Miss Georgia tripped in the final round of the 2015 Miss America Pageant. Jennifer Lawrence stumbled on the way to accepting her Oscar. Rock stars, world leaders, and presidential candidates have all fallen in front of crowds.
Now, researchers have identified a way to help robots fall with grace — and without serious damage. This is important now that costly robots have become more common in manufacturing and are sought for health care or domestic tasks.
Georgia Tech Ph.D. graduate Sehoon Ha and Professor Karen Liu have developed a new algorithm that tells a robot how to react to a wide variety of falls — from taking a single step to recover from a gentle nudge, to going into a rolling motion to break a high-speed fall. By learning the best sequence of movements to slow their momentum, robots can minimize the damage or injury they might cause to themselves or others while falling. The algorithm has been validated in physics simulation and experimentally tested on a BioloidGP humanoid robot.
The latest finding builds upon Liu's previous research that studied how cats modify their bodies in the midst of a fall. Liu knew from that work that one of the most important factors in a fall is the angle of the landing. Armed with that information, the two researchers used the robots' silicon brains to optimize the sequence of motions that take place during a fall.
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