Segmentation and Recognition of Human Grasps for Programming-by-Demonstration using Time-clustering and Fuzzy Modeling

Abstract

In this article we address the problem of programming by demonstration (PbD) of grasping tasks for a five-fingered robotic hand. The robot is instructed by a human operator wearing a data glove capturing the hand poses. For a number of human grasps, the corresponding fingertip trajectories are modeled in time and space by fuzzy clustering and Takagi-Sugeno modeling. This so-called time-clustering leads to grasp models using the time as input parameter and the fingertip positions as outputs. For a test sequence of grasps the control system of the robot hand identifies the grasp segments, classifies the grasps and generates the sequence of grasps shown before. For this purpose, each grasp is correlated with a training sequence. By means of a hybrid fuzzy model the demonstrated grasp sequence can be reconstructed.