However, one of the issues involved with having humans work individually with robots is that every person works differently, and the robots will have to adapt to each worker.

To address that problem, Shah and her research team at MIT created an algorithm that to quickly learn an individual's preference for a certain task, and adapt accordingly to help complete the task. If the robot can learn and adjust quickly, it can move seamlessly from working with one worker to another.

Researchers are using the algorithm to train robots to work with humans, according to MIT. Shaw and her team are scheduled to present their findings at the Robotics: Science and Systems Conference in Sydney in July.

"It's an interesting machine-learning human-factors problem," Shah said. "Using this algorithm, we can significantly improve the robot's understanding of what the person's next likely actions are."

Researchers used a computational model in the form of a decision tree. Each branch of the tree represents a choice that a mechanic might make. For instance, does the mechanic want to put one bolt in place and hammer it in, or does the worker want to put a row of bolts in place first and then hammer them in.