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Automating Transfer of Robot Task Plans using Functorial Data Migrations

arXiv 2406.15961 ↗First posted 22 Jun 2024cs.RO cs.AI math.CTcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 19 of its 71 references that are in the map. First posted on arXiv 22 Jun 2024.

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References · 19

  1. 2017AI2-THOR: An Interactive 3D Environment for Visual AIcited by 869
  2. 2020Integrated Task and Motion Planningcited by 290
  3. 2023RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulationcited by 218
  4. 2020Rearrangement: A Challenge for Embodied AIcited by 155
  5. 2023Task and Motion Planning with Large Language Models for Object Rearrangementcited by 141
  6. 2024On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)cited by 40
  7. 2021Offline Meta-Reinforcement Learning for Industrial Insertioncited by 39
  8. 2023Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worldscited by 26
  9. 2021Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policiescited by 22
  10. 2023Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challengescited by 18
  11. 2018Transfer of Deep Reactive Policies for MDP Planningcited by 11
  12. 2023Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Frameworkcited by 7
7 more
  1. 2018Scanning the Internet for ROS: A View of Security in Robotics Researchcited by 6
  2. 2021Categorical Data Structures for Technical Computingcited by 5
  3. 2019Learning to Plan Hierarchically from Curriculumcited by 4
  4. 2021Computational category-theoretic rewritingcited by 3
  5. 2023Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planningcited by 3
  6. 2016Algebraic Databasescited by 2
  7. 2018Finding plans subject to stipulations on what information they divulgecited by 1

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