surfaces.

[ USC Viterbi ]

We propose the “multi-vision hand”, in which a number of small high-speed cameras are mounted on the robot hand of a common 7 degrees-of-freedom robot. Also, we propose visual-servoing control by using a multi-vision system that combines the multi-vision hand and external fixed high-speed cameras. The target task was ball catching motion, which requires high-speed operation. In the proposed catching control, the catch position of the ball, which is estimated by the external fixed high-speed cameras, is corrected by the multi-vision hand in real-time.

More details available through IROS on-demand.

[ Namiki Laboratory ]

Shunichi Kurumaya wrote in to share his work on PneuFinger, a pneumatically actuated compliant robotic gripping system.

[ Nakamura Lab ]

Thanks Shunichi!

Motivated by insights into the human teaching process, we introduce a method for incorporating unstructured natural language into imitation learning. At training time, the expert can provide demonstrations along with verbal descriptions in order to describe the underlying intent, e.g., “Go to the large green bowl’’. The training process, then, interrelates the different modalities to encode the correlations between language, perception, and motion. The resulting language-conditioned visuomotor policies can be conditioned at run time on new human commands and instructions, which allows for more fine-grained control over the trained policies while also reducing situational ambiguity.

[ ASU ]

Thanks Heni!

Gita is on sale for the holidays for only $2,000.

[ Gita ]

This video introduces a computational approach for routing thin artificial muscle actuators through hyperelastic soft robots, in order to achieve a desired deformation behavior. Provided with a robot design, and a set of example deformations, we continuously co-optimize the routing of actuators, and their actuation, to approximate example deformations as closely as possible.

[ Disney Research ]

Researchers and mountain rescuers in Switzerland are making huge progress in the field of autonomous drones as the technology becomes more in-demand for global search-and-rescue operations.

[ SWI ]

This short clip of the Ghost Robotics V60 features an interesting, if awkward looking, righting behavior at the end.

[ Ghost Robotics ]

Europe’s Rosalind Franklin ExoMars rover has a younger ’sibling’, ExoMy. The blueprints and software for this mini-version of the full-size Mars explorer are available for free so that anyone can 3D print, assemble and program their own ExoMy.

[ ESA ]

The holiday season is here, and with the added impact of Covid-19 consumer demand is at an all-time high. Berkshire Grey is the partner that today’s leading organizations turn to when it comes to fulfillment automation.

[ Berkshire Grey ]

Until very recently, the vast majority of studies and reports on the use of cargo drones for public health were almost exclusively focused on the technology. The driving interest from was on the range that these drones could travel, how much they could carry and how they worked. Little to no attention was placed on the human side of these projects. Community perception, community engagement, consent and stakeholder feedback were rarely if ever addressed. This webinar presents the findings from a very recent study that finally sheds some light on the human side of drone delivery projects.

[ WeRobotics ] Continue reading →

Posted in Human Robots