Application of hidden Markov model to acquisition of manipulation skills from haptic rendered virtual environment

This paper explores the feasibility of reconstructing human manipulation skills in complex constrained motion by tracing and learning the manipulation performed by the operator. The peg-in-hole insertion problem is used as a case study, which represents a typical constrained motion force sensitive manufacturing task with the attendant issues of jamming, tight clearance and the need for quick assembly times. In the developed system, position and contact force and torque as well as orientation data generated in the haptic rendered virtual environment combined with a priori knowledge about the task are used to identify and learn the skills in the newly demonstrated task. The recorded training data is classified into contact states, which are identified with hidden Markov model (HMM) as human skills. The HMM parameters are obtained from the training data. By evaluating the controller's performance in each contact state from haptic rendered virtual environment, the robot develops the best trajectories to imitate the human behaviour. In this paper the significance of this research project is highlighted and the developed approach and the progress made so far on this project are reported.