Stochastic Learning Manipulation of Object Pose With Under-Actuated Impulse Generator Arrays

Robotic assembly systems are common in modern industry and a fixture of commerce. However, the robots themselves lack the adaptability of humans in terms of singulating and grasping parts with uncontrolled pose. To this end, vibratory bowl feeder (VBF) devices are often employed to pre-orient the part for robot grasping. Unfortunately, VBFs themselves are inflexible (usually bespoken for one specific part), noisy, and very expensive to design and tune. We consider an alternative to the VBF - an array of impulse-generating solenoids positioned under a semi-rigid part-carrying platform that uses computer vision and self-supervised machine learning to generate a policy implementing a closed-loop controller to orient randomly positioned parts into a pose acceptable for robot grasping. Using a flat square wooden nut from a child's assembly toy as a test object, we were able to flip the nut into the desired orientation (standing vertically on the narrow edge) 21.1% of the time with a single impulse, and 35.4% of the time with two impulses, versus just 10.2% and 19.2% (respectively) of the time for a baseline policy of random choice of solenoid position and impulse duration, thus demonstrating black-box control of a process commonly considered too difficult to physically model.