Grasp Planning Based on Grasp Strategy Extraction from Demonstration

In this paper, we discuss information that is beneficial to robotic grasp planning and can be extracted from human demonstration. We present a method that integrates grasp intention: grasp type, and the relative thumb positions and orientations on the grasped object to the force-closure-based grasp planning procedure. Instead of completely mimicking the human grasp, grasp type and the relative thumb position are partially extracted from the demonstration to represent the task properties and grasp strategies, and avoid the challenging kinematic correspondence problem. Instead of mapping the demonstrated motion, the grasp type and thumb position provide meaningful constraints on hand posture and wrist position. Both the feasible workspace of a robotic hand and the search space of grasp planning are thereby highly reduced by the constraints. This approach has been evaluated in a simulation with a Barrett hand and a Shadow hand on eight daily objects