A Choice of Grippers Helps Dual-Arm Robot Pick Up Objects Faster Than Ever

Dex-Net 4.0 enables “ambidextrous” robots to choose the best gripper for the job We’ve been following Dex-Net’s progress towards universal grasping for several years now, and today in a paper in Science Robotics, UC Berkeley is presenting Dex-Net 4.0. The new and exciting bit about this latest version of Dex-Net is that it’s able to successfully grasp 95 percent of unseen objects at a rate of 300 per hour, thanks to some added ambidexterity that lets the robot dynamically choose between two different kinds of grippers.