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.