# Automating Transfer of Robot Task Plans using Functorial Data Migrations

arXiv: https://arxiv.org/abs/2406.15961 · HTML: https://mlsanity.com/paper/2406.15961/

## 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.

## Builds on (most cited first, 19 of 19)

- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [Integrated Task and Motion Planning](https://mlsanity.com/paper/2010.01083/) (2020)
- [RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation](https://mlsanity.com/paper/2311.01455/) (2023)
- [Rearrangement: A Challenge for Embodied AI](https://mlsanity.com/paper/2011.01975/) (2020)
- [Task and Motion Planning with Large Language Models for Object Rearrangement](https://mlsanity.com/paper/2303.06247/) (2023)
- [On the Prospects of Incorporating Large Language Models (LLMs) in Automated Planning and Scheduling (APS)](https://mlsanity.com/paper/2401.02500/) (2024)
- [Offline Meta-Reinforcement Learning for Industrial Insertion](https://mlsanity.com/paper/2110.04276/) (2021)
- [Integrating Action Knowledge and LLMs for Task Planning and Situation Handling in Open Worlds](https://mlsanity.com/paper/2305.17590/) (2023)
- [Causal Reasoning in Simulation for Structure and Transfer Learning of Robot Manipulation Policies](https://mlsanity.com/paper/2103.16772/) (2021)
- [Transfer Learning in Robotics: An Upcoming Breakthrough? A Review of Promises and Challenges](https://mlsanity.com/paper/2311.18044/) (2023)
- [Transfer of Deep Reactive Policies for MDP Planning](https://mlsanity.com/paper/1810.11488/) (2018)
- [Efficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework](https://mlsanity.com/paper/2306.01839/) (2023)
- [Scanning the Internet for ROS: A View of Security in Robotics Research](https://mlsanity.com/paper/1808.03322/) (2018)
- [Categorical Data Structures for Technical Computing](https://mlsanity.com/paper/2106.04703/) (2021)
- [Learning to Plan Hierarchically from Curriculum](https://mlsanity.com/paper/1906.07371/) (2019)
- [Computational category-theoretic rewriting](https://mlsanity.com/paper/2111.03784/) (2021)
- [Using Implicit Behavior Cloning and Dynamic Movement Primitive to Facilitate Reinforcement Learning for Robot Motion Planning](https://mlsanity.com/paper/2307.16062/) (2023)
- [Algebraic Databases](https://mlsanity.com/paper/1602.03501/) (2016)
- [Finding plans subject to stipulations on what information they divulge](https://mlsanity.com/paper/1809.09682/) (2018)

## 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.