Learning ambidextrous robot grasping policies
An ambidextrous grasping policy trained on synthetic datasets consistently clears bins of up to 25 novel objects with a physical robot. Universal picking (UP) or reliable robot grasping of a diverse range of novel objects from heaps, is a grand challenge for e commerce order fulfillment, manufacturing, inspection, and home service robots. Optimizing the rate, reliability, and range of UP is difficult due to inherent uncertainty in sensing, control, and contact physics. This paper explores "ambidextrous" robot grasping, where two or more heterogeneous grippers are used. We present Dexterity Network (Dex Net) 4.0, a substantial extension to previous versions of Dex Net that learns policies for a given set of grippers by training on synthetic datasets using domain randomization with analytic models of physics and geometry. We train policies for a parallel jaw and a vacuum based suction cup gripper on 5 million synthetic depth images, grasps, and rewards generated from heaps of three dimensional objects. On a physical robot with two grippers, the Dex Net 4.0 policy consistently clears bins of up to 25 novel objects with reliability greater than 95% at a rate of more than 300 mean picks per hour.
Science Robotics