Nov , 2006 The 2nd Korea-Sweden Workshop on Intelligent Systems for Societal Challenges of the 21st Century Pine Hall, 23F, Hotel The Silla Seoul, Korea Ontology-based Unified Robot Knowledge Framework (OUR-K) Il Hong Suh and Gi Hyun Lim Intelligence and Interaction Lab., Hanyang University

Intelligence and Interaction Lab. Hanyang University 2 Research Objective – Building Knowledge System for robot intelligence  Building ontology-based robot intelligence system which is capable of sharing and developing necessary knowledge for task services  Development of robot-centered unified ontology schema for concept representation of robot, object, behavior, interaction, environments  Development of ontology schema for robot intelligence  Development of ontology schema for daily schedule management  Generation of object ontology instance by use of multimodality of visual and auditory information, where 20 different object ontology schema will be considered. Object ontology instances should be then verified to know that knowledge consistency is still being preserved  Spatial-Temporal context understanding by use of ontology instances  20 different situations- 80% recognition accuracy  Prediction of human activity

Intelligence and Interaction Lab. Hanyang University 3 Application – Round-trip delivery service  Round-trip delivery service of previously unregistered objects from a room at a floor of a building to a room at a different floor of the building  Available sensors : auditory, 2D and/or 3D visual sensor, sonar  Building map can be provided or not.  Registration of objects by showing will be allowed.  20 objects will include:  Door ( office door, elevator door )  Sofa, TV, refrigerator, flowerpot, window, desk, chair  Wall hanging paintings, wall hanging clock, dinning table, bed  Shelf, air-conditioner, computer monitor, computer keyboard  Telephone, mug, eye glasses, newspapers

Intelligence and Interaction Lab. Hanyang University 4 What should we develop?  Ontology-based Unified Robot Knowledge (OUR-K) Framework  Knowledge Manager  How can we design knowledge manager to seamlessly integrate robot-centered ontology with Bayesian networks for domain specific object detection, context understanding and/or decision making?  Robot-centered ontology  Generic taxonomy of perception, model, context, activity  Multi-layered Multi-level Knowledge description is desirable for efficient representation, storage and inference?  Ontology instance API  What API should we develop to get ontology instance from sensor level to high level symbolic knowledge level?  Reasoning engine  Can we develop a hierarchical inference engine to process effectively ontology instance of multi-layered and multi-level knowledge description?  Bayesian Network  Domain-specific object detection, context understanding and/or decision making by using uncertain and/or incomplete description of sensory information, object information and/or contextual information  What do we want to achieve by applying OUR-K?  Cognitive navigation  Robust object recognition

Intelligence and Interaction Lab. Hanyang University 5 Research Schedule  Research group of professor Y.T. Park of Soongsil University will join us for development of ontology and reasoning engine. DurationObjectives Phase I ( ~ ) Knowledge Manager  Unified Knowledge manager for robust object recognition and context understanding using visual information and range finder (sonar) Robot-centered Ontology  Ontology and ontology instance for robust object recognition and context understanding using visual information and range finder (sonar) Bayesian Network  Bayesian network for robust object recognition and context understanding using visual information and range finder (sonar) Phase II ( ~ ) Knowledge Manager  Unified Knowledge manager for round-trip delivery service using visual, auditory and range finder information Robot-centered Ontology  Ontology and ontology instance for round-trip delivery service using visual, auditory and range finder information Bayesian Network  Bayesian network for round-trip delivery service using visual, auditory and range finder information