  Future NASA lander missions to icy moons will require completely automated,
accurate, and data efficient calibration methods for the robot manipulator arms
that sample icy terrains in the lander's vicinity. To support this need, this
paper presents a Gaussian Process (GP) approach to the classical manipulator
kinematic calibration process. Instead of identifying a corrected set of
Denavit-Hartenberg kinematic parameters, a set of GPs models the residual
kinematic error of the arm over the workspace. More importantly, this modeling
framework allows a Gaussian Process Upper Confident Bound (GP-UCB) algorithm to
efficiently and adaptively select the calibration's measurement points so as to
minimize the number of experiments, and therefore minimize the time needed for
recalibration. The method is demonstrated in simulation on a simple 2-DOF arm,
a 6 DOF arm whose geometry is a candidate for a future NASA mission, and a 7
DOF Barrett WAM arm.
