# Ontology-Based Framework for Cooperative Learning of 3D Object Recognition
## 3 Proposed Cloud and Ontology-Based Framework Architecture
### The Ontology-Based Framework  
We create an ontology-based framework to support the creation of a cooperative heterogenous robot learning environment. Our framework allows multiple robots to offload compute-intensive machine vision tasks to cloud infrastructure. Concretely, we propose the Robot Object Description (ROD) ontology, which uses of the SUMO ontology to represent objects and the Friend-of-a-Friend (FOAF) ontology to represent users who own or utilize robots. A user can label an object observed by a robot with a WordNet concept to create a common understanding of what the object is. Figure 2 gives a high-level overview of the framework. A newly labeled object can be added to a collective training dataset by a particular robot. Once the new knowledge is incorporated into the collective knowledge base, other robots can reuse the new knowledge to in turn recognize the same or a similar object and possibly perform an action with the object. In this example, the robot's goal is to grasp the object. The knowledge-based component of the proposed framework has five main modules, namely the Robot Repository, the Object Ontology, WordNet, the 3D Object Repository, and the Object Grasping Strategy Repository. We detail each module as follows. The Robot Repository is a collection of robot descriptions. We use the Unified Robot Description Format (URDF), which is a standard for describing the physical and mechanical characteristics of robots, as shown in Figure 3. In addition, Appendix A elaborates on the URDF format in more detail. In fact, physical differences among a heterogenous collective of robots could affect the transferability of knowledge and their ability to cooperate toward a particular goal. For example, in order for a service robot to grasp an object, the robot must first be able to recognize the identity of the object, gauge the position of the object in the robotic frame of reference, and estimate the orientation of the object with respect the robot's position, using appropriate sensors apparatus. However, for the service robot to plan how to move and grasp the object, it needs a physical description of itself. Figure 2: Schematic of the ontology-based framework. The Object Ontology is a knowledge base of heterogenous objects. When robots learn knowledge in isolation, the information they acquire may only be usable by that particular robot. This would mean the platform would not allow reuse of object recognition and pose estimation knowledge. It would also require each robot to store knowledge of an exhaustively large number of objects, which is infeasible due to limited storage and computational power. We solve these problems by providing a cloud-based 3D Object Repository to store the collective knowledge of multiple service robots. Our knowledge base uses the new Robot Object Description (ROD) ontology to represent objects and FOAF to represent people that label objects. The use of ontologies promotes cooperation in learning about objects by multiple robots with different physical descriptions. WordNet enables the system to share a common understanding of objects across multiple robots and multiple applications. The basic operation required for a human to assist a robot in an object understanding task is to identify (label) the object and optionally provide a textual description. To construct and share such knowledge among multiple robots in various locations operated by different users, linguistic analysis needs to be considered. We integrate WordNet, a lexical database for the English language, with our ontology-based framework to enable interpretation and identification of objects and their descriptions. In this way, all the robots in our cooperative learning environment can share knowledge of objects and build up a common understanding of such objects. The Point Cloud 3D Object description format is a standard for describing a 3D object as a collection of data points defined in a 3D vector space, as shown in Figure 4. Appendix B elaborates on the PCD format in more detail.