Wired reporter Matt Simon writes that CSAIL researchers have developed a new virtual system that could eventually be used to teach robots how to perform household chores. Researchers hope the system could one day help robots, “learn to anticipate future actions and be able to change the environment for the human,” explains PhD student Xavier Puig.
MIT researchers have developed a virtual reality system that can train drones to fly faster while also avoiding obstacles, reports Lauren Barack for Salon. Barack explains that the “researchers are programming the drones so they think they're in a living room or bedroom while they fly. They virtually see obstacles around them, but those impediments aren't really there.”
Using LiDAR sensors, MIT researchers have developed an autonomous vehicle navigation system for rural roads with “no detailed, three-dimensional map for the vehicle to reference,” reports Rob Verger of Popular Science. “The solution for urban mapping really doesn’t scale very well to a huge portion of the country,” explains graduate student Teddy Ort.
CSAIL researchers have developed a system that uses LIDAR and GPS to allow self-driving cars to navigate rural roads without detailed maps, writes Tracey Lindeman of Motherboard. Autonomous ride-hailing or car-sharing is important in rural communities because “the carless in these areas have few transportation options; many small communities don’t even have public buses,” notes Lindeman.
Eric Mack writes for Forbes about a new system from MIT researchers that uses GPS in conjunction with LIDAR and IMU sensors to power self-driving vehicle navigation. Graduate student Teddy Ort says the system “shows the potential of self-driving cars being able to actually handle roads beyond the small number that tech companies have mapped.”
MapLite, a new system developed by CSAIL, aims to help autonomous vehicles navigate uncharted areas, writes Jesus Diaz for Co.Design. “[I]f autonomous cars can reach the millions of people who live beyond the city and are unable to pilot their own vehicles,” said graduate student Teddy Ort, “they will be uniquely capable of providing mobility to those who have very few alternatives.”
Emily Matchar of Smithsonian details research out of the Media Lab, which seeks to help both autonomous and standard vehicles avoid obstacles in heavy fog conditions. “You’d see the road in front of you as if there was no fog,” says graduate student and lead researcher Guy Satat. “[O]r the car would create warning messages that there’s an object in front of you.”
MIT Media Lab researchers have created a system that can detect obstacles through fog that are not visible to the human eye, writes Darren Weaver for CNBC. “The goal is to integrate the technology into self-driving cars so that even in bad weather, the vehicles can avoid obstacles,” explains Warren.
MIT researchers have developed a new imaging system that could allow autonomous vehicles to see through dense fog, writes Andrew Liszewski of Gizmodo. The laser-based system, which used a new processing algorithm, was able “to clearly see objects 21 centimeters further away than human eyes could discern,” Liszewski writes.
Graduate student Achuta Kadambi speaks with the BBC’s Gareth Mitchell about the new depth sensors he and his colleagues developed that could eventually be used in self-driving cars. “This new approach is able to obtain very high-quality positioning of objects that surround a robot,” Kadambi explains.
Fortune reporter David Morris writes that MIT researchers have tricked an artificial intelligence system into thinking that a photo of a machine gun was a helicopter. Morris explains that, “the research points towards potential vulnerabilities in the systems behind technology like self-driving cars, automated security screening systems, or facial-recognition tools.”
Abigail Beall of New Scientist writes that MIT researchers have developed an algorithm that can trick an AI system, highlighting potential weaknesses in new image-recognition technologies used in everything from self-driving cars to facial recognition systems. “If a driverless car failed to spot a pedestrian or a security camera misidentified a gun the consequences could be incredibly serious.”
CSAIL researchers have tricked a machine-learning algorithm into misidentifying an object, reports Louise Matsakis for Wired. The research, “demonstrates that attackers could potentially create adversarial examples that can trip up commercial AI systems,” explains Matsakis.
Using video to processes shadows, MIT researchers have developed an algorithm that can see around corners, writes Alyssa Meyers for The Boston Globe. “When you first think about this, you might think it’s crazy or impossible, but we’ve shown that it’s not if you can understand the physics of how light propagates,” says lead author and MIT graduate Katie Bouman.
CSAIL researchers have developed a system that detects objects and people hidden around blind corners, writes Anthony Cuthbertson for Newsweek. “We show that walls and other obstructions with edges can be exploited as naturally occurring ‘cameras’ that reveal the hidden scenes beyond them,” says lead author and MIT graduate Katherine Bouman.