# Ontology-Based Framework for Cooperative Learning of 3D Object Recognition
###### Abstract  
Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots' object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots. on 2021 27 August 2021 31 August 2021 3  
Different service robots have different capabilities and different physical characteristics but connecting multiple service robots within the same network could enable sharing and pooling of resources. To make this goal a reality, more research is needed on internet-based architectures for robot learning. In our work, we propose a cloud-based framework for knowledge sharing between networked service robots to enable cooperative 3D object recognition. Our framework represents knowledge of robots (e.g., a robot's capabilities and physical characteristics) and objects (e.g., an object's appearance and description). Using knowledge sharing capabilities in our framework, robots can retrieve knowledge synthesized by other robots without performing extensive computation. This will potentially help lower robots' computation costs and time for performing object recognition tasks. We have developed a proof-of-concept prototype of the cloud-based knowledge sharing framework and a cloud-based service for the use case of 3D object recognition. An evaluation of the proposed framework with our prototype demonstrates the feasibility of delivering object recognition services to robots. Currently, different service robotic applications describe objects using their own specific methods. This prevents cooperative learning about detection and recognition between different service robots across different applications. In a cooperative environment in which robots share knowledge, translating object descriptions between different robotic applications would be tedious and error prone. It would involve different robot systems and different applications creating non-reusable knowledge. With the aim of enabling cooperative learning of recognition models for heterogeneous 3D objects, we propose an ontology-based framework. The framework allows a distributed system of cooperative robots to populate a unified knowledge base of heterogenous 3D objects. We build on the Unified Robot Description Format (URDF) defined by ROS.org to represent robots, because URDF is the de facto standard for describing robots [4; 5]. We define a new ontology, the Robotic Object Description (ROD) ontology, to represent heterogeneous objects.