In restaurants around the world, from Shanghai to New York, robots are cooking meals. They make burgers and dosas, pizzas and stir-fries, in much the same way robots have made other things for the past 50 years: by following instructions precisely, doing the same steps in the same way, over and over.
But Ishika Singh wants to build a robot that can make dinner—one that can go into a kitchen, riffle through the fridge and cabinets, pull out ingredients that will coalesce into a tasty dish or two, then set the table. It’s so easy that a child can do it. Yet no robot can. It takes too much knowledge about that one kitchen—and too much common sense and flexibility and resourcefulness—for robot programming to capture.
The problem, says Singh, a Ph.D. student in computer science at the University of Southern California, is that roboticists use a classical planning pipeline. “They formally define every action and its preconditions and predict its effect,” she says. “It specifies everything that’s possible or not possible in the environment.” Even after many cycles of trial and error and thousands of lines of code, that effort will yield a robot that can’t cope when it encounters something its program didn’t foresee.
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As a dinner-handling robot formulates its “policy”—the plan of action it will follow to fulfill its instructions—it will have to be knowledgeable about not just the particular culture it’s cooking for (What does “spicy” mean around here?) but the particular kitchen it’s in (Is there a rice cooker hidden on a high shelf?) and the particular people it’s feeding (Hector will be extra hungry from his workout) on that particular night (Aunt Barbara is coming over, so no…
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