A new algorithm helps robots decide which gripper works best for picking up different objects.
From spoons to stuffed animals, humans learn early in life how to pick up objects that have a variety of shapes, textures and sizes.
A new machine-learning algorithm developed by engineers at UC Berkeley can teach robots to grasp and carry items with similar dexterity.
The algorithm helps “ambidextrous” robots equipped with different types of grippers — for example, a suction gripper and a parallel-jaw gripper — decide which gripper to use for any given object.
“Any single gripper cannot handle all objects,” said Jeff Mahler, a postdoctoral researcher at UC Berkeley and lead author of a new paper describing the advance, published this week in Science Robotics.
“For example, a suction cup cannot create a seal on porous objects such as clothing, and parallel-jaw grippers may not be able to reach both sides of some tools and toys. ‘Ambidextrous’ robots offer greater diversity.”
The technology could be especially useful in fulfillment centers for e-commerce companies like Amazon, which rely on robots for packaging.
“When you are in a warehouse putting together packages for delivery, objects vary considerably,” said Ken Goldberg, a UC Berkeley professor with joint appointments in the Department of Electrical Engineering and Computer Sciences and the Department of Industrial Engineering and Operations Research.
“We need a variety of grippers to handle a variety of objects.”
Source: UC Berkeley.