# Ontology-Based Framework for Cooperative Learning of 3D Object Recognition
## 2 Related Work  
Many researchers have explored the possibility of adopting cloud infrastructure for robotic applications. See [PERSON] et al. for a survey on the rise of robotic systems supported by cloud computing and [PERSON] et al. for a more extensive survey [6; 7]. Robotic cloud support systems, in general, have been developed as instances of the \"platform as a service\" (Paas) and \"software as a service\" (Saas) traditional cloud service models. However, a new \"robot as a service\" (RaaS) model has also been introduced. For example, [PERSON] et al. [8] propose a RaaS system as a standard platform for common robotic services with a corresponding development process and execution platform. The system provides cloud infrastructure to schedule utility robotic services and give users access to robot resources at low cost [9]. As an example of robotic applications at the PaaS level, [PERSON] et al. [10] propose the cloud computing platform REALabs for providing cloud resources to networked robotics applications. The platform provides a Virtual Machine management service that allows users to provision and manage their own virtual machines as well as a collection of roboticservices, including access control and data security. This platform allows some knowledge sharing among robotic applications. At the SaaS level, [PERSON] et al. [11] present DAvinCi, a cloud-based framework for multiple service robots. They describe a parallel FastSLAM (Fast Simultaneous Localization and Mapping) system deployed on the Hadoop MapReduce API. Their work demonstrates the utility of sharing a centralized large area map among networked robots. Another example of SaaS is the SCRS, the Senior Companion Robot System [11]. The proponents propose a cloud-computing platform to provide remote robot control services for interaction between humans and robots. The platform assists seniors with day-to-day activities such as reading books and home device control. A further example is the ROBOEARTH system, a cloud-based knowledge representation using the web ontology language, proposed by [PERSON] et al. [12]. Their system allows the use of knowledge among networked robots. One use case of the system was demonstrated in a convenience store in which a robot tracked objects with attached RFID tags to detect and pick up specific objects. Moving toward object recognition support using cloud computing, [PERSON] et al. present an application based on the Willow Garage PR2 research robot that uses cloud-based support to perform highly difficult and highly complex tasks. Concretely, the robot acquires a 2D image and measures 3D point cloud data for a scene. Thereafter, the robot sends the 2D image to be processed on the cloud-based server for object recognition [13]. If the server can classify the object, it responds with the object's 3D CAD model. The robot utilizes both the 3D point cloud data and the 3D CAD model to compute grasping strategies, and stores the results on the server for reuse in the future. [PERSON] and [PERSON] [14] present a robotic system integrated with cloud infrastructure that offloads highly computational tasks such as object perception. The system contains multiple sensors for acquiring image data from robotic applications and uploading them to the cloud server. Thereafter, the cloud server determines the locations of objects and how the robot can interact with them. Other researchers are exploring support for interoperability between multiple robotic applications. For example, [PERSON] et al. demonstrate a robot decision-making approach using an ontology-based platform developed at Shandong University [15]. They describe household objects, sensors, equipment, and location of robots in a machine-interpretable manner using an ontology. The use of this ontology-based platform allows automation of service robots without human intervention. Concretely, they define a context-aware infrastructure ontology to represent human activities at home.