We had been collaborating with Everyday Robots for, I want to say, seven years already. Even though we were two separate teams, we have very, very deep connections. In fact, one of the things that prompted us to really start looking into robotics at the time was a collaboration that was a bit of a skunkworks project with the Everyday Robots team, where they happened to have a number of robot arms lying around that had been discontinued. They were one generation of arms that had led to a new generation, and they were just lying around, doing nothing.
We decided it would be fun to pick up those arms, put them all in a room and have them practice and learn how to grasp objects. The very notion of learning a grasping problem was not in the zeitgeist at the time. The idea of using machine learning and perception as the way to control robotic grasping was not something that had been explored. When the arms succeeded, we gave them a reward, and when they failed, we give them a thumbs-down.
For the first time, we used machine learning and essentially solved this problem of generalized grasping, using machine learning and AI. That was a lightbulb moment at the time. There really was something new there. That triggered both the investigations with Everyday Robots around focusing on machine learning as a way to control those robots. And also, on the research side, pushing a lot more robotics as an interesting problem to apply all of the deep learning AI techniques that we’ve been able to work so well into other areas.