Robots can (usually) walk or roll around just fine, but hand control has often proven elusive: it's hard to give them the same kind of finger dexterity as a human. However, University of Washington researchers just got much closer to fulfilling that dream. They've built a robot hand that is not only dextrous enough to spin objects (such as the tube of coffee beans you see above), but learns how to do this on its own. Its algorithm gradually discovers what works and what doesn't -- give it enough time and it'll go from clumsy to reasonably skilled.

Pianists, surgeons, typists, gamers and baton-twirlers all learn to use their hands more skillfully as they ply their trade, but what about robots? Researchers at the University of Washington say they've developed a five-fingered robot hand that's more capable than ours, and can learn to handle objects better and better without human intervention. The ADROIT Manipulation Platform draws upon machine learning and real-world feedback to improve its performance, rather than relying on its programmers to specify its every move. "Such dynamic dexterous manipulation with free objects has never been demonstrated before even in simulation, let along the physical hardware results we have," Vikash Kumar, a UW doctoral student in computer science and engineering, told GeekWire in an email. Kumar and his colleagues discuss the project in a paper to be presented May 17 at the IEEE International Conference on Robotics and Automation.