2024/06/22 by Angeline Aguinaldo, Evan Patterson, Aguinaldo, Angeline +3 · 1 citation
Computer Science · Engineering · #18-08 #Artificial Intelligence (cs.AI) #Category Theory (math.CT) #FOS: Computer and information sciences #FOS: Mathematics #I.2.9 #Reinforcement Learning in Robotics #Robot Manipulation and Learning #Robotics (cs.RO) #Scheduling and Optimization Algorithms
paper · pdf · doi:10.48550/arxiv.2406.15961
openalex publication_date 2024/06/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper introduces a novel approach to ontology-based robot plan transfer by leveraging functorial data migrations, a structured mapping method derived from category theory. Functors provide structured maps between planning domain ontologies which enables the transfer of task plans without the need for replanning. Unlike methods tailored to specific plans, our framework applies universally within the source domain once a structured map is defined. We demonstrate this approach by transferring a task plan from the canonical Blocksworld domain to one compatible with the AI2-THOR Kitchen environment. Additionally, we discuss practical limitations, propose benchmarks for evaluating symbolic plan transfer methods, and outline future directions for scaling this approach.