In the basement of the Gates Computer Science Building at Stanford University, a screen attached to a red robotic arm lights up.
"Meet Bender," says Ajay Mandlekar, PhD student in electrical engineering.
SURREAL speeds the learning process by running multiple experiences at once, essentially allowing the robots to learn from many experiences simultaneously.
"With RoboTurk and SURREAL, we can push the boundary of what robots can do by combining lots of data collected by humans and coupling that with large-scale reinforcement learning," said Mandlekar, a member of the team that developed the frameworks.
Yuke Zhu, a PhD student in computer science and a member of the team, showed how the system works by opening the app on his iPhone and waving it through the air.
He guided the robot arm - like a mechanical crane in an arcade game - to hover over his prize: a wooden block painted to look like a steak.