Location Reasoning of Target Objects Based on Human Common Sense and Robot Experiences

The location reasoning of target objects in robot-operated environment is a challenging task. Objects that robots need to interact with are often located at a distance or are contained within containers, making them inaccessible for direct observation by the robot. The uncertainty of the storage location of the target objects and the lack of reasoning ability present considerable challenges. In this article, we propose a method for semantic localization of robot-operated objects based on human common sense and robot experiences. Instead of reasoning the object storage locations solely based on the category of the target object, a probabilistic ontology model is introduced to represent uncertain knowledge in the task of object localization, which combines the expressive power of classical first-order logic and the inference capability of Bayesian inference. The target location is then estimated using the probabilistic ontologies with dynamic integration of human common sense and robot experiences. Experimental results in both simulation and real-world environments demonstrate the effectiveness of the proposed integration of human common sense and robot experiences in the task of semantic localization of robot-operated objects.