Abstract: This work presents a novel and efficient non-linear programming framework that tightly integrates hierarchical decision-making with inverse kinematic planning and control. Decision-making plays a central role in many aspects of robotics, from sparse inverse kinematic control with a minimal number of joints, to inverse kinematic planning while simultaneously selecting a discrete end-effector location from multiple candidates. Current approaches often rely on heavy computations using mixed-integer non-linear programming, separate decision-making from inverse kinematics (some times approximated by reachability methods), or employ efficient but less accurate \ell₁-norm formulations of linear sparse programming, without addressing the underlying non-linear problem formulations. In contrast, the proposed sparse hierarchical non-linear programming solver is efficient, versatile, and accurate by exploiting sparse hierarchical structure and leveraging the rarely used \ell₀-norm in robotics. The solver efficiently addresses complex non-linear hierarchical decision-making problems, such as inverse kinematic planning with simultaneous prioritized selection of end-effector locations from a large set of candidates, or inverse kinematic control with simultaneous selection of bi-manual grasp locations on a randomly rotated box.