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.

Fortifying the future of cryptography

As a boy growing up in a small South Indian village, Vinod Vaikuntanathan taught himself calculus by reading books his grandfather left lying around the house. Years later in college, he toiled away in the library studying number theory, which deals with the properties and relationships of numbers, primarily positive integers. This field of study naturally steered Vaikuntanathan toward what he calls “the most important application of number theory in the modern world”: cryptography. Today, Vaikuntanathan, a recently tenured associate professor of electrical engineering and computer science at MIT, is using number theory and other mathematical concepts to fortify encryption so it can be used for new applications and stand up to even the toughest adversaries. One major focus is developing more efficient encryption techniques that can be scaled to do complex computations on large datasets. That means multiple parties can share data while ensuring the data remains private. For Continue reading Fortifying the future of cryptography