Grasp mapping using locality preserving projections and kNN regression

In this paper, we propose a novel mapping approach to map a human grasp to a robotic grasp based on human grasp motion trajectories rather than grasp poses, since the grasp trajectories of a human grasp provide more information to disambiguate between different grasp types than grasp poses. Human grasp motions usually contain complex and nonlinear patterns in a high-dimensional space. In this paper, we reduced the high-dimensionality of motion trajectories by using locality preserving projections (LPP). Then, a Hausdorff distance was performed to find the k-nearest neighbor trajectories in the reduced low-dimensional subspace, and k-nearest neighbor (kNN) regression was used to map a demonstrated grasp motion by a human hand to a robotic hand. Several experiments were designed and carried out to compare the robotic grasping trajectory generated with and without the trajectory-based mapping approach. The regression errors of the mapping results show that our approach generates more robust grasps than using only grasp poses. In addition, our approach has the ability to successfully map a grasp motion of a new grasp demonstration that has not been trained before to a robotic hand.