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Humans can grab a book from a shelf with little obvious thought. But it's a complex process for the brain that involves planning and navigating around obstacles, like other books or knickknacks. Robotics researchers have struggled to replicate this kind of human movement when their systems perform similar tasks. Known as motion planning, the process of training a robot to get an object from one point to another without hitting any obstacles takes time and resources because the robot can't react dynamically like humans in unknown environments.

A team from Carnegie Mellon University's Robotics Institute (RI) has developed Neural Motion Planning to help improve how robots react in new environments. The data-driven approach uses a single, versatile, artificial intelligence network to perform motion planning in various unfamiliar household environments, like cabinets, dishwashers and refrigerators.

"Sometimes when you deploy a robot, you want it to operate in unstructured or unknown settings—environments where you can't assume that you know everything," said Murtaza Dalal, an RI doctoral student. "That's where these classic motion planning methods break down. One big issue is that these algorithms are very slow because they have to do thousands, maybe even millions, of collision checks."

Neural Motion Planning was inspired by how humans gather diverse experiences to practice and gradually increase proficiency. When acquiring new skills, humans start with slow, unsure behavior and progress to fast, dynamic motions. Neural Motion Planning allows robots to be more versatile in unfamiliar environments and to generally adapt when moving objects.

Credit: Carnegie Mellon University

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