# Construction of Human Behavior Cognitive Map for Robots
## 1 Introduction  
With the rapid development of robots nowadays, service robots are becoming increasingly popular in ageing societies. It is necessary that such robots be capable of gathering information about their external environment. However, most robots are only able to consider physical information, such as images captured from cameras or depth information provided by a laser rangefinder. Given the wide variety of objects encountered in the environment, it is impossible to collect all possible appearances of objects to train robots to recognize them, regardless of whether the data are in the form of laser points or images. Therefore, it is necessary to develop a solution for this problem. To extract extra information in the environment model, there are two principal methods: the semantic map and cognitive map. In the semantic map, semantic labels are recorded for the object in the environment so that the map can provide a semantic level of understanding of the environment. [PERSON] and [PERSON] [1] defined this as \"a map that contains, in addition to spatial information about the environment, assignments of mapped features to entities of known classes. Further knowledge about these entities, independent of the map contents, is available for reasoning in some knowledge base with an associated reasoning engine\". The semantic map focuses on tagging an object or area so that robots can understand the meaning of a statement and perform the appropriate action [2; 3; 4]. A cognitive map is a mentality model of a person to explain the mechanism of recording information from an environment. The cognitive map is constructed by the abstract information, such as landmarks, path and nodes [5]. Humans learn about an object and its function by imitating the interactions of other people with the object. Like animals can learn new behavior by observing other animals [6], people can gain benefit from imitating other people, and so do robots [7; 8; 9]. By learning the behavior that happens in a specific position, robots can understand the use of space. Therefore, recording human behavior in a specific position is the primary idea involved in creating a cognitive map. In biology, a cognitive map is regarded as a cognitive representation of a specific location in space and is constructed from place cells, which are found in the hippocampus [10; 11]. The activity of place cells in the cognitive map is associated with a specific location in the environment and the corresponding location is called the place field. Place cells provide the location in the environment in the same way as the global positioning system (GPS). The activity of place cells will be reset, called remapping, as seen in studies on rats in another environment. This explains the plasticity of the hippocampus and the rats' ability to adapt. With the development of the brain, grid cells have been found in the entorhinal cortex [12]. The activity of a grid cell is associated not only with a specific location but with several locations in the environment. Grid cells provide an internal coordinate system for navigation. The cognitive map is formed of place cells and grid cells such that it memorizes and understands the use of space. A lot of research that adapts the cognitive map to robot applications is focused on creating a mathematical model for navigation or building a map for navigation because the cognitive map tries to classify the mechanism of navigation and memory for the use of space [13; 14]. There are some research works [15; 16] that associate the use of space with behavior. However, the behavior of moving still remains unsolved, such as busy walking, idle walking, wandering and stopping [17]. However, the cognitive map is rarely recording the low-level human behavior in the map such as standing, sitting, picking up and object, waving a hand, etc. In [18], the authors proposed a behavior-based map which is divided into many sub-regions. For each region, all human movement directions are described as transition probabilities.