Application of Competitive Clustering to Acquisition of Human Manipulation Skills Acquisition

The work 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, 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 data obtained from the virtual environment is classified into different cluster sets using a competitive fuzzy clustering algorithm called Competitive Agglomeration (CA). The CA algorithm starts with an over specified number of clusters which compete for feature points in the training procedure. Clusters with small cardinalities lose the competition and gradually vanish. The optimal number of clusters that win the competition is eventually determined. 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. A background on the work and its significance is provided. The approach developed is explained and the results obtained so far are presented.