With robots increasingly being used on factory floors and elsewhere, researchers are looking for ways to help humans work better with their electromechanical counterparts.
Scientists at MIT say the answer is cross-training: Humans and robots should switch jobs to learn how they affect each other's work.
"People aren't robots -- they don't do things the same way every single time," said Julie Shah, head of MIT's Interactive Robotics Group, in a statement. So there's a "mismatch between the way we program robots to perform tasks in exactly the same way each time and what we need them to do" if they are going to work with people.
A lot of work has been done on the safety issues that must be addressed when robots and people work together. But, Shah said, more research needs to be done to make robots smart and flexible enough to work more effectively with humans.
To advance research in that area, Shah and MIT doctoral candidate Stefanos Nikolaidis built an algorithm to allow robots to learn from people, so humans could show the robots how they want their jobs done.
After the cross-training, MIT reported, humans and robots worked together in teams 71% more efficiently and the time that people spent waiting for robots to complete tasks dropped by 41%.
This version of this story was originally published in Computerworld's print edition. It was adapted from an article that appeared earlier on Computerworld.com.
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This story, "MIT aims to boost robot-human synergy" was originally published by Computerworld.