Manipulation skills acquisition through state classification and dimension decrease

The paper carried out to explore the feasibility of reconstructing human constrained motion manipulation skills is reported. This is achieved by tracing and learning the manipulation performed by a human operator in a haptic rendered virtual environment. The peg-in-hole insertion problem is used as a case study. In the developed system, force and position variables 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 data obtained from the virtual environment is classified into different cluster sets using fuzzy Gustafson-Kessel model (FGK). Principal component analysis (PCA) is applied to each cluster to reduce the dimension of the data. The clusters in the optimum cluster set are tuned using locally weighted regression (LWR) to produce prediction models for robot trajectory performing the physical assembly based on the force/position information received from the rig