Despite all the recent advances in robotics, one fundamental task appears to remain as hard as ever: robot programming.
To be sure, robot programming in industrial settings has evolved significantly, from a series of mechanical switches to advanced programming languages and teach-pendant devices for trajectory planning. But getting robots to do their jobs still requires a great deal of human labor -- and human intelligence.
The situation is even worse when it comes to programming robots to do things in non-industrial environments. Homes, offices, and hospitals are unstructured spaces, where robots need to deal with more uncertainty and act more safely.
To overcome this programming bottleneck, engineers need to create robots that are more flexible and adaptable -- robots that, like humans, learn by doing.
That's what a team led by Dr. Jan Peters at the Robot Learning Lab, part of the Max-Planck Institute for Biological Cybernetics, in Tübingen, Germany, is trying to do. Peters wants to transform robot programming into robot learning. In other words, he wants to design robots that can learn tasks effortlessly instead of requiring people to painstakingly determine their every move.
See my full article about robots learning to play ping pong on IEEE Spectrum Automaton. See the video that explains the approach below: