DARPA Pushes Machine Learning with Legged LittleDog Robot

With phase two testing wrapped up, six teams of roboticists are focused on improving LittleDog's speed and agility

Courtesy of Boston Dynamics/DARPA/Carnegie Mellon University

Editor's note: Legged robots have the ability to follow troops on long journeys across extremely difficult terrain. In our series on legged robotics, Scientific American Online explores the challenges such technology poses as well as two DARPA projects—BigDog and LittleDog—that have shown great promise.

If BigDog is the Defense Advanced Research Projects Agency's (DARPA) dopey but lovable Great Dane, LittleDog is its extremely intelligent—if high-strung—Jack Russell terrier.

Shortly after DARPA commissioned Boston Dynamics to build its BigDog autonomous legged robot, the agency decided it should broaden its research to include a likewise legged device that was aware of its environment and deliberately placed its feet to avoid falling. LittleDog's software spells out the robot's route and its cameras and sensors help it "see" obstacles so it can avoid missteps.

While BigDog's quick thinking and nimbleness has its limits—particularly if it happens to step off of a high ledge or cliff, LittleDog's specialty is being able to sense its surroundings and avoid such dangers all together. It methodically moves over obstacles much larger than its leg length and body size—it measures 11.8 by 7.1 inches (30 by 18 centimeters), stands 5.5 inches (14 centimeters) tall and weighs 4.9 pounds (2.2 kilograms). "We wanted LittleDog to deal with the locomotion problem," says Larry Jackel, a DARPA program manager responsible for robotic vehicles who spent four years at the agency until June 2007 and now works as an independent consultant.

DARPA is looking for its mini-legged robot to cross progressively difficult terrain at increased speeds. "BigDog and LittleDog are related in that they are both focused on solving the problems that will enable legged robots to accompany war fighters as they cross complex terrain," says Tom Wagner, program manager in DARPA's Information Processing Techniques Office. (For more on BigDog, read "Leggy 'BigDog' Robot Set to Step Up for the Military.")

Phase two of LittleDog's development recently wrapped up, and phase three is set to begin this summer. In the first phase, which began in late 2005, DARPA asked six teams of roboticists—from Carnegie Mellon University, the Florida University System's Institute for Human and Machine Cognition, the Massachusetts Institute of Technology, Stanford University, the University of Southern California and the University of Pennsylvania—to improve on the same basic quadruped robot platform, which DARPA paid Boston Dynamics more than $1.6 million to design, build and support. To successfully complete this phase, each team's LittleDog needed to move at the rate of at least a half an inch (1.3 centimeters) per second over terrain that included obstacles 1.9 inches (4.8 centimeters) in height.

To succeed in phase two, which ends today, the teams need to tune their LittleDogs to scurry 1.7 inches (4.3 centimeters) per second across obstacles 3.1 inches (7.9 centimeters) tall. Now the teams have their eyes on funding for the next phase, whose requirements are 2.8 inches (7.1 centimeters) per second across obstacles 4.3 inches (10.9 centimeters) tall. To do this, DARPA scientists created a specific terrain for LittleDog, which is equipped with sensors in each leg. "We knew where the robot was with respect to its environment," Jackel says.

One of the LittleDog competition's biggest challenges has been improving on the original software so that the robot can read any map and then navigate the map's terrain, says Carnegie Mellon Robotics Institute research scientist Drew Bagnell. The teams are asked to ship nothing more than a hard drive containing their software to DARPA, which then loads the program into their own version of LittleDog. "There's a blind component to the test," he adds. "We get tested on terrain that we've never seen nor will ever see."

DARPA's strategy is a sound one, says James Kuffner, an associate professor at the Robotics Institute. It is a good testing strategy because it forces the roboticists to write software that works for a variety of terrains, he adds. "We don't want to hard code something into the robot that works for only a few examples."

One thing DARPA will not do is commission a remote-controlled legged robot, which the agency believes would be impossible for one person to manage. Jackel likens it to driving a car that has four steering wheels. Instead, DARPA has called on LittleDog's research teams to develop algorithms that manage each leg. "People have been talking about legged vehicles since the 1960s but they didn't have good algorithms to make the work," he says. "If you know that every step is going to be a repeat of the previous step, it's not that hard. But when you get to unstructured environments, that's where things fall apart."

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