Multifingered Grasp Planning Based on Gaussian Process Implicit Surface and its Partial Differentials

Abstract

Grasp planning for irregularly shaped objects using multifingered robotic hands is challenging due to the high dimensionality of the search space and a lack of proper modeling methods for object geometry. To address these issues, we propose a grasp planning approach based on Gaussian process implicit surfaces (GPIS). To explore the object geometry and identify feasible contact positions and normals, our method introduces several moving points called attractors along with a dynamical system. The dynamical system constrains and guides the attractors with the partial differentials of the GPIS, which can be conveniently obtained through the linear expression of a GP. The hand motion is also guided by the dynamical system. In addition, an inverse kinematics method, which considers finger joint limits, is developed to simultaneously adjust the palm pose and finger joint angles for a feasible grasp. The performance of our method is demonstrated using various robotic hands and objects, and real robot experiments are conducted to validate the planned grasp's effectiveness in reality. Experimental evaluation demonstrates that the method works for different robotic hands and objects of varying shapes, with a higher likelihood of generating grasps with better quality.