Intuitive human skill reconstruction for compliance control

This paper presents a robust and efficient method of generating manipulation motion skill for non-force-feedback high speed constrained compliant robot motion. Using a non-structured teaching environment, the inherent task in the captured demonstration force and position data is estimated and reconstructed from three sets of complimentary models, including analytical mathematical modelling, empirical modelling and human skill demonstration modelling. The approach addresses task specification accuracy deficiencies, and involves outward interface simplifications, with embedded rigorous analytical methodologies that enable users to realise complex and robust constrained compliant robot motion without dealing with the low level motion generation aspects. Function based task representation supports an intuitive approach to generate robust constrained motion by skill superimposition, as exemplified by peg-in-hole with crank turning.