## 1 Introduction  
Cloud computing is a widely advancing information and communications technology service and is a key technology of the advanced industry. Robot clouds applying cloud computing to robots allows robots to connect to a cloud environment, uses a huge computational infrastructure, and obtains the results of high level programs from the cloud [1]. The cloud robots share information including environments, actions, and objects and offload heavy computation to a cloud server [2].  
However, such cloud robot services could give rise to security issues of privacy breaches and latency issues of control signal delays for robot motions. Recently, to solve these issues, fog robotics, distributing computing work properly with fog servers and edges, is receiving attention for its advantages in reducing latency and security matters (Figure 1) [3; 4; 5].  
These merits of fog robotics can accord with cognitive robots to reduce the cost of the robot and its human-robot interaction (HRI) services. If the cognitive robot adopts the fog robotics model for offloading burdened computing tasks to clouds or fog servers, it also needs to consider privacy, security, and latency as well as abundant computing power for advanced intelligent functions. Cognitive robots represent experienced cognitive information, store it in a proper form, and retrieve it using a reasoning procedure [6; 7]. This means that the fog robotics model of cognitive robots needs to consider the characteristics of the cognitive structure [8].  
In this paper, a function as a service based fog robotics (FaaS-FR) model for the sentential cognitive system (SCS) of cognitive robots is proposed. The FaaS-FR model includes the edge as the local robot system, the fog robot servers for private, security, and computing power, and the robot clouds for high performance computation. The previous SCS consists of multiple modules to recognize new events that the robot has experienced and describes them in a sentential form to be stored in a sentential memory and retrieved with a reasoning process in the future [9]. In this approach, the SCS adoptsFaaS-FR, and each module of the SCS is classified with the functionality of privacy, security, and latency as well as required computing power. According to the functionality, the computation of modules is executed on the edge or offloaded to fog robot servers or robot clouds.  
The merit of FaaS-FR is that advanced services utilizing high performance computation is possible even in the edge system of low cost and low computing capability. A module in the SCS acquires and transfers raw data to the fog sever or a cloud. Then, the server processes the data and sends back the results to the SCS. In the implementation and test, we can observe that the FaaS-FR model makes cognitive robots more efficient via proper distribution of the computing power and information sharing.  
The contributions of this study are as follows: (1) a fog robotics model, FaaS-FR model, is suggested to be applied to a cognitive robot for efficient and advance services at a low cost; (2) with this model, a functionality-based modular networking in the SCS of a robot is proposed and tested.  
This paper is organized as follows. In Section 2, related work on robot cloud and fog robotics are described. Section 3 details the theoretical background of the proposed FaaS-FR model. Section 4 describes an application of the model to a SCS for a cognitive robot platform. Section 5 provides the implementation of the proposed approach to a service robot through experimental results. Finally, conclusions and future work are presented in Section 6.