Quantitative evaluation for skill controller based on comparison with human demonstration

One of the promising strategies to design a skill controller for robots is to observe the human worker´s skill and embed it in the robot controller under certain control architecture. However, no systematic design strategies to realize this scenario have yet been developed due to the lack of a quantitative performance evaluation of the skill controller. In this brief, the switching-impedance controller is considered as the skill controller and is developed based on a comparison with human worker´s demonstration. The enabling condition to switch the impedance parameter is optimized by calculating a hidden Markov model (HMM) distance which can measure the similarity between the skill of the human worker and the robot. HMM is a doubly stochastic system and is recognized as a useful tool for speech recognition. Thanks to the similarity in the stochastic characteristics between speech and skill (position/force) data, HMM is also expected to play a crucial role in skill controller design. An insertion task of deformable objects with the assistance of a vision sensor is considered in this brief. Some parameters which appear in the skill controller are optimized so as to increase the similarity with human worker´s demonstration.