Nov. 16-17, 2006

The 2nd Korea-Sweden Workshop onIntelligent 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.

Duration Objectives

Phase I

(2007.3 ~ 2008. 2)

Knowledge ManagerUnified 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 NetworkBayesian network for robust object recognition and context

understanding using visual information and range finder (sonar)

Phase II

(2008.3 ~ 2009. 2)

Knowledge ManagerUnified 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 NetworkBayesian network for round-trip delivery service using visual,

auditory and range finder information