Robot uses machine-learning to grab objects on the first try

Training robots how to grasp various objects without dropping them usually requires a lot of practice. But a new robot, designed by researchers at UC Berkeley and Siemens and described in an upcoming paper, can learn how to grip new objects just by studying a database of 3D shapes. The robot is connected to a 3D sensor and a deep-learning neural network to which researchers fed images of objects. They included information about the objects' shapes, visual appearances and the physics of how to go about grabbing them. So, when a new object is placed in front of the robot, it just has to match it to a similar object in the database.