In tomorrow's world, when robots replace humans in many activities, how will robots learn?
Self-learning may be the answer:
A brief extract:
Robots master skills with ‘deep learning’ technique
UC Berkeley researchers' new algorithms enable robots to learn motor tasks by trial and error
May 22, 2015
Robot learns to use hammer. What could go wrong? (credit: UC Berkeley)
UC Berkeley researchers have developed new algorithms that enable robots to learn motor tasks by trial and error, using a process that more closely approximates the way humans learn.
They demonstrated their technique, a type of reinforcement learning, by having a robot complete various tasks — putting a clothes hanger on a rack, assembling a toy plane, screwing a cap on a water bottle, and more — without pre-programmed details about its surroundings.
A new AI approach
“What we’re reporting on here is a new approach to empowering a robot to learn,” said Professor Pieter Abbeel of UC Berkeley’s Department of Electrical Engineering and Computer Sciences. “The key is that when a robot is faced with something new, we won’t have to reprogram it. The exact same software, which encodes how the robot can learn, was used to allow the robot to learn all the different tasks we gave it.”
The work is part of a new People and Robots Initiative at UC’s Center for Information Technology Research in the Interest of Society (CITRIS). The new multi-campus, multidisciplinary research initiative seeks to keep the advances in artificial intelligence, robotics and automation aligned to human needs.
“Most robotic applications are in controlled environments where objects are in predictable positions,” said UC Berkeley faculty member Trevor Darrell, director of the Berkeley Vision and Learning Center. “The challenge of putting robots into real-life settings, like homes or offices, is that those environments are constantly changing. The robot must be able to perceive and adapt to its surroundings.”