A robot travelling from point A to point B is more efficient if it understands that point A is the living room couch and point B is a refrigerator, even if it's in an unfamiliar place. That's the common-sense idea behind a "semantic" navigation system developed by Carnegie Mellon University and Facebook AI Research (FAIR).

SemExp, or Goal-Oriented Semantic Exploration, uses machine learning to train a robot to recognize objects -- knowing the difference between a kitchen table and an end table, for instance -- and to understand where in a home such objects are likely to be found.

Classical robotic navigation systems, by contrast, explore space by building a map showing obstacles. The robot eventually gets to where it needs to go, but the route can be circuitous.
Previous attempts to use machine learning to train semantic navigation systems have been hampered because they tend to memorize objects and their locations in specific environments. Not only are these environments complex, but the system often has difficulty generalizing what it has learned to different environments.

Semantic navigation ultimately will make it easier for people to interact with robots, enabling them to simply tell the robot to fetch an item in a particular place, or give it directions such as "go to the second door on the left."