The algorithm lets robots find the shortest route in unfamiliar environments, opening the door to robots that can work inside homes and offices.

The news: A team at Facebook AI has created a reinforcement learning algorithm that lets a robot find its way in an unfamiliar environment without using a map. Using just a depth-sensing camera, GPS, and compass data, the algorithm gets a robot to its goal 99.9% of the time along a route that is very close to the shortest possible path, which means no wrong turns, no backtracking, and no exploration. This is a big improvement over previous best efforts.

Why it matters: Mapless route-finding is essential for next-gen robots like autonomous delivery drones or robots that work inside homes and offices. Some of the best robots available today, such as Spot and Atlas made by Boston Dynamics and Digit made by Agility Robotics, are packed with sensors that make them pretty good at keeping their balance and avoiding obstacles. But if you dropped them off at an unfamiliar street corner and left them to find their way home, they’d be screwed. While Facebook’s algorithm does not yet handle outside environments, it is a promising step in that direction and could probably be adapted to urban spaces.

Two billion steps and counting: Facebook trained bots for three days inside AI Habitat, a photorealistic virtual mock-up of the interior of a building, with rooms and corridors and furniture. In that time they took 2.5 billion steps—the equivalent of 80 years of human experience. Others have taken a month or more to train bots in a similar task, but Facebook massively sped things up by culling the slowest bots from the pool so that faster ones did not have to wait at the finish line each round.

As ever, the team doesn’t know exactly how the AI learned to navigate, but a best guess is that it picked up on patterns in the interior structure of the human-designed environments. Facebook is now testing its algorithm in real physical spaces using a LoCoBot robot.

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