Inside a brightly decorated lab at the University of California, Berkeley, an ordinary-looking robot has developed an exceptional knack for picking up awkward and unusual objects. What’s stunning, though, is that the robot got so good at grasping by working with virtual objects.

The robot learned what kind of grip should work for different items by studying a vast data set of 3-D shapes and suitable grasps. The UC Berkeley researchers fed images to a large deep-learning neural network connected to an off-the-shelf 3-D sensor and a standard robot arm. When a new object is placed in front of it, the robot’s deep-learning system quickly figures out what grasp the arm should use.

The bot is significantly better than anything developed previously. In tests, when it was more than 50 percent confident it could grasp an object, it succeeded in lifting the item and shaking it without dropping the object 98 percent of the time. When the robot was unsure, it would poke the object in order to figure out a better grasp. After doing that it was successful at lifting it 99 percent of the time. This is a significant step up from previous methods, the researchers say.