Tag Archives: robots

#436261 AI and the future of work: The prospects ...

AI experts gathered at MIT last week, with the aim of predicting the role artificial intelligence will play in the future of work. Will it be the enemy of the human worker? Will it prove to be a savior? Or will it be just another innovation—like electricity or the internet?

As IEEE Spectrum previously reported, this conference (“AI and the Future of Work Congress”), held at MIT’s Kresge Auditorium, offered sometimes pessimistic outlooks on the job- and industry-destroying path that AI and automation seems to be taking: Self-driving technology will put truck drivers out of work; smart law clerk algorithms will put paralegals out of work; robots will (continue to) put factory and warehouse workers out of work.

Andrew McAfee, co-director of MIT’s Initiative on the Digital Economy, said even just in the past couple years, he’s noticed a shift in the public’s perception of AI. “I remember from previous versions of this conference, it felt like we had to make the case that we’re living in a period of accelerating change and that AI’s going to have a big impact,” he said. “Nobody had to make that case today.”

Elisabeth Reynolds, executive director of MIT’s Task Force on the Work of the Future, noted that following the path of least resistance is not a viable way forward. “If we do nothing, we’re in trouble,” she said. “The future will not take care of itself. We have to do something about it.”

Panelists and speakers spoke about championing productive uses of AI in the workplace, which ultimately benefit both employees and customers.

As one example, Zeynep Ton, professor at MIT Sloan School of Management, highlighted retailer Sam’s Club’s recent rollout of a program called Sam’s Garage. Previously customers shopping for tires for their car spent somewhere between 30 and 45 minutes with a Sam’s Club associate paging through manuals and looking up specs on websites.

But with an AI algorithm, they were able to cut that spec hunting time down to 2.2 minutes. “Now instead of wasting their time trying to figure out the different tires, they can field the different options and talk about which one would work best [for the customer],” she said. “This is a great example of solving a real problem, including [enhancing] the experience of the associate as well as the customer.”

“We think of it as an AI-first world that’s coming,” said Scott Prevost, VP of engineering at Adobe. Prevost said AI agents in Adobe’s software will behave something like a creative assistant or intern who will take care of more mundane tasks for you.

“We need a mindset change. That it is not just about minimizing costs or maximizing tax benefits, but really worrying about what kind of society we’re creating and what kind of environment we’re creating if we keep on just automating and [eliminating] good jobs.”
—Daron Acemoglu, MIT Institute Professor of Economics

Prevost cited an internal survey of Adobe customers that found 74 percent of respondents’ time was spent doing repetitive work—the kind that might be automated by an AI script or smart agent.

“It used to be you’d have the resources to work on three ideas [for a creative pitch or presentation],” Prevost said. “But if the AI can do a lot of the production work, then you can have 10 or 100. Which means you can actually explore some of the further out ideas. It’s also lowering the bar for everyday people to create really compelling output.”

In addition to changing the nature of work, noted a number of speakers at the event, AI is also directly transforming the workforce.

Jacob Hsu, CEO of the recruitment company Catalyte spoke about using AI as a job placement tool. The company seeks to fill myriad positions including auto mechanics, baristas, and office workers—with its sights on candidates including young people and mid-career job changers. To find them, it advertises on Craigslist, social media, and traditional media.

The prospects who sign up with Catalyte take a battery of tests. The company’s AI algorithms then match each prospect’s skills with the field best suited for their talents.

“We want to be like the Harry Potter Sorting Hat,” Hsu said.

Guillermo Miranda, IBM’s global head of corporate social responsibility, said IBM has increasingly been hiring based not on credentials but on skills. For instance, he said, as much as 50 per cent of the company’s new hires in some divisions do not have a traditional four-year college degree. “As a company, we need to be much more clear about hiring by skills,” he said. “It takes discipline. It takes conviction. It takes a little bit of enforcing with H.R. by the business leaders. But if you hire by skills, it works.”

Ardine Williams, Amazon’s VP of workforce development, said the e-commerce giant has been experimenting with developing skills of the employees at its warehouses (a.k.a. fulfillment centers) with an eye toward putting them in a position to get higher-paying work with other companies.

She described an agreement Amazon had made in its Dallas fulfillment center with aircraft maker Sikorsky, which had been experiencing a shortage of skilled workers for its nearby factory. So Amazon offered to its employees a free certification training to seek higher-paying work at Sikorsky.

“I do that because now I have an attraction mechanism—like a G.I. Bill,” Williams said. The program is also only available for employees who have worked at least a year with Amazon. So their program offers medium-term job retention, while ultimately moving workers up the wage ladder.

Radha Basu, CEO of AI data company iMerit, said her firm aggressively hires from the pool of women and under-resourced minority communities in the U.S. and India. The company specializes in turning unstructured data (e.g. video or audio feeds) into tagged and annotated data for machine learning, natural language processing, or computer vision applications.

“There is a motivation with these young people to learn these things,” she said. “It comes with no baggage.”

Alastair Fitzpayne, executive director of The Aspen Institute’s Future of Work Initiative, said the future of work ultimately means, in bottom-line terms, the future of human capital. “We have an R&D tax credit,” he said. “We’ve had it for decades. It provides credit for companies that make new investment in research and development. But we have nothing on the human capital side that’s analogous.”

So a company that’s making a big investment in worker training does it on their own dime, without any of the tax benefits that they might accrue if they, say, spent it on new equipment or new technology. Fitzpayne said a simple tweak to the R&D tax credit could make a big difference by incentivizing new investment programs in worker training. Which still means Amazon’s pre-existing worker training programs—for a company that already famously pays no taxes—would not count.

“We need a different way of developing new technologies,” said Daron Acemoglu, MIT Institute Professor of Economics. He pointed to the clean energy sector as an example. First a consensus around the problem needs to emerge. Then a broadly agreed-upon set of goals and measurements needs to be developed (e.g., that AI and automation would, for instance, create at least X new jobs for every Y jobs that it eliminates).

Then it just needs to be implemented.

“We need to build a consensus that, along the path we’re following at the moment, there are going to be increasing problems for labor,” Acemoglu said. “We need a mindset change. That it is not just about minimizing costs or maximizing tax benefits, but really worrying about what kind of society we’re creating and what kind of environment we’re creating if we keep on just automating and [eliminating] good jobs.” Continue reading

Posted in Human Robots

#436256 Alphabet Is Developing a Robot to Take ...

Robots excel at carrying out specialized tasks in controlled environments, but put them in your average office and they’d be lost. Alphabet wants to change that by developing what they call the Everyday Robot, which could learn to help us out with our daily chores.

For a long time most robots were painstakingly hand-coded to carry out their functions, but since the deep learning revolution earlier this decade there’s been a growing effort to imbue them with AI that lets them learn new tasks through experience.

That’s led to some impressive breakthroughs, like a robotic hand nimble enough to solve a Rubik’s cube and a robotic arm that can accurately toss bananas across a room.

And it turns out Alphabet’s early-stage research and development division, Alphabet X, has also secretly been using similar machine learning techniques to develop robots adaptable enough to carry out a range of tasks in cluttered and unpredictable human environments like homes and offices.

The robots they’ve built combine a wheeled base with a single arm and a head full of sensors (including LIDAR) for 3D scanning, borrowed from Alphabet’s self-driving car division, Waymo.

At the minute, though, they’re largely restricted to sorting trash for recycling, project leader Hans Peter Brondmo writes in a blog post. While that might sound mundane, identifying different kinds of trash, grasping it, and moving it to the correct bin is still a difficult thing for a robot to do consistently. Some of the robots also have to navigate around the office to sort trash at various recycling stations.

Alphabet says even its human staff were getting it wrong 20 percent of the time, but after several months of training the robots have managed to get that down to 3.5 percent.

Every day, 30 robots toil away in what’s been dubbed the “playpen” sorting trash, and then every night thousands of virtual robots continue to practice in a simulation. This experience is then used to update the robots’ control algorithms each night. All the robots also share their experiences with the others through a process called collaborative learning.

The process isn’t flawless, though. Simonite notes that while the robots exhibit some uncannily smart behaviors, like stirring piles of rubbish to make it easier to grab specific items, they also frequently miss or fumble the objects they’re trying to grasp.

Nonetheless, the project’s leaders are happy with their progress so far. And the hope is that creating robots that are able to learn from little more than experience in complex environments like an office should be a first step towards general-purpose robots that can pick up a variety of useful skills to assist humans.

Taking that next step will be the major test of the project. So far there’s been limited evidence that experience gained by robots in one task can be transferred to learning another. That’s something the group hopes to demonstrate next year.

And it seems there may be more robot news coming out of Alphabet X soon. The group has several other robotics “moonshots” in the pipeline, built on technology and talent transferred over in 2016 from the remains of a broadly unsuccessful splurge on robotics startups by former Google executive Andy Rubin.

Whether this robotics renaissance at Alphabet will finally help robots break into our homes and offices remains to be seen, but with the resources they have at hand, they just may be able to make it happen.

Image Credit: Everyday Robot, Alphabet X Continue reading

Posted in Human Robots

#436234 Robot Gift Guide 2019

Welcome to the eighth edition of IEEE Spectrum’s Robot Gift Guide!

This year we’re featuring 15 robotic products that we think will make fantastic holiday gifts. As always, we tried to include a broad range of robot types and prices, focusing mostly on items released this year. (A reminder: While we provide links to places where you can buy these items, we’re not endorsing any in particular, and a little bit of research may result in better deals.)

If you need even more robot gift ideas, take a look at our past guides: 2018, 2017, 2016, 2015, 2014, 2013, and 2012. Some of those robots are still great choices and might be way cheaper now than when we first posted about them. And if you have suggestions that you’d like to share, post a comment below to help the rest of us find the perfect robot gift.

Skydio 2

Image: Skydio

What makes robots so compelling is their autonomy, and the Skydio 2 is one of the most autonomous robots we’ve ever seen. It uses an array of cameras to map its environment and avoid obstacles in real-time, making flight safe and effortless and enabling the kinds of shots that would be impossible otherwise. Seriously, this thing is magical, and it’s amazing that you can actually buy one.
$1,000
Skydio
UBTECH Jimu MeeBot 2

Image: UBTECH

The Jimu MeeBot 2.0 from UBTECH is a STEM education robot designed to be easy to build and program. It includes six servo motors, a color sensor, and LED lights. An app for iPhone or iPad provides step-by-step 3D instructions, and helps you code different behaviors for the robot. It’s available exclusively from Apple.
$130
Apple
iRobot Roomba s9+

Image: iRobot

We know that $1,400 is a crazy amount of money to spend on a robot vacuum, but the Roomba s9+ is a crazy robot vacuum. As if all of its sensors and mapping intelligence wasn’t enough, it empties itself, which means that you can have your floors vacuumed every single day for a month and you don’t have to even think about it. This is what home robots are supposed to be.
$1,400
iRobot
PFF Gita

Photo: Piaggio Fast Forward

Nobody likes carrying things, which is why Gita is perfect for everyone with an extra $3,000 lying around. Developed by Piaggio Fast Forward, this autonomous robot will follow you around with a cargo hold full of your most important stuff, and do it in a way guaranteed to attract as much attention as possible.
$3,250
Gita
DJI Mavic Mini

Photo: DJI

It’s tiny, it’s cheap, and it takes good pictures—what more could you ask for from a drone? And for $400, this is an excellent drone to get if you’re on a budget and comfortable with manual flight. Keep in mind that while the Mavic Mini is small enough that you don’t need to register it with the FAA, you do still need to follow all the same rules and regulations.
$400
DJI
LEGO Star Wars Droid Commander

Image: LEGO

Designed for kids ages 8+, this LEGO set includes more than 1,000 pieces, enough to build three different droids: R2-D2, Gonk Droid, and Mouse Droid. Using a Bluetooth-controlled robotic brick called Move Hub, which connects to the LEGO BOOST Star Wars app, kids can change how the robots behave and solve challenges, learning basic robotics and coding skills.
$200
LEGO
Sony Aibo

Photo: Sony

Robot pets don’t get much more sophisticated (or expensive) than Sony’s Aibo. Strictly speaking, it’s one of the most complex consumer robots you can buy, and Sony continues to add to Aibo’s software. Recent new features include user programmability, and the ability to “feed” it.
$2,900 (free aibone and paw pads until 12/29/2019)
Sony
Neato Botvac D4 Connected

Photo: Neato

The Neato Botvac D4 may not have all of the features of its fancier and more expensive siblings, but it does have the features that you probably care the most about: The ability to make maps of its environment for intelligent cleaning (using lasers!), along with user-defined no-go lines that keep it where you want it. And it cleans quite well, too.
$530 $350 (sale)
Neato Robotics
Cubelets Curiosity Set

Photo: Modular Robotics

Cubelets are magnetic blocks that you can snap together to make an endless variety of robots with no programming and no wires. The newest set, called Curiosity, is designed for kids ages 4+ and comes with 10 robotic cubes. These include light and distance sensors, motors, and a Bluetooth module, which connects the robot constructions to the Cubelets app.
$250
Modular Robotics
Tertill

Photo: Franklin Robotics

Tertill does one simple job: It weeds your garden. It’s waterproof, dirt proof, solar powered, and fully autonomous, meaning that you can leave it out in your garden all summer and just enjoy eating your plants rather than taking care of them.
$350
Tertill
iRobot Root

Photo: iRobot

Root was originally developed by Harvard University as a tool to help kids progressively learn to code. iRobot has taken over Root and is now supporting the curriculum, which starts for kids before they even know how to read and should keep them busy for years afterwards.
$200
iRobot
LOVOT

Image: Lovot

Let’s be honest: Nobody is really quite sure what LOVOT is. We can all agree that it’s kinda cute, though. And kinda weird. But cute. Created by Japanese robotics startup Groove X, LOVOT does have a whole bunch of tech packed into its bizarre little body and it will do its best to get you to love it.
$2,750 (¥300,000)
LOVOT
Sphero RVR

Photo: Sphero

RVR is a rugged, versatile, easy to program mobile robot. It’s a development platform designed to be a bridge between educational robots like Sphero and more sophisticated and expensive systems like Misty. It’s mostly affordable, very expandable, and comes from a company with a lot of experience making robots.
$250
Sphero
“How to Train Your Robot”

Image: Lawrence Hall of Science

Aimed at 4th and 5th graders, “How to Train Your Robot,” written by Blooma Goldberg, Ken Goldberg, and Ashley Chase, and illustrated by Dave Clegg, is a perfect introduction to robotics for kids who want to get started with designing and building robots. But the book isn’t just for beginners: It’s also a fun, inspiring read for kids who are already into robotics and want to go further—it even introduces concepts like computer simulations and deep learning. You can download a free digital copy or request hardcopies here.
Free
UC Berkeley
MIT Mini Cheetah

Photo: MIT

Yes, Boston Dynamics’ Spot, now available for lease, is probably the world’s most famous quadruped, but MIT is starting to pump out Mini Cheetahs en masse for researchers, and while we’re not exactly sure how you’d manage to get one of these things short of stealing one directly for MIT, a Mini Cheetah is our fantasy robotics gift this year. Mini Cheetah looks like a ton of fun—it’s portable, highly dynamic, super rugged, and easy to control. We want one!
Price N/A
MIT Biomimetic Robotics Lab

For more tech gift ideas, see also IEEE Spectrum’s annual Gift Guide. Continue reading

Posted in Human Robots

#436220 How Boston Dynamics Is Redefining Robot ...

Gif: Bob O’Connor/IEEE Spectrum

With their jaw-dropping agility and animal-like reflexes, Boston Dynamics’ bioinspired robots have always seemed to have no equal. But that preeminence hasn’t stopped the company from pushing its technology to new heights, sometimes literally. Its latest crop of legged machines can trudge up and down hills, clamber over obstacles, and even leap into the air like a gymnast. There’s no denying their appeal: Every time Boston Dynamics uploads a new video to YouTube, it quickly racks up millions of views. These are probably the first robots you could call Internet stars.

Spot

Photo: Bob O’Connor

84 cm HEIGHT

25 kg WEIGHT

5.76 km/h SPEED

SENSING: Stereo cameras, inertial measurement unit, position/force sensors

ACTUATION: 12 DC motors

POWER: Battery (90 minutes per charge)

Boston Dynamics, once owned by Google’s parent company, Alphabet, and now by the Japanese conglomerate SoftBank, has long been secretive about its designs. Few publications have been granted access to its Waltham, Mass., headquarters, near Boston. But one morning this past August, IEEE Spectrum got in. We were given permission to do a unique kind of photo shoot that day. We set out to capture the company’s robots in action—running, climbing, jumping—by using high-speed cameras coupled with powerful strobes. The results you see on this page: freeze-frames of pure robotic agility.

We also used the photos to create interactive views, which you can explore online on our Robots Guide. These interactives let you spin the robots 360 degrees, or make them walk and jump on your screen.

Boston Dynamics has amassed a minizoo of robotic beasts over the years, with names like BigDog, SandFlea, and WildCat. When we visited, we focused on the two most advanced machines the company has ever built: Spot, a nimble quadruped, and Atlas, an adult-size humanoid.

Spot can navigate almost any kind of terrain while sensing its environment. Boston Dynamics recently made it available for lease, with plans to manufacture something like a thousand units per year. It envisions Spot, or even packs of them, inspecting industrial sites, carrying out hazmat missions, and delivering packages. And its YouTube fame has not gone unnoticed: Even entertainment is a possibility, with Cirque du Soleil auditioning Spot as a potential new troupe member.

“It’s really a milestone for us going from robots that work in the lab to these that are hardened for work out in the field,” Boston Dynamics CEO Marc Raibert says in an interview.

Atlas

Photo: Bob O’Connor

150 cm HEIGHT

80 kg WEIGHT

5.4 km/h SPEED

SENSING: Lidar and stereo vision

ACTUATION: 28 hydraulic actuators

POWER: Battery

Our other photographic subject, Atlas, is Boston Dynamics’ biggest celebrity. This 150-centimeter-tall (4-foot-11-inch-tall) humanoid is capable of impressive athletic feats. Its actuators are driven by a compact yet powerful hydraulic system that the company engineered from scratch. The unique system gives the 80-kilogram (176-pound) robot the explosive strength needed to perform acrobatic leaps and flips that don’t seem possible for such a large humanoid to do. Atlas has inspired a string of parody videos on YouTube and more than a few jokes about a robot takeover.

While Boston Dynamics excels at making robots, it has yet to prove that it can sell them. Ever since its founding in 1992 as a spin-off from MIT, the company has been an R&D-centric operation, with most of its early funding coming from U.S. military programs. The emphasis on commercialization seems to have intensified after the acquisition by SoftBank, in 2017. SoftBank’s founder and CEO, Masayoshi Son, is known to love robots—and profits.

The launch of Spot is a significant step for Boston Dynamics as it seeks to “productize” its creations. Still, Raibert says his long-term goals have remained the same: He wants to build machines that interact with the world dynamically, just as animals and humans do. Has anything changed at all? Yes, one thing, he adds with a grin. In his early career as a roboticist, he used to write papers and count his citations. Now he counts YouTube views.

In the Spotlight

Photo: Bob O’Connor

Boston Dynamics designed Spot as a versatile mobile machine suitable for a variety of applications. The company has not announced how much Spot will cost, saying only that it is being made available to select customers, which will be able to lease the robot. A payload bay lets you add up to 14 kilograms of extra hardware to the robot’s back. One of the accessories that Boston Dynamics plans to offer is a 6-degrees-of-freedom arm, which will allow Spot to grasp objects and open doors.

Super Senses

Photo: Bob O’Connor

Spot’s hardware is almost entirely custom-designed. It includes powerful processing boards for control as well as sensor modules for perception. The ­sensors are located on the front, rear, and sides of the robot’s body. Each module consists of a pair of stereo cameras, a wide-angle camera, and a texture projector, which enhances 3D sensing in low light. The sensors allow the robot to use the navigation method known as SLAM, or simultaneous localization and mapping, to get around autonomously.

Stepping Up

Photo: Bob O’Connor

In addition to its autonomous behaviors, Spot can also be steered by a remote operator with a game-style controller. But even when in manual mode, the robot still exhibits a high degree of autonomy. If there’s an obstacle ahead, Spot will go around it. If there are stairs, Spot will climb them. The robot goes into these operating modes and then performs the related actions completely on its own, without any input from the operator. To go down a flight of stairs, Spot walks backward, an approach Boston Dynamics says provides greater stability.

Funky Feet

Gif: Bob O’Connor/IEEE Spectrum

Spot’s legs are powered by 12 custom DC motors, each geared down to provide high torque. The robot can walk forward, sideways, and backward, and trot at a top speed of 1.6 meters per second. It can also turn in place. Other gaits include crawling and pacing. In one wildly popular YouTube video, Spot shows off its fancy footwork by dancing to the pop hit “Uptown Funk.”

Robot Blood

Photo: Bob O’Connor

Atlas is powered by a hydraulic system consisting of 28 actuators. These actuators are basically cylinders filled with pressurized fluid that can drive a piston with great force. Their high performance is due in part to custom servo valves that are significantly smaller and lighter than the aerospace models that Boston Dynamics had been using in earlier designs. Though not visible from the outside, the innards of an Atlas are filled with these hydraulic actuators as well as the lines of fluid that connect them. When one of those lines ruptures, Atlas bleeds the hydraulic fluid, which happens to be red.

Next Generation

Gif: Bob O’Connor/IEEE Spectrum

The current version of Atlas is a thorough upgrade of the original model, which was built for the DARPA Robotics Challenge in 2015. The newest robot is lighter and more agile. Boston Dynamics used industrial-grade 3D printers to make key structural parts, giving the robot greater strength-to-weight ratio than earlier designs. The next-gen Atlas can also do something that its predecessor, famously, could not: It can get up after a fall.

Walk This Way

Photo: Bob O’Connor

To control Atlas, an operator provides general steering via a manual controller while the robot uses its stereo cameras and lidar to adjust to changes in the environment. Atlas can also perform certain tasks autonomously. For example, if you add special bar-code-type tags to cardboard boxes, Atlas can pick them up and stack them or place them on shelves.

Biologically Inspired

Photos: Bob O’Connor

Atlas’s control software doesn’t explicitly tell the robot how to move its joints, but rather it employs mathematical models of the underlying physics of the robot’s body and how it interacts with the environment. Atlas relies on its whole body to balance and move. When jumping over an obstacle or doing acrobatic stunts, the robot uses not only its legs but also its upper body, swinging its arms to propel itself just as an athlete would.

This article appears in the December 2019 print issue as “By Leaps and Bounds.” Continue reading

Posted in Human Robots

#436215 Help Rescuers Find Missing Persons With ...

There’s a definite sense that robots are destined to become a critical part of search and rescue missions and disaster relief efforts, working alongside humans to help first responders move faster and more efficiently. And we’ve seen all kinds of studies that include the claim “this robot could potentially help with disaster relief,” to varying degrees of plausibility.

But it takes a long time, and a lot of extra effort, for academic research to actually become anything useful—especially for first responders, where there isn’t a lot of financial incentive for further development.

It turns out that if you actually ask first responders what they most need for disaster relief, they’re not necessarily interested in the latest and greatest robotic platform or other futuristic technology. They’re using commercial off-the-shelf drones, often consumer-grade ones, because they’re simple and cheap and great at surveying large areas. The challenge is doing something useful with all of the imagery that these drones collect. Computer vision algorithms could help with that, as long as those algorithms are readily accessible and nearly effortless to use.

The IEEE Robotics and Automation Society and the Center for Robotic-Assisted Search and Rescue (CRASAR) at Texas A&M University have launched a contest to bridge this gap between the kinds of tools that roboticists and computer vision researchers might call “basic” and a system that’s useful to first responders in the field. It’s a simple and straightforward idea, and somewhat surprising that no one had thought of it before now. And if you can develop such a system, it’s worth some cash.

CRASAR does already have a Computer Vision Emergency Response Toolkit (created right after Hurricane Harvey), which includes a few pixel filters and some edge and corner detectors. Through this contest, you can get paid your share of a $3,000 prize pool for adding some other excessively basic tools, including:

Image enhancement through histogram equalization, which can be applied to electro-optical (visible light cameras) and thermal imagery

Color segmentation for a range

Grayscale segmentation for a range in a thermal image

If it seems like this contest is really not that hard, that’s because it isn’t. “The first thing to understand about this contest is that strictly speaking, it’s really not that hard,” says Robin Murphy, director of CRASAR. “This contest isn’t necessarily about coming up with algorithms that are brand new, or even state-of-the-art, but rather algorithms that are functional and reliable and implemented in a way that’s immediately [usable] by inexperienced users in the field.”

Murphy readily admits that some of what needs to be done is not particularly challenging at all, but that’s not the point—the point is to make these functionalities accessible to folks who have better things to do than solve these problems themselves, as Murphy explains.

“A lot of my research is driven by problems that I’ve seen in the field that you’d think somebody would have solved, but apparently not. More than half of this is available in OpenCV, but who’s going to find it, download it, learn Python, that kind of thing? We need to get these tools into an open framework. We’re happy if you take libraries that already exist (just don’t steal code)—not everything needs to be rewritten from scratch. Just use what’s already there. Some of it may seem too simple, because it IS that simple. It already exists and you just need to move some code around.”

If you want to get very slightly more complicated, there’s a second category that involves a little bit of math:

Coders must provide a system that does the following for each nadir image in a set:

Reads the geotag embedded in the .jpg
Overlays a USNG grid for a user-specified interval (e.g., every 50, 100, or 200 meters)
Gives the GPS coordinates of each pixel if a cursor is rolled over the image
Given a set of images with the GPS or USNG coordinate and a bounding box, finds all images in the set that have a pixel intersecting that location

The final category awards prizes to anyone who comes up with anything else that turns out to be useful. Or, more specifically, “entrants can submit any algorithm they believe will be of value.” Whether or not it’s actually of value will be up to a panel of judges that includes both first responders and computer vision experts. More detailed rules can be found here, along with sample datasets that you can use for testing.

The contest deadline is 16 December, so you’ve got about a month to submit an entry. Winners will be announced at the beginning of January. Continue reading

Posted in Human Robots