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Toyota Announces Major Push Into AI and Robotics, Wants Cars That Never Crash

At a press conference in Palo Alto, Calif., today, Toyota is announcing the first step of what is expected to be a major push into artificial intelligence and robotics, technologies that the company sees as critical for addressing current and future societal challenges. Toyota, the world’s largest automaker by sales, says it will establish two collaborative research centers at MIT and Stanford, with an investment of $50 million over the next five years. The initial focus will be on accelerating the development of AI with applications to smarter and safer vehicles, as well as robots that can make our lives better at home, especially as we age.

Toyota says an immediate goal is to figure out ways to save lives on the road. But the company is very clear that it’s not trying to develop a fully autonomous car in the same way that Google and many others are. Instead, they’re working on assistive autonomy: you’ll be driving most of the time (or at least in control of the vehicle), but the vehicle will be continuously sensing and interpreting the environment around you, ready to step in as soon as it detects a dangerous situation. Toyota believes this approach could make cars virtually crash-proof.

“Our long-term goal is to make a car that is never responsible for a crash,” says Dr. Gill Pratt, who was until just a few months ago the program manager at DARPA responsible for the DARPA Robotics Challenge (among other ambitious robotics programs) and will now direct this research at Toyota. He added that such intelligent cars will “allow older people to be able to drive, and help prevent the one and a half million deaths that occur as a result of cars every single year around the world.”

Dr. Pratt will be working with Professor Daniela Rus, head of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), as well as Professor Fei-Fei Li, director of the Stanford Artificial Intelligence Laboratory (SAIL).

Earlier this week, we spoke with Pratt, Rus, and Li to get all the details on what we have to look forward to over the next five years.

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Poison-Injecting Robot Submarine Assassinates Sea Stars to Save Coral Reefs

You might think that the biggest threat to the world’s coral reefs is humanity. And you’d be right, of course: climate change, pollution, overfishing, and scuba divers who have no idea where their fins are all contribute to coral reef destruction. There are other, more natural but no less worrisome causes as well, and one of those is the crown-of-thorns sea star. It’s big, it’s spiky, and it eats coral for breakfast, lunch, and dinner.

Population explosions of these sea stars can devastate entire reefs, and it’s not unheard of to see 100,000 crown-of-thorns sea stars per square kilometer. There isn’t a lot that we can do to combat these infestations, because the sea stars can regenerate from absurd amounts of physical damage (they have to be almost entirely dismembered or completely buried under rocks), so humans have to go up to each and every sea star and inject them with poison 10 times over (!) because once isn’t enough.

Bring on the autonomous stabby poison-injecting robot submarines, please.

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Open Source Project Proposes Vision-Free Grasping With RFID and Touchscreens

At IROS  2012, Gill Pratt declared that grasping was solved, which was a bit of a surprise for all the people doing grasping research. Grasping, after all, is the easiest thing ever, as long as you know absolutely everything there is to know about the thing that you want to grasp. The tricky bit now is perception: recognizing what the object that you want to grasp is, where it is, and how it’s oriented. This is why robots are festooned with all sorts of sensing things, but if all you care about is manipulating an object that you’re familiar with already, dealing with vision is a lot of work.

Liatris is an open-source hardware and software project (led by roboticist Mark Silliman) that does away with vision completely. Instead, you can determine the identity and pose of slightly modified objects with just a touchscreen and an RFID reader. It’s simple, relatively inexpensive, and as long as you’re not trying to deal with anything new, it works impressively well.

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DARPA Wants Swarms of Cheap “Gremlin” Drones

A gremlin is a sort of mythical fairy that RAF pilots blamed for causing mechanical problems with their aircraft as far back as World War I. This sounds like a bad thing, but apparently, gremlins were actually good luck charms for pilots. As long as you listen to your Irish girlfriend and feed them cream. Or something.

Anyway, Gremlins is also the name of a new DARPA program that’s seeking proposals to develop the technology to launch swarms of low-cost, reusable unmanned aerial vehicles (UAVs) over great distances and then retrieve them in mid-air.

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Is a Cambrian Explosion Coming for Robotics?

This article originally appeared in the Journal of Economic Perspectives, Vol. 29, No. 3 (Summer 2015). We thank the American Economic Association for giving us permission to reproduce it here.

About half a billion years ago, life on earth experienced a short period of very rapid diversification called the “Cambrian Explosion.” Many theories have been proposed for the cause of the Cambrian Explosion, with one of the most provocative being the evolution of vision, which allowed animals to dramatically increase their ability to hunt and find mates (for discussion, see Parker 2003). Today, technological developments on several fronts are fomenting a similar explosion in the diversification and applicability of robotics. Many of the base hardware technologies on which robots depend—particularly computing, data storage, and communications—have been improving at exponential growth rates. Two newly blossoming technologies—“Cloud Robotics” and “Deep Learning”—could leverage these base technologies in a virtuous cycle of explosive growth. In Cloud Robotics—a term coined by James Kuffner (2010)—every robot learns from the experiences of all robots, which leads to rapid growth of robot competence, particularly as the number of robots grows. Deep Learning algorithms are a method for robots to learn and generalize their associations based on very large (and often cloud-based) “training sets” that typically include millions of examples. Interestingly, Li (2014) noted that one of the robotic capabilities recently enabled by these combined technologies is vision—the same capability that may have played a leading role in the Cambrian Explosion.

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Video Friday: Mini Surgical Robot, Precision Drones, and Bioinspired Robotics at Harvard

When you have a brand new robot to show the world, it’s not always easy to come up with a demo that will attract attention, especially if your robot does stuff that’s (and forgive us for saying this) inherently kind of boring. Don’t get me wrong: robots that do boring things are very important, because otherwise humans would be doing those things instead.

PRENAV (which I’m going to call Prenav so that I don’t get a headache) is introducing an aerial robot that can inspect tall structures, and what’s impressive about it is that it can (through the assistance of another robot on the ground) localize itself with centimeter-level accuracy. To demonstrate how well this works, Prenav stuck some lights on its drone and photographed it while it flew around. The timelapsed footage is amazing.

Be amazed, and then watch some other videos, it’s Video Friday.

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Robots With Smooth Moves Are Up to 40% More Efficient

Robot arms (and robots in general) are all about following orders. Your orders, merciless human overlord. You tell them to jump, and they say “I’m an arm, I don’t jump, but I can move around a little bit if you want.” And then they do, as best as they can. As far as the arm is concerned, its entire reason for existing is to move where you tell it to as fast as possible, I guess because it figures (usually quite wrongly) that you have better things to do than sit there and mind it.

These fast, precise movements are one of the reasons that we like robots as much as we do, but as it turns out, they’re not particularly energy efficient. This might not be something that you think about after dropping tens of thousands of dollars (or whatever) on a robot arm, but energy use adds up, especially if you have tens of thousands (or whatever) of arms.

Swedish researchers at Chalmers University of Technology, working as part of the European Union’s AREUS Project (Automation and Robotics for European Sustainable Manufacturing), have taken a crack at robot arm efficiency, and come up with an optimization algorithm that tweaks acceleration and deceleration to reduce energy consumption by up to 40 percent.

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Sony's New Drone: a Modern Take on a Familiar Design

Last month, Sony Mobile announced a partnership with ZMP to build drones. Or rather, “to collaborate on the development and launch of enterprise solutions using autonomous unmanned aerial vehicles for image capture combined with cloud-based data processing.”

Okay then.

To me, this kind of sounds like Sony mostly just not wanting to be left out of this whole drone thing that everybody seems to be so excited about, so they figure they’d better come up with some drones that can, you know, do some… stuff (they mentioned “solutions that meet needs including measuring, surveying, observing, and inspecting”). Having said that, if Sony can develop a reliable and streamlined real-time cloud interface for drones, that would be pretty cool.

The partnership between Sony and ZMP is called Aerosense, and yesterday, they released a flight test video of their new fixed-wing VTOL drone.

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Video Friday: Giant Fighting Robots, Glass 3D Printer, and 10 New Robots from Fetch

Once again, the biggest thing that happened in robotics this week was apparently something about a giant robot duel, despite the fact that we posted some absolutely excellent stuff about robot arms control and simulated evolution in leafcutter ants. But, it’s basically impossible to compete with giant robot duels, so that’s what we’re starting with today.

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