Automatic Generation of a Simulated Robot from an Ontology-Based Semantic Description

Humans are capable of generating simulated mental worlds based on their past experiences and use such an environment for prospecting, planning, and learning. Such capabilities could enhance current robotic systems, allowing them to plan ahead based on predicted outputs, and even compare their performance with a different agent. In this work, we propose a semantic robot modeling framework, which is able to express intrinsic semantic knowledge in order to better represent the robot and its surrounding environment. We also show that such data can be used to automatically generate a simulated model, allowing robots to simulate themselves and other modeled agents.