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<p>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.</p>

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A Unified Framework Integrating Object-oriented Task Learning and Knowledge-based Task Planning for Long-horizon Cooking Tasks

  • 期刊论文

  • 发表日期：

    2025年12月5日

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  • Sunwoong Na,
  • Soojin Jeong,
  • Hyojeong Kim,
  • Jiho Lee,
  • Jungkyoo Shin,
  • Soyeon Park,
  • Dongmin Yoon,
  • Jieun Han,
  • Eunwoo Kim,
  • Yoonseon Oh

摘要

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.