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

香港中文大學研究人員

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摘要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.
著者Liu TT, Wang JL, Meng MQH
會議名稱IEEE International Conference on Robotics and Biomimetics (ROBIO)
會議開始日06.12.2015
會議完結日09.12.2015
會議地點Zhuhai
會議國家/地區中國
詳細描述organized by IEEE,
出版年份2015
月份1
日期1
出版社IEEE
頁次206 - 211
電子國際標準書號978-1-4673-9675-2
語言英式英語
關鍵詞Hidden Markov model; Human intention learning; Human intention recognition; Human robot cooperation
Web of Science 學科類別Robotics

上次更新時間 2021-18-10 於 00:38