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Rice University

Dr. Lydia E. Kavraki: A Woman Making Robots Work

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Rice University

Dr. Lydia E. Kavraki’s list of accomplishments, accolades, and titles is almost too much to process—the computer scientist is currently the Noah Harding Professor of Computer Science and Bioengineering at Rice University, but she also holds an appointment at the Department of Structural and Computational Biology and Molecular Biophysics at the Baylor College of Medicine in Houston, in addition to sitting on a number of advisory boards for various publications, holding fellowships and memberships at a whole mess of associations and institutions, and running her own lab at Rice. It may sound a bit dry, until you realize what Kavraki’s work consists of: She makes robots work. It’s not bad work if you can get it (or, alternately, you’re brilliant enough to do it).

Kavraki’s work is incredibly complex (to put it mildly), but her robotics work essentially boils down to path planning for robots—making sure they have a collision-free path to follow. Her method, the Probabilistic Roadmap Method (PRM), is hailed for providing a paradigm shift across the robotics community, as it utilizes randomizing and sampling-based motion planners to path plan, a simpler technique than had been previously used (one that meant that all applicable path space had to be explored and taken into account). Kavraki also helped write the book on the subject—literally: Her Principles of Robot Motion is the preeminent text on the subject. She also developed the Open Motion Planning Library, part of the Robot Operating System, which is referred to as “the Unix of robotics”—it’s that essential to modern robotic movement. Kavraki’s research is highly applicable across all sorts of robotics, including previously unsolvable problems like how to dock an airspace shuttle to an orbiting space station and “teaching” robots how to tie knots when suturing in a surgical environment.

Kavraki’s expertise also extends to the world of bioinformatics, and her work there applies to the structure and flexibility of molecules, just in case she didn’t have enough to do already.

In 2000, Kavraki won the Association for Computing Machinery (ACM) Grace Murray Hopper Award for her technical contributions, an incredibly special award that only goes to a computer professional who makes a single, significant technical or service contribution at or before age 35. (How special? On five occasions, the award wasn’t given out; their standards are just that high.) She’s also got a Sloan Fellowship, an NSF CAREER award, recognition as a top young investigator from the MIT Technology Review magazine, a “Brilliant 10” designation from Popular Science, and a 2002 inclusion from Technology Review on annual list of 35 innovators under the age of 35, just to keep things interesting (and lauded).

For now, Kavraki continues to teach at Rice University, with her own Kavraki Lab bent on researching the two prongs of her scientific interests: robotics and bioinformatics and biomedicine. By all accounts, Kavraki’s brilliance in the lab translates to the classroom, as she is a recipient of Rice’s own Duncan Award for excellence in research and teaching. Kavraki’s dedication to the advancement of not only her research, but her students and science in general, is clear enough already, though beautifully minimized with one line on her Rice website linking out to still more accomplishments and awards, those of others dear to her, reading simply, “I am most proud of the accomplishments and awards of my students.”

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Nick Briggs/Comic Relief
What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]