Kinematic Synergy Primitives for Human-Like Grasp Motion Generation

Grasping with five-fingered humanoid hands is a complex control problem. Throughout the entire grasping motion, all finger joints need to be coordinated to achieve a stable grasp. Grasp synergies provide a simplified, low-dimensional representation of grasp postures and motions, that can be used for the description of human grasps as well as the generation of novel, human-like grasps. However, the abstract synergy representation complicates the association of relevant high-level grasp parameters, as for example the grasp type and final posture or the grasp speed. Therefore, it is difficult to control these grasp characteristics in the synergy space. This paper presents an adaptable representation for kinematic grasping motions in synergy space, that allows the generation of novel, human-like grasps under direct control of high-level grasp parameters. It is based on via-point movement primitives trained on synergy trajectories of human grasping motions. The representation using synergy primitives allows for a straightforward adaptation of grasp characteristics while preserving the essential grasping motion learned from human demonstration. The kinematic synergy primitives have a low reproduction error of 3.9% of the maximum finger joint angle and are able to generate successful grasps on a simulated human hand and a real prosthetic hand.