In the basement of the Gates Computer Science Building at Stanford University, a screen attached to a red robotic arm lights up. A pair of cartoon eyes blinks. “Meet Bender,” says Ajay Mandlekar, PhD student in electrical engineering.
Bender is one of the robot arms that a team of Stanford researchers is using to test two frameworks that, together, could make it faster and easier to teach robots basic skills. The RoboTurk framework allows people to direct the robot arms in real time with a smartphone and a browser by showing the robot how to carry out tasks like picking up objects. SURREAL speeds the learning process by running multiple experiences at once, essentially allowing the robots to learn from many experiences simultaneously.
RoboTurk and SURREAL team member Linxi Fan is a 2016 Stanford Graduate Fellow.