Dell Technologies Realize magazine Issue 3 | Page 14

TRENDS 12 plate and slice the pizza; then deposit a slice on the plate. This series of actions requires grasps of hard, soft, and even floppy objects, hot and cold objects, as well as liquids and solids. While humans instinctively understand how to grasp any object— even one we’ve never seen before—robots have to be taught this skill: The robot has to perceive the object with its sensors, model it appropriately, determine a strategy for picking it up, then execute the desired action. The extensive training required to teach commercial robots these skills is expensive. That’s where AUTOLAB’s work comes in. LEARNING THROUGH FAILURE Goldberg, Mahler, and their post-graduate students began working on AUTOLAB’s Dexterity Network (Dex-Net) in 2015. The groundbreaking venture develops and refines robot “picking” strategies and, just as importantly, has improved the machine learning behind the picking calculations. “Dex-Net can be used to train a robotic system for handling a variety of items without advance knowledge,” says Mahler, citing CAD models, mass, or images as examples. “One of the advantages is that it can be rapidly adapted to different hardware systems consisting of various arms, grippers, and 3D depth cameras, enabling faster customization of robotic learning systems.” The first iteration of Dex-Net entailed a system for grasping one object at a time with parallel jaws—think two fingers or pliers. Their current work—Dex-Net 4.0—trains robots to grasp a wider variety of objects piled in heaps that make picking more challenging. Dex-Net 4.0 includes both the parallel-jaw gripper and a pneumatic suction arm—each with its own neural network. The robot’s central programming provides size and shape information via sensors, but the two arms’ separate neural networks decide whether an object should be handled by grip or suction. While AUTOLAB researchers applaud their advances, what really interests them is the failures: the objects the robot couldn’t pick up or hold on to. “Part of the AUTOLAB philosophy is to probe for failure modes that provide deeper insight into a method,” Mahler says. “That’s where the adversarial objects came from. The results behind Dex-Net were only possible with countless hours of meticulous experimentation and healthy skepticism.” AUTOLAB has designed and created thousands of adversarial objects—some as virtual simulations and many others 3D-printed. Some objects look like familiar shapes, but with a peculiar twist—like a cube where part of one surface has been shaved, creating a new plane that easily slips out of the grippers of a cube-picking robot. Others seem surfaced from a nightmare: melted, twisted fivelegged objects. The physical objects are small— around 10cm—as they’re meant to thwart a robot with 5cm grippers, Mahler says. AUTOLAB’s robot uses its suction arm to pick up scissors. QUICKER PICKING Perhaps AUTOLAB’s biggest breakthrough is that Goldberg, Mahler, and their adversarial objects