Which way to the fridge? Common sense helps robots navigate: Winning strategy speeds up robotic searches

That navigation system, called SemExp, last month won the Habitat ObjectNav Challenge during the virtual Computer Vision and Pattern Recognition conference, edging 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.