'New method helps robots to learn tasks from people'

Scientists, including those of Indian origin, are testing two frameworks that could make it faster and easier to teach robot arms basic skills such as picking up objects.

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, said Ajay Mandlekar, a PhD student at Stanford University in the US.

Another framework called 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," Mandlekar said.

Robots typically learn by interacting with and exploring their environment -- which usually results in lots of random arm waving -- or from large datasets.

Neither of these is as efficient as getting some human help. In the same way that parents teach their children to brush their teeth by guiding their hands, people can demonstrate to robots how to do specific tasks, researchers said.

"Humans are by no means optimal at this, but this experience is still integral for the robots," Mandlekar said.

These trials -- even the failures -- provide invaluable information.

The demonstrations collected through RoboTurk will give the robots background knowledge to kickstart their learning. SURREAL can run thousands of simulated experiences by people worldwide at once to speed the learning process.

"With SURREAL, we want to accelerate this process of interacting with the environment," said Linxi Fan, a PhD student in computer science and a member of the Stanford team.

These frameworks drastically increase the amount of data for the robots to learn from.

"The twin frameworks combined can provide a mechanism for AI-assisted human performance of tasks where we can bring humans away from dangerous environments while still retaining a similar level of task execution proficiency," said postdoctoral fellow Animesh Garg, a member of the Stanford team that developed the frameworks.

The team envisions that robots will be an integral part of everyday life in the future: helping with household chores, performing repetitive assembly tasks in manufacturing or completing dangerous tasks that may pose a threat to humans.