Evolving hidden Markov model based human intention learning and inference

Abstract

To effectively facilitate human robot cooperation, human intention should be recognized by robot accurately and effectively. Teaching the robot human intentions in advance could be well suitable for a static environment with limited tasks. Nevertheless, in an dynamic environment that requires task update, the pre-teaching approach cannot satisfy the evolving knowledge of human intention. The unknown human intentions which have not been taught in advance, will not be understood by robot. This problem limits the human robot cooperation in a real dynamic environment. In this paper, we proposed a human intention learning and inference method to improve the intuitive cooperative capability of the robot. An evolving hidden Markov model (EHMM) approach has been developed to learn and infer human intentions according to the observation. Assembly tasks with ten different configurations have been designed and simulation experiments were carried out. Four assembly configurations have been used for known human intention recognition experiment and six configurations have been used for unknown human intention learning and inference experiment. The accurate and robust results obtained from the experiments have shown the feasibility of the proposed EHMM for human intention learning and inference.