# Learning and Development of Home Service Robots' Service Cognition Based on a Learning Mechanism
## 1 Introduction  
With the improvement of living standards, people hope that robots will have enough intelligence to actively and correctly identify the services that users need according to current user status and context information. The development of IoT technology makes it easier for robots to access context information [1, 2], including the on/off and open/closed status of devices and furniture. At present, the main methods oriented toward the service cognition of home service robots are knowledge-based, learning-based, and \"genetic search\" approaches. Although these methods have achieved remarkable success in their fields, they have many limitations in robot service cognition. Robots can neither respond flexibly according to a complex and unstructured context using historical experience nor store existing experience in their \"brains\" like humans. A home service robot's ability to learn from its existing experience and develop its \"mind\" like a human is key for the realization of real intelligence in robots. In recent years, many studies have been carried out on the topic of autonomic service cognition in home service robots. [PERSON] et al. [3] proposed the management of information in a smart home using ontology technology. From the constructed ontology model, the autonomous cognition of service tasks can be realized by using the JSHOP2 planner and inference rule base to build the service task. During service cognition, the user's emotions should also be considered. On the basis of the ontology model in [3], [PERSON] et al. [4] proposed a user emotion-based service recognition system in which the user's emotion serves as reward feedback to dynamically adjust the service. Soar, a cognitive service robot presented by [PERSON] et al. [5], constructs a healthcare table as well as a basic profile of each family member and provides the service with the maximum utility value. Aiming to solve the problem of robotic services provided to multiple users at the same time, [PERSON] et al. [6] proposed a scheduling policy to provide the service to multiple users according to a service queue, which is generated according to user intentions. However, the service cognition methods mentioned above are all based on specific tasks, and the developer needs to rebuild and train the model if the task or model changes. This process results in robots that have low intelligence and universality. Robots can be truly intelligent only if they have the ability to learn by themselves and develop their \"minds\" autonomously. Reinforcement learning lies in self-learning, and it has been widely used in the field of robotics. In order to provide users with more suitable services, [PERSON] et al. [7] proposed a network-based recommendation robot that could learn users' habits by interacting with them through reinforcement learning, thereby providing personalized services. Focusing on multi-robot systems, [PERSON] et al. [8] proposed a multi-robot task allocation algorithm for fire-disaster response based on reinforcement learning and achieved distributed task allocation for multi-robot systems. To address the problem of indoor localization, [PERSON] et al. [9] proposed a model that could closely estimate target locations using deep reinforcement learning (DRL) in IoT and smart city scenarios. [PERSON] et al. [10] used reinforcement learning to design an energy-preserving Media Access Control (MAC) protocol, thereby realizing high-density communication in wireless sensor networks (WSN). They established that the application of reinforcement learning in a smart home is technically feasible, which provides the basis for this article. In the field of robotics, reinforcement learning is often used in navigation and path planning [11; 12; 13]. Therefore, we focused on applying reinforcement learning to the service cognition of robots in the home environment to provide robots with the ability to learn the skill of service cognition by themselves.