Carnegie Mellon's winning strategy speeds up robotic searches
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).
That navigation system, called SemExp, last month won the Habitat ObjectNav Challenge during the virtual Computer Vision and Pattern Recognition conference, edging out a team from Samsung Research China. It was the second consecutive first-place finish for the CMU team in the annual challenge.
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. This enables the system to think strategically about how to search for something, said Devendra S. Chaplot, a Ph.D. student in CMU's Machine Learning Department.
"Common sense says that if you're looking for a refrigerator, you'd better go to the kitchen," Chaplot said. Classical robotic navigation systems, by contrast, explore a space by building a map showing obstacles. The robot eventually gets to where it needs to go, but the route can be circuitous.
