Na, Sunwoong Jeong, Soojin Kim, Hyojeong Lee, Jiho Shin, Jungkyoo Park, Soyeon Yoon, Dongmin Han, Jieun Kim, Eunwoo Oh, Yoonseon 2025-12-23T07:30:15Z 2025-12-23T07:30:15Z 2025-12-19 2025-12 1598-6446 https://pubs.kist.re.kr/handle/201004/153869 Many tasks for service robots are complex and require lengthy processes. Task planning methods are widely used to address such challenges, but search-based planners are often inflexible, while learning-based planners do not guarantee feasibility. To overcome these limitations, we propose a hierarchical framework that integrates a knowledge base, a learning-based object-oriented task planner, and a symbolic robot task planner. The object-oriented task planner predicts subgoals, defined as changes in object states, from only a recipe name and a list of ingredients. The symbolic robot task planner then generates a feasible sequence of high-level robot actions using the proposed object knowledge base. Our framework focuses on high-level symbolic task planning and demonstrates generalization and feasibility across diverse recipes and action sets. We focus on cooking as a representative long-horizon domain, where sequential dependencies and embodiment-specific constraints naturally arise. Experimental validation was conducted on 20 representative recipes with 20,000 generated task samples, demonstrating robust performance across diverse cooking scenarios. English 제어·로봇·시스템학회 A Unified Framework Integrating Object-oriented Task Learning and Knowledge-based Task Planning for Long-horizon Cooking Tasks Article 10.1007/s12555-025-0524-5 1 International Journal of Control, Automation, and Systems, v.23, no.12, pp.3637 - 3648 International Journal of Control, Automation, and Systems 23 12 3637 3648 N scie scopus kci ART003266767 001632328900013 2-s2.0-105024067561 Automation & Control Systems Automation & Control Systems Article MOTION Long-horizon planning task learning task planning