# Ontology-Based Framework for Cooperative Learning of 3D Object Recognition
## 2 Related Work
They also include a concept in their ontology, namely \"object,\" to describe the types of objects used in the smart home. The ontology, however, does not comply with standards or reuse existing ontologies [16]. [PERSON] et al. define an ontology, namely RehabRobo-Onto, to formally represent data about rehabilitation robots in a knowledge base [17]. They also develop a cloud-based platform to integrate this knowledge base with other knowledge resources such as patient and disease data. The platform can recommend suitable robots to assist patients. The platform allows knowledge to be exchanged between rehabilitation robot researchers over the Internet to improve the development of rehabilitation robots. [PERSON] et al. propose an ontology-based platform using the Internet-of-Robotic-Things (IoRT) ontology [18]. The IoRT ontology contains different modules that describe various aspects of networked service robots, for example, semantic sensor networks for IoT, localization, person profiles, tasks, and personalized robotics. The platform provides a service for robot task planning based on IoT sensor data. [PERSON] et al. and [PERSON] et al. propose the Core Ontology for Robotics and Automation (CORA) to represent heterogeneous robots in a knowledge base [19; 20]. CORA was developed by the IEEE Ontologies for Robotics and Automation Working Group. Their work focuses only on robot description [21]. [PERSON] describes an ontology standard for autonomous robots called ORA developed by the IEEE RAS Autonomous Robotics (AuR) Study Group [22]. ORA includes concepts and relations for robotic applications inherited from SUMO [23]. Our work, in contrast to the previous work, represents het erogenous robots with the Unified Robot Description Format (URDF), the de facto standard for describing robots defined by ROS.org [24; 25]. The existing work does not consider knowledge sharing about object recognition tasks between different robots and different robotic applications. To achieve that goal, we propose a new ontology, the Robotic Object Description (ROD) ontology, to enable object learning by possibly heterogenous robots. Using URDF and ROD with WordNet, a large lexical database, our framework provides a cooperative learning environment inclusive of disparate robots and applications [26]. [PERSON] et al. present the Sharework Ontology for Human Robot Collaboration (SOHO) ontology built on top of the CORA ontology to represent collaborative manufacturing scenarios [27]. [PERSON] and [PERSON] propose an ontology-based knowledge management system to present the environment of home service robots and interaction between robots and the environment using natural language commands [28]. Our work, instead of focusing on enabling a collaborative environment for robots and humans, involves a framework to provide a common understanding about objects between service robots. To achieve this, we propose to use ontologies and linguistic analysis to describe robots and objects. [PERSON] et al. propose an ontology-based framework for storing cultural information to manage and adapt a robot's interaction to the user's habits and preferences [29]. This work presents a generic framework, while our work defines a specific ontology for robot cooperative learning. [PERSON] et al. also present an ontology-based framework, namely Perception and Manipulation Knowledge (PMK) to provide common vocabularies for knowledge sharing between humans and robots [30]. Their work focuses on a single environment, while our work proposes a framework to share knowledge among varied robots in different environments. The technology of 3D object perception helps support service robots in their effective operation, because a helpful robot must use 3D data to estimate the positions of objects relative to itself and the surroundings. In order to enable effective recognition of 3D objects and to enable planning of grasping or other manipulation strategies, we use the Point Cloud Data (PCD) format standard [31].