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This paper presents a novel method for classifying regions from human movements in service robots' working environments. The entire space is segmented subject to the class type according to the functionality or affordance of each place which accommodates a typical human behavior. This is achieved based on a grid map in two steps. First a probabilistic model is developed to capture human movements for each grid cell by using a non-ergodic HMM. Then the learned transition probabilitiesdoi:10.1109/roman.2016.7745092 dblp:conf/ro-man/WangJF16 fatcat:zrm7spnkuveixjlq2kzrlip2pa