ROBOT LEARNS VIA TRY ANS ERROR LIKE A HUMAN.

Researchers at the University of California Berkeley have developed algorithms that allow robots to learn by making mistakes -- just like a real person.

Robot "brains" are very different from human brains, and teaching robots how to do tasks is usually just a matter of writing the right code. It may sound simpler than the way humans learn, but it's actually very difficult -- without human intuition, things like flexibility and adapting to changing circumstances become nigh impossible.
A team of researchers at UC Berkeley have today demonstrated a robot that can learn via trial and error, much like how humans learn. It constitutes a pretty big step forward in the field of artificial intelligence.

"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 team developed a series of algorithms that allowed a Willow Garage PR2 robot named BRETT to learn a series of motor tasks, such as screwing a cap on a water bottle, or assembling a toy aeroplane, without pre-programmed knowledge of its surroundings -- by allowing it to eliminate possibilities by making mistakes, or what we'd call "trial and error."

This could allow robots to operate more efficiently in environments that are a little more chaotic than the environments in which they usually operate, such as factories or laboratories.