Grasp recognition by time-clustering, fuzzy modeling, and Hidden Markov Models (HMM) - a comparative study

Abstract

This paper deals with three different methods for grasp recognition for a human hand. Grasp recognition is a major part of the approach for programming-by-demonstration (PbD) for five-fingered robotic hands. A human operator instructs the robot to perform different grasps wearing a data glove. For a number of human grasps, the finger joint angle trajectories are recorded and modeled by fuzzy clustering and Takagi-Sugeno modeling. This leads to grasp models using the time as input parameter and the joint angles as outputs. Given a test grasp by the human operator the robot classifies and recognizes the grasp and generates the corresponding robot grasp. Three methods for grasp recognition are presented and compared. In the first method the test grasp is compared with model grasps using the difference between the model outputs. In the second one, qualitative fuzzy models are used for recognition and classification. The third method is based on hidden-Markov-models (HMM) which are commonly used in robot learning.