Welcome to the home page of the computer Hex research group. We --- Kenny Young, Kelly Li, Broderick, Phil, Ryan, Jakub (and previously Aja, David, Jack, Mike, Morgan, Nathan Po, Maryia, Martha, Leah, Yngvi, Geoff Ryan, and Robert Budac) --- build Hex players and solvers. The group informally dates from 1999, when Jack, who wrote Queenbee, started an MSc with Jonathan. Current projects include MoHex, and Solver. Previous projects include Wolve, Mongoose and Queenbee.

You get into your car and ask it to get you home in time for the start of the big game, stopping off at your favorite Chinese restaurant on the way to grab some takeout. But the car informs you that the road past the Chinese restaurant is closed for repairs, so you will not make it home in 30 minutes unless you choose a different food outlet. You select a nearby Korean restaurant from the options the car suggests, and set off on the chosen route. Vehicles, robots and other autonomous devices could soon collaborate with humans in this way, thanks to researchers at MIT who are developing systems capable of negotiating with people to determine the best way to achieve their goals. "In general, everything around us is getting smarter," says Brian Williams, a professor of aeronautics and astronautics and leader of the Model-Based Embedded and Robotic Systems group within MIT's Computer Science and Artificial Intelligence Laboratory.

Researchers at MIT's Computer Science and Artificial Intelligence Laboratory have developed a new algorithm that can accurately measure the heart rates of people depicted in ordinary digital video by analyzing imperceptibly small head movements that accompany the rush of blood caused by the heart's contractions. In tests, the algorithm gave pulse measurements that were consistently within a few beats per minute of those produced by electrocardiograms (EKGs). It was also able to provide useful estimates of the time intervals between beats, a measurement that can be used to identify patients at risk for cardiac events. Guha Balakrishnan, a graduate student in MIT's Department of Electrical Engineering and Computer Science, and his two advisors -- John Guttag, the Dugald C. Jackson Professor of Electrical Engineering and Computer Science and director of MIT's Data-Driven Medicine Group, and professor of computer science and engineering Fredo Durand -- describe the new algorithm in a paper appearing this summer at the Institute of Electrical and Electronics Engineers' Computer Vision and Pattern Recognition conference. A video-based pulse-measurement system could be useful for monitoring newborns or the elderly, whose sensitive skin could be damaged by frequent attachment and removal of EKG leads.

Spending a day in someone else's shoes can help us to learn what makes them tick. Now the same approach is being used to develop a better understanding between humans and robots, to enable them to work together as a team. Robots are increasingly being used in the manufacturing industry to perform tasks that bring them into closer contact with humans. But while a great deal of work is being done to ensure robots and humans can operate safely side-by-side, more effort is needed to make robots smart enough to work effectively with people, says Julie Shah, an assistant professor of aeronautics and astronautics at MIT and head of the Interactive Robotics Group in the Computer Science and Artificial Intelligence Laboratory (CSAIL). "People aren't robots, they don't do things the same way every single time," Shah says.

John Mikhael sees three fields as key to understanding the brain: math, neuroscience, and medicine. "If you want to understand how the brain works, combining those three is a great way to get there," he says. Mikhael, who graduated from MIT in June with a bachelor's degree in mathematics, plans to pursue his study of neuroscience next fall when he enters an MD/PhD program at Oxford University with a Rhodes Scholarship. "Neuroscience is a very exciting field," he says. "In many ways, the brain is the most sophisticated computer out there. Our brains can do things effortlessly that we couldn't even dream of teaching computers how to do, like producing language, understanding social cues, or recognizing faces with our level of proficiency."

The birth of artificial-intelligence research as an autonomous discipline is generally thought to have been the monthlong Dartmouth Summer Research Project on Artificial Intelligence in 1956, which convened 10 leading electrical engineers -- including MIT's Marvin Minsky and Claude Shannon -- to discuss "how to make machines use language" and "form abstractions and concepts." A decade later, impressed by rapid advances in the design of digital computers, Minsky was emboldened to declare that "within a generation ... the problem of creating'artificial intelligence' will substantially be solved." The problem, of course, turned out to be much more difficult than AI's pioneers had imagined. In recent years, by exploiting machine learning -- in which computers learn to perform tasks from sets of training examples -- artificial-intelligence researchers have built special-purpose systems that can do things like interpret spoken language or play Jeopardy with great success. But according to Tomaso Poggio, the Eugene McDermott Professor of Brain Sciences and Human Behavior at MIT, "These recent achievements have, ironically, underscored the limitations of computer science and artificial intelligence. We do not yet understand how the brain gives rise to intelligence, nor do we know how to build machines that are as broadly intelligent as we are."

Many robotic designs take nature as their muse: sticking to walls like geckos, swimming through water like tuna, sprinting across terrain like cheetahs. Such designs borrow properties from nature, using engineered materials and hardware to mimic animals' behavior. Now, scientists at MIT and the University of Pennsylvania are taking more than inspiration from nature -- they're taking ingredients. The group has genetically engineered muscle cells to flex in response to light, and is using the light-sensitive tissue to build highly articulated robots. This "bio-integrated" approach, as they call it, may one day enable robotic animals that move with the strength and flexibility of their living counterparts.

Computer scientists at MIT and the National University of Ireland (NUI) at Maynooth have developed a mapping algorithm that creates dense, highly detailed 3-D maps of indoor and outdoor environments in real time. The researchers tested their algorithm on videos taken with a low-cost Kinect camera, including one that explores the serpentine halls and stairways of MIT's Stata Center. Applying their mapping technique to these videos, the researchers created rich, three-dimensional maps as the camera explored its surroundings. As the camera circled back to its starting point, the researchers found that after returning to a location recognized as familiar, the algorithm was able to quickly stitch images together to effectively "close the loop," creating a continuous, realistic 3-D map in real time. The technique solves a major problem in the robotic mapping community that's known as either "loop closure" or "drift": As a camera pans across a room or travels down a corridor, it invariably introduces slight errors in the estimated path taken.

Imagine a robot able to retrieve a pile of laundry from the back of a cluttered closet, deliver it to a washing machine, start the cycle and then zip off to the kitchen to start preparing dinner. This may have been a domestic dream a half-century ago, when the fields of robotics and artificial intelligence first captured public imagination. However, it quickly became clear that even "simple" human actions are extremely difficult to replicate in robots. Now, MIT computer scientists are tackling the problem with a hierarchical, progressive algorithm that has the potential to greatly reduce the computational cost associated with performing complex actions. Leslie Kaelbling, the Panasonic Professor of Computer Science and Engineering, and Tomás Lozano-Pérez, the School of Engineering Professor of Teaching Excellence and co-director of MIT's Center for Robotics, outline their approach in a paper titled "Hierarchical Task and Motion Planning in the Now," which they presented at the IEEE Conference on Robotics and Automation earlier this month in Shanghai.

MIT's version of the "robotoddler" is just the latest MIT entry in the world of robots that can move themselves in a variety of settings. There's still a long way to go before today's robots evolve into practical, everyday technologies, but even now, autonomous robotic vehicles developed at MIT are exploring uncharted or hazardous places, assisting troops in combat and performing household tasks. In addition to his well-known work on humanoid robots such as Kismet, Professor Rodney Brooks led the development of several robotic vehicles and co-founded a company, iRobot, that develops these machines commercially. Troops in Afghanistan use PackBots to explore enemy caves, and soldiers in Iraq use them to detect improvised explosive devices and inspect weapons caches. "In 20 years, we've gone from robots that can hardly maneuver around objects to ones that can navigate in unstructured environments," said Brooks, director of the Computer Science and Artificial Intelligence Laboratory (CSAIL).