Integrated Hierarchical Decision-Making in Inverse Kinematic Planning and Control
For robotics researchers, this provides a more efficient and versatile method for inverse kinematic problems involving discrete decisions, addressing a key bottleneck in current mixed-integer approaches.
This work presents a novel nonlinear programming framework that integrates hierarchical decision-making with whole-body inverse kinematic planning and control, enabling efficient selection of end-effector locations from many candidates. The solver achieves up to 100x speedup over mixed-integer approaches while maintaining accuracy.
This work presents a novel and efficient nonlinear programming framework that tightly integrates hierarchical decision-making with whole-body 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 nonlinear programming, separate decision-making from inverse kinematics (some times approximated by reachability methods), or employ efficient but less versatile $\ell_1$-norm formulations of linear sparse programming, without addressing the underlying nonlinear problem formulations. In contrast, the proposed sparse hierarchical nonlinear programming solver is efficient, versatile, and accurate by exploiting sparse hierarchical structure and leveraging the $\ell_0$-norm which is rarely used in robotics. The solver efficiently tackles complex nonlinear hierarchical decision-making problems previously unaddressed in the literature, 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.