"Our algorithm actually strategizes and then explores all the possible and important motions and chooses the best one through dynamic simulation, like playing many simulated games involving moving robots," says Soon-Jo Chung, Caltech's Bren Professor of Control and Dynamical Systems and a senior research scientist at JPL, which Caltech manages for NASA. "The breakthrough innovation here is that we have derived a very efficient way of finding that optimal safe motion that typical optimization-based methods would never find."
"You don't want a designer to have to go in and handcraft these motions and say, 'This is the discrete set of moves the robot should be able to do,'" says John Lathrop, a graduate student in control and dynamical systems at Caltech and co-lead author. "Our system can evaluate and decide dynamically on motions, accommodating the unpredictable nature of real-world environments."
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