Tag Archives: women

#438785 Video Friday: A Blimp For Your Cat

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!):

HRI 2021 – March 8-11, 2021 – [Online Conference]
RoboSoft 2021 – April 12-16, 2021 – [Online Conference]
ICRA 2021 – May 30-5, 2021 – Xi'an, China
Let us know if you have suggestions for next week, and enjoy today's videos.

Shiny robotic cat toy blimp!

I am pretty sure this is Google Translate getting things wrong, but the About page mentions that the blimp will “take you to your destination after appearing in the death of God.”

[ NTT DoCoMo ] via [ RobotStart ]

If you have yet to see this real-time video of Perseverance landing on Mars, drop everything and watch it.

During the press conference, someone commented that this is the first time anyone on the team who designed and built this system has ever seen it in operation, since it could only be tested at the component scale on Earth. This landing system has blown my mind since Curiosity.

Here's a better look at where Percy ended up:

[ NASA ]

The fact that Digit can just walk up and down wet, slippery, muddy hills without breaking a sweat is (still) astonishing.

[ Agility Robotics ]

SkyMul wants drones to take over the task of tying rebar, which looks like just the sort of thing we'd rather robots be doing so that we don't have to:

The tech certainly looks promising, and SkyMul says that they're looking for some additional support to bring things to the pilot stage.

[ SkyMul ]

Thanks Eohan!

Flatcat is a pet-like, playful robot that reacts to touch. Flatcat feels everything exactly: Cuddle with it, romp around with it, or just watch it do weird things of its own accord. We are sure that flatcat will amaze you, like us, and caress your soul.

I don't totally understand it, but I want it anyway.

[ Flatcat ]

Thanks Oswald!

This is how I would have a romantic dinner date if I couldn't get together in person. Herman the UR3 and an OptiTrack system let me remotely make a romantic meal!

[ Dave's Armoury ]

Here, we propose a novel design of deformable propellers inspired by dragonfly wings. The structure of these propellers includes a flexible segment similar to the nodus on a dragonfly wing. This flexible segment can bend, twist and even fold upon collision, absorbing force upon impact and protecting the propeller from damage.

[ Paper ]

Thanks Van!

In the 1970s, The CIA​ created the world's first miniaturized unmanned aerial vehicle, or UAV, which was intended to be a clandestine listening device. The Insectothopter was never deployed operationally, but was still revolutionary for its time.

It may never have been deployed (not that they'll admit to, anyway), but it was definitely operational and could fly controllably.

[ CIA ]

Research labs are starting to get Digits, which means we're going to get a much better idea of what its limitations are.

[ Ohio State ]

This video shows the latest achievements for LOLA walking on undetected uneven terrain. The robot is technically blind, not using any camera-based or prior information on the terrain.

[ TUM ]

We define “robotic contact juggling” to be the purposeful control of the motion of a three-dimensional smooth object as it rolls freely on a motion-controlled robot manipulator, or “hand.” While specific examples of robotic contact juggling have been studied before, in this paper we provide the first general formulation and solution method for the case of an arbitrary smooth object in single-point rolling contact on an arbitrary smooth hand.

[ Paper ]

Thanks Fan!

A couple of new cobots from ABB, designed to work safely around humans.

[ ABB ]

Thanks Fan!

It's worth watching at least a little bit of Adam Savage testing Spot's new arm, because we get to see Spot try, fail, and eventually succeed at an autonomous door-opening behavior at the 10 minute mark.

[ Tested ]

SVR discusses diversity with guest speakers Dr. Michelle Johnson from the GRASP Lab at UPenn; Dr Ariel Anders from Women in Robotics and first technical hire at Robust.ai; Alka Roy from The Responsible Innovation Project; and Kenechukwu C. Mbanesi and Kenya Andrews from Black in Robotics. The discussion here is moderated by Dr. Ken Goldberg—artist, roboticist and Director of the CITRIS People and Robots Lab—and Andra Keay from Silicon Valley Robotics.

[ SVR ]

RAS presents a Soft Robotics Debate on Bioinspired vs. Biohybrid Design.

In this debate, we will bring together experts in Bioinspiration and Biohybrid design to discuss the necessary steps to make more competent soft robots. We will try to answer whether bioinspired research should focus more on developing new bioinspired material and structures or on the integration of living and artificial structures in biohybrid designs.

[ RAS SoRo ]

IFRR presents a Colloquium on Human Robot Interaction.

Across many application domains, robots are expected to work in human environments, side by side with people. The users will vary substantially in background, training, physical and cognitive abilities, and readiness to adopt technology. Robotic products are expected to not only be intuitive, easy to use, and responsive to the needs and states of their users, but they must also be designed with these differences in mind, making human-robot interaction (HRI) a key area of research.

[ IFRR ]

Vijay Kumar, Nemirovsky Family Dean and Professor at Penn Engineering, gives an introduction to ENIAC day and David Patterson, Pardee Professor of Computer Science, Emeritus at the University of California at Berkeley, speaks about the legacy of the ENIAC and its impact on computer architecture today. This video is comprised of lectures one and two of nine total lectures in the ENIAC Day series.

There are more interesting ENIAC videos at the link below, but we'll highlight this particular one, about the women of the ENIAC, also known as the First Programmers.

[ ENIAC Day ] Continue reading

Posted in Human Robots

#438001 How an Israeli Startup Is Using AI to ...

The first baby conceived using in-vitro fertilization (IVF) was born in the UK in 1978. Over 40 years later, the technique has become commonplace, but its success rate is still fairly low at around 22 to 30 percent. A female-founded Israeli startup called Embryonics is setting out to change this by using artificial intelligence to screen embryos.

IVF consists of fertilizing a woman’s egg with her partner’s or a donor’s sperm outside of her body, creating an embryo that’s then implanted in the uterus. It’s not an easy process in any sense of the word—physically, emotionally, or financially. Insurance rarely covers IVF, and the costs run anywhere from $12,000 to $25,000 per cycle (a cycle takes about a month and includes stimulating a woman’s ovaries to produce eggs, extracting the eggs, inseminating them outside the body, and implanting an embryo).

Women have to give themselves daily hormone shots to stimulate egg production, and these can cause uncomfortable side effects. After so much stress and expense, it’s disheartening to think that the odds of a successful pregnancy are, at best, one in three.

A crucial factor in whether or not an IVF cycle works—that is, whether the embryo implants in the uterus and begins to develop into a healthy fetus—is the quality of the embryo. Doctors examine embryos through a microscope to determine how many cells they contain and whether they appear healthy, and choose the one that looks most viable.

But the human eye can only see so much, even with the help of a microscope; despite embryologists’ efforts to select the “best” embryo, success rates are still relatively low. “Many decisions are based on gut feeling or personal experience,” said Embryonics founder and CEO Yael Gold-Zamir. “Even if you go to the same IVF center, two experts can give you different opinions on the same embryo.”

This is where Embryonics’ technology comes in. They used 8,789 time-lapse videos of developing embryos to train an algorithm that predicts the likelihood of successful embryo implantation. A little less than half of the embryos from the dataset were graded by embryologists, and implantation data was integrated when it was available (as a binary “successful” or “failed” metric).

The algorithm uses geometric deep learning, a technique that takes a traditional convolutional neural network—which filters input data to create maps of its features, and is most commonly used for image recognition—and applies it to more complex data like 3D objects and graphs. Within days after fertilization, the embryo is still at the blastocyst stage, essentially a microscopic clump of just 200-300 cells; the algorithm uses this deep learning technique to spot and identify patterns in embryo development that human embryologists either wouldn’t see at all, or would require massive collation of data to validate.

On top of the embryo videos, Embryonics’ team incorporated patient data and environmental data from the lab into its algorithm, with encouraging results: the company reports that using its algorithm resulted in a 12 percent increase in positive predictive value (identifying embryos that would lead to implantation and healthy pregnancy) and a 29 percent increase in negative predictive value (identifying embyros that would not result in successful pregnancy) when compared to an external panel of embryologists.

TechCrunch reported last week that in a pilot of 11 women who used Embryonics’ algorithm to select their embryos, 6 are enjoying successful pregnancies, while 5 are still awaiting results.

Embryonics wasn’t the first group to think of using AI to screen embryos; a similar algorithm developed in 2019 by researchers at Weill Cornell Medicine was able to classify the quality of a set of embryo images with 97 percent accuracy. But Embryonics will be one of the first to bring this sort of technology to market. The company is waiting to receive approval from European regulatory bodies to be able to sell the software to fertility clinics in Europe.

Its timing is ripe: as more and more women delay having kids due to lifestyle and career-related factors, demand for IVF is growing, and will likely accelerate in coming years.

The company ultimately hopes to bring its product to the US, as well as to expand its work to include using data to improve hormonal stimulation.

Image Credit: Gerd Altmann from Pixabay Continue reading

Posted in Human Robots

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

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

#437809 Q&A: The Masterminds Behind ...

Illustration: iStockphoto

Getting a car to drive itself is undoubtedly the most ambitious commercial application of artificial intelligence (AI). The research project was kicked into life by the 2004 DARPA Urban Challenge and then taken up as a business proposition, first by Alphabet, and later by the big automakers.

The industry-wide effort vacuumed up many of the world’s best roboticists and set rival companies on a multibillion-dollar acquisitions spree. It also launched a cycle of hype that paraded ever more ambitious deadlines—the most famous of which, made by Alphabet’s Sergei Brin in 2012, was that full self-driving technology would be ready by 2017. Those deadlines have all been missed.

Much of the exhilaration was inspired by the seeming miracles that a new kind of AI—deep learning—was achieving in playing games, recognizing faces, and transliterating voices. Deep learning excels at tasks involving pattern recognition—a particular challenge for older, rule-based AI techniques. However, it now seems that deep learning will not soon master the other intellectual challenges of driving, such as anticipating what human beings might do.

Among the roboticists who have been involved from the start are Gill Pratt, the chief executive officer of Toyota Research Institute (TRI) , formerly a program manager at the Defense Advanced Research Projects Agency (DARPA); and Wolfram Burgard, vice president of automated driving technology for TRI and president of the IEEE Robotics and Automation Society. The duo spoke with IEEE Spectrum’s Philip Ross at TRI’s offices in Palo Alto, Calif.

This interview has been condensed and edited for clarity.

IEEE Spectrum: How does AI handle the various parts of the self-driving problem?

Photo: Toyota

Gill Pratt

Gill Pratt: There are three different systems that you need in a self-driving car: It starts with perception, then goes to prediction, and then goes to planning.

The one that by far is the most problematic is prediction. It’s not prediction of other automated cars, because if all cars were automated, this problem would be much more simple. How do you predict what a human being is going to do? That’s difficult for deep learning to learn right now.

Spectrum: Can you offset the weakness in prediction with stupendous perception?

Photo: Toyota Research Institute for Burgard

Wolfram Burgard

Wolfram Burgard: Yes, that is what car companies basically do. A camera provides semantics, lidar provides distance, radar provides velocities. But all this comes with problems, because sometimes you look at the world from different positions—that’s called parallax. Sometimes you don’t know which range estimate that pixel belongs to. That might make the decision complicated as to whether that is a person painted onto the side of a truck or whether this is an actual person.

With deep learning there is this promise that if you throw enough data at these networks, it’s going to work—finally. But it turns out that the amount of data that you need for self-driving cars is far larger than we expected.

Spectrum: When do deep learning’s limitations become apparent?

Pratt: The way to think about deep learning is that it’s really high-performance pattern matching. You have input and output as training pairs; you say this image should lead to that result; and you just do that again and again, for hundreds of thousands, millions of times.

Here’s the logical fallacy that I think most people have fallen prey to with deep learning. A lot of what we do with our brains can be thought of as pattern matching: “Oh, I see this stop sign, so I should stop.” But it doesn’t mean all of intelligence can be done through pattern matching.

“I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur?”
—Gill Pratt, Toyota Research Institute

For instance, when I’m driving and I see a mother holding the hand of a child on a corner and trying to cross the street, I am pretty sure she’s not going to cross at a red light and jaywalk. I know from my experience being a human being that mothers and children don’t act that way. On the other hand, say there are two teenagers—with blue hair, skateboards, and a disaffected look. Are they going to jaywalk? I look at that, you look at that, and instantly the probability in your mind that they’ll jaywalk is much higher than for the mother holding the hand of the child. It’s not that you’ve seen 100,000 cases of young kids—it’s that you understand what it is to be either a teenager or a mother holding a child’s hand.

You can try to fake that kind of intelligence. If you specifically train a neural network on data like that, you could pattern-match that. But you’d have to know to do it.

Spectrum: So you’re saying that when you substitute pattern recognition for reasoning, the marginal return on the investment falls off pretty fast?

Pratt: That’s absolutely right. Unfortunately, we don’t have the ability to make an AI that thinks yet, so we don’t know what to do. We keep trying to use the deep-learning hammer to hammer more nails—we say, well, let’s just pour more data in, and more data.

Spectrum: Couldn’t you train the deep-learning system to recognize teenagers and to assign the category a high propensity for jaywalking?

Burgard: People have been doing that. But it turns out that these heuristics you come up with are extremely hard to tweak. Also, sometimes the heuristics are contradictory, which makes it extremely hard to design these expert systems based on rules. This is where the strength of the deep-learning methods lies, because somehow they encode a way to see a pattern where, for example, here’s a feature and over there is another feature; it’s about the sheer number of parameters you have available.

Our separation of the components of a self-driving AI eases the development and even the learning of the AI systems. Some companies even think about using deep learning to do the job fully, from end to end, not having any structure at all—basically, directly mapping perceptions to actions.

Pratt: There are companies that have tried it; Nvidia certainly tried it. In general, it’s been found not to work very well. So people divide the problem into blocks, where we understand what each block does, and we try to make each block work well. Some of the blocks end up more like the expert system we talked about, where we actually code things, and other blocks end up more like machine learning.

Spectrum: So, what’s next—what new technique is in the offing?

Pratt: If I knew the answer, we’d do it. [Laughter]

Spectrum: You said that if all cars on the road were automated, the problem would be easy. Why not “geofence” the heck out of the self-driving problem, and have areas where only self-driving cars are allowed?

Pratt: That means putting in constraints on the operational design domain. This includes the geography—where the car should be automated; it includes the weather, it includes the level of traffic, it includes speed. If the car is going slow enough to avoid colliding without risking a rear-end collision, that makes the problem much easier. Street trolleys operate with traffic still in some parts of the world, and that seems to work out just fine. People learn that this vehicle may stop at unexpected times. My suspicion is, that is where we’ll see Level 4 autonomy in cities. It’s going to be in the lower speeds.

“We are now in the age of deep learning, and we don’t know what will come after.”
—Wolfram Burgard, Toyota Research Institute

That’s a sweet spot in the operational design domain, without a doubt. There’s another one at high speed on a highway, because access to highways is so limited. But unfortunately there is still the occasional debris that suddenly crosses the road, and the weather gets bad. The classic example is when somebody irresponsibly ties a mattress to the top of a car and it falls off; what are you going to do? And the answer is that terrible things happen—even for humans.

Spectrum: Learning by doing worked for the first cars, the first planes, the first steam boilers, and even the first nuclear reactors. We ran risks then; why not now?

Pratt: It has to do with the times. During the era where cars took off, all kinds of accidents happened, women died in childbirth, all sorts of diseases ran rampant; the expected characteristic of life was that bad things happened. Expectations have changed. Now the chance of dying in some freak accident is quite low because of all the learning that’s gone on, the OSHA [Occupational Safety and Health Administration] rules, UL code for electrical appliances, all the building standards, medicine.

Furthermore—and we think this is very important—we believe that empathy for a human being at the wheel is a significant factor in public acceptance when there is a crash. We don’t know this for sure—it’s a speculation on our part. I’ve driven, I’ve had close calls; that could have been me that made that mistake and had that wreck. I think people are more tolerant when somebody else makes mistakes, and there’s an awful crash. In the case of an automated car, we worry that that empathy won’t be there.

Photo: Toyota

Toyota is using this
Platform 4 automated driving test vehicle, based on the Lexus LS, to develop Level-4 self-driving capabilities for its “Chauffeur” project.

Spectrum: Toyota is building a system called Guardian to back up the driver, and a more futuristic system called Chauffeur, to replace the driver. How can Chauffeur ever succeed? It has to be better than a human plus Guardian!

Pratt: In the discussions we’ve had with others in this field, we’ve talked about that a lot. What is the standard? Is it a person in a basic car? Or is it a person with a car that has active safety systems in it? And what will people think is good enough?

These systems will never be perfect—there will always be some accidents, and no matter how hard we try there will still be occasions where there will be some fatalities. At what threshold are people willing to say that’s okay?

Spectrum: You were among the first top researchers to warn against hyping self-driving technology. What did you see that so many other players did not?

Pratt: First, in my own case, during my time at DARPA I worked on robotics, not cars. So I was somewhat of an outsider. I was looking at it from a fresh perspective, and that helps a lot.

Second, [when I joined Toyota in 2015] I was joining a company that is very careful—even though we have made some giant leaps—with the Prius hybrid drive system as an example. Even so, in general, the philosophy at Toyota is kaizen—making the cars incrementally better every single day. That care meant that I was tasked with thinking very deeply about this thing before making prognostications.

And the final part: It was a new job for me. The first night after I signed the contract I felt this incredible responsibility. I couldn’t sleep that whole night, so I started to multiply out the numbers, all using a factor of 10. How many cars do we have on the road? Cars on average last 10 years, though ours last 20, but let’s call it 10. They travel on an order of 10,000 miles per year. Multiply all that out and you get 10 to the 10th miles per year for our fleet on Planet Earth, a really big number. I asked myself, if all of those cars had automated drive, how good would they have to be to tolerate the number of crashes that would still occur? And the answer was so incredibly good that I knew it would take a long time. That was five years ago.

Burgard: We are now in the age of deep learning, and we don’t know what will come after. We are still making progress with existing techniques, and they look very promising. But the gradient is not as steep as it was a few years ago.

Pratt: There isn’t anything that’s telling us that it can’t be done; I should be very clear on that. Just because we don’t know how to do it doesn’t mean it can’t be done. Continue reading

Posted in Human Robots

#437758 Remotely Operated Robot Takes Straight ...

Roboticists love hard problems. Challenges like the DRC and SubT have helped (and are still helping) to catalyze major advances in robotics, but not all hard problems require a massive amount of DARPA funding—sometimes, a hard problem can just be something very specific that’s really hard for a robot to do, especially relative to the ease with which a moderately trained human might be able to do it. Catching a ball. Putting a peg in a hole. Or using a straight razor to shave someone’s face without Sweeney Todd-izing them.

This particular roboticist who sees straight-razor face shaving as a hard problem that robots should be solving is John Peter Whitney, who we first met back at IROS 2014 in Chicago when (working at Disney Research) he introduced an elegant fluidic actuator system. These actuators use tubes containing a fluid (like air or water) to transmit forces from a primary robot to a secondary robot in a very efficient way that also allows for either compliance or very high fidelity force feedback, depending on the compressibility of the fluid.

Photo: John Peter Whitney/Northeastern University

Barber meets robot: Boston based barber Jesse Cabbage [top, right] observes the machine created by roboticist John Peter Whitney. Before testing the robot on Whitney’s face, they used his arm for a quick practice [bottom].

Whitney is now at Northeastern University, in Boston, and he recently gave a talk at the RSS workshop on “Reacting to Contact,” where he suggested that straight razor shaving would be an interesting and valuable problem for robotics to work toward, due to its difficulty and requirement for an extremely high level of both performance and reliability.

Now, a straight razor is sort of like a safety razor, except with the safety part removed, which in fact does make it significantly less safe for humans, much less robots. Also not ideal for those worried about safety is that as part of the process the razor ends up in distressingly close proximity to things like the artery that is busily delivering your brain’s entire supply of blood, which is very close to the top of the list of things that most people want to keep blades very far away from. But that didn’t stop Whitney from putting his whiskers where his mouth is and letting his robotic system mediate the ministrations of a professional barber. It’s not an autonomous robotic straight-razor shave (because Whitney is not totally crazy), but it’s a step in that direction, and requires that the hardware Whitney developed be dead reliable.

Perhaps that was a poor choice of words. But, rest assured that Whitney lived long enough to answer our questions after. Here’s the video; it’s part of a longer talk, but it should start in the right spot, at about 23:30.

If Whitney looked a little bit nervous to you, that’s because he was. “This was the first time I’d ever been shaved by someone (something?!) else with a straight razor,” he told us, and while having a professional barber at the helm was some comfort, “the lack of feeling and control on my part was somewhat unsettling.” Whitney says that the barber, Jesse Cabbage of Dentes Barbershop in Somerville, Mass., was surprised by how well he could feel the tactile sensations being transmitted from the razor. “That’s one of the reasons we decided to make this video,” Whitney says. “I can’t show someone how something feels, so the next best thing is to show a delicate task that either from experience or intuition makes it clear to the viewer that the system must have these properties—otherwise the task wouldn’t be possible.”

And as for when Whitney might be comfortable getting shaved by a robotic system without a human in the loop? It’s going to take a lot of work, as do most other hard problems in robotics. “There are two parts to this,” he explains. “One is fault-tolerance of the components themselves (software, electronics, etc.) and the second is the quality of the perception and planning algorithms.”

He offers a comparison to self-driving cars, in which similar (or greater) risks are incurred: “To learn how to perceive, interpret, and adapt, we need a very high-fidelity model of the problem, or a wealth of data and experience, or both” he says. “But in the case of shaving we are greatly lacking in both!” He continues with the analogy: “I think there is a natural progression—the community started with autonomous driving of toy cars on closed courses and worked up to real cars carrying human passengers; in robotic manipulation we are beginning to move out of the ‘toy car’ stage and so I think it’s good to target high-consequence hard problems to help drive progress.”

The ultimate goal is much more general than the creation of a dedicated straight razor shaving robot. This particular hardware system is actually a testbed for exploring MRI-compatible remote needle biopsy.

Of course, the ultimate goal here is much more general than the creation of a dedicated straight razor shaving robot; it’s a challenge that includes a host of sub-goals that will benefit robotics more generally. This particular hardware system Whitney is developing is actually a testbed for exploring MRI-compatible remote needle biopsy, and he and his students are collaborating with Brigham and Women’s Hospital in Boston on adapting this technology to prostate biopsy and ablation procedures. They’re also exploring how delicate touch can be used as a way to map an environment and localize within it, especially where using vision may not be a good option. “These traits and behaviors are especially interesting for applications where we must interact with delicate and uncertain environments,” says Whitney. “Medical robots, assistive and rehabilitation robots and exoskeletons, and shared-autonomy teleoperation for delicate tasks.”
A paper with more details on this robotic system, “Series Elastic Force Control for Soft Robotic Fluid Actuators,” is available on arXiv. Continue reading

Posted in Human Robots