CAMBRIDGE, Mass. -- In today’s manufacturing plants, the division of labor between humans and robots is quite clear: Large, automated robots are typically cordoned off in metal cages, manipulating heavy machinery and performing repetitive tasks, while humans work in less hazardous areas on jobs requiring finer detail.
But according to Julie Shah, the Boeing Career Development Assistant Professor of Aeronautics and Astronautics at MIT, the factory floor of the future may host humans and robots working side by side, each helping the other in common tasks. Shah envisions robotic assistants performing tasks that would otherwise hinder a human’s efficiency, particularly in airplane manufacturing.
“If the robot can provide tools and materials so the person doesn’t have to walk over to pick up parts and walk back to the plane, you can significantly reduce the idle time of the person,” says Shah, who leads the Interactive Robotics Group in MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL). “It’s really hard to make robots do careful refinishing tasks that people do really well. But providing robotic assistants to do the non-value-added work can actually increase the productivity of the overall factory.”
A robot working in isolation has to simply follow a set of preprogrammed instructions to perform a repetitive task. But working with humans is a different matter: For example, each mechanic working at the same station at an aircraft assembly plant may prefer to work differently — and Shah says a robotic assistant would have to effortlessly adapt to an individual’s particular style to be of any practical use.
Now Shah and her colleagues at MIT have devised an algorithm that enables a robot to quickly learn an individual’s preference for a certain task, and adapt accordingly to help complete the task. The group is using the algorithm in simulations to train robots and humans to work together, and will present its findings at the Robotics: Science and Systems Conference in Sydney in July.
“It’s an interesting machine-learning human-factors problem,” Shah says. “Using this algorithm, we can significantly improve the robot’s understanding of what the person’s next likely actions are.”