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To avoid these constraints, researchers at the Massachusetts Institute of Technology combined different models, with each addressing a different type of constraint, to develop a new model that can find solutions collectively.models, a kind of generative artificial intelligence models that are trained together, so they share some knowledge, like the geometry of the objects the robot will tackle.
“My vision is to push robots to do more complicated tasks that have many geometric constraints and more continuous decisions that need to be made — these are the kinds of problems service robots face in our unstructured and diverse human environments,” said Zhutian Yang, an electrical engineering and computer science graduate student and lead author the
“We don’t always get to a solution at the first guess. But when you keep refining the solution and some violation happens, it should lead you to a better solution. You get guidance from getting something wrong,” added Yang.
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