  For more than half a century, vibratory bowl feeders have been the standard
in automated assembly for singulation, orientation, and manipulation of small
parts. Unfortunately, these feeders are expensive, noisy, and highly
specialized on a single part design bases. We consider an alternative device
and learning control method for singulation, orientation, and manipulation by
means of seven fixed-position variable-energy solenoid impulse actuators
located beneath a semi-rigid part supporting surface. Using computer vision to
provide part pose information, we tested various machine learning (ML)
algorithms to generate a control policy that selects the optimal actuator and
actuation energy. Our manipulation test object is a 6-sided craps-style die.
Using the most suitable ML algorithm, we were able to flip the die to any
desired face 30.4\% of the time with a single impulse, and 51.3\% with two
chosen impulses, versus a random policy succeeding 5.1\% of the time (that is,
a randomly chosen impulse delivered by a randomly chosen solenoid).
