Evolving Hidden Markov Model based Human Intention Learning and Inference
Refereed conference paper presented and published in conference proceedings

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AbstractTo 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.
All Author(s) ListLiu TT, Wang JL, Meng MQH
Name of ConferenceIEEE International Conference on Robotics and Biomimetics (ROBIO)
Start Date of Conference06/12/2015
End Date of Conference09/12/2015
Place of ConferenceZhuhai
Country/Region of ConferenceChina
Detailed descriptionorganized by IEEE,
Pages206 - 211
LanguagesEnglish-United Kingdom
KeywordsHidden Markov model; Human intention learning; Human intention recognition; Human robot cooperation
Web of Science Subject CategoriesRobotics

Last updated on 2021-12-09 at 00:28