In a recent study, Dr. Shah and a student had human-robot teams perform a chore borrowed from the assembly line: the humans placed screws and the robots did the drilling. Then the teammates exchanged jobs and the robots observed the humans drill.

“The robot gathers information on how the person does the drilling,” adding that information to its algorithms, Dr. Shah said. “The robot isn’t learning one optimal way to drill. Instead it is learning a teammate’s preferences, and how to cooperate.”

When the cross-trained teams resumed their original roles, both robots and people did their jobs more efficiently, the study found. The time that the humans were idle while waiting for the robot to finish a task dropped 41 percent and the time that humans and robots worked simultaneously increased 71 percent, when compared with teams working with robots trained the old way.

“This is a fascinating application of cross-training,” said Andrea Thomaz, an assistant professor of interactive computing at Georgia Tech. “By learning the human’s role, the robot can better anticipate actions and be a better partner, even if in the end it will only do one role.”