In robotics, there has been a growing interest in expressing actions as a combination of meaningful subparts commonly called motion primitives. Primitives are analogous to words in a language. Similar to words put together according to the rules of language in a sentence, primitives
arranged with certain rules make an action. In this paper we investigate modeling and recognition of arm manipulation actions at different levels of complexity using primitives. Primitives are detected automatically in a sequential manner. Here, we assume no prior knowledge on primitives,
but look for correlating segments across various sequences. All actions are then modeled within a single hidden Markov models whose structure is learned incrementally as new data is observed. We also generate an action grammar based on these primitives and thus link signals to symbols.
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Document Type: Research Article
Copenhagen Institute of Technology, Computer Vision and Machine Intelligence Lab, Latrupvang 15, 2750 Ballerup, Denmark;, Email: [email protected]
Copenhagen Institute of Technology, Computer Vision and Machine Intelligence Lab, Latrupvang 15, 2750 Ballerup, Denmark
Royal Institute of Technology, Computational Vision and Active Perception Lab, Centre for Autonomous Systems, 100 44 Stockholm, Sweden
Publication date: 01 January 2011