Julie Shah, 32
This MIT engineering professor is turning robots into ideal colleagues for humans.
“In factories there are usually physical barriers between people and robots. Originally, this was for safety—industrial robots were unwieldy and unyielding. Although robots are increasingly designed to safely share human workspaces, even in these settings, people do one set of jobs and robots do another.
“Imagine if robots could be truly collaborative partners, able to anticipate and adapt to the needs of their human teammates. Such robots could greatly extend productivity. That possibility is really exciting to me.
“Human interaction isn’t part of the traditional curriculum for training roboticists. Our field is always pushing to make our systems more autonomous, and have richer capabilities and intelligence, but in that push we tend to look past the fact that these systems are, and always will be, working in human contexts.
“My lab is now focused on how to create robots that make flexible plans and reconsider their best next action based on changing conditions. It’s a challenging problem, because it’s so hard to model people—to know exactly what they’ll do and when. You also have a computational challenge, because the robot needs to reason on all these possible futures so quickly (the way humans naturally do so well). And you need to make the robot interact in a way that a person will accept. But experiments show that when people work with the adaptive robots we have designed, they can complete their task faster, with less idle time, and they even feel safer and more comfortable.
“The interesting thing about this is that there’s evidence to suggest the techniques can translate to better human-machine teamwork in almost any setting—from manufacturing to operating rooms to military applications. I think the insights will apply very broadly. After all, good teamwork is good teamwork.”
—as told to Will Knight
Watch this Innovator at EmTech 2014
Machines Like Us: Robots and Drones at Work
Hear more about robotics at EmTech MIT 2017.