# A Survey of Knowledge Representation in Service Robotics

arXiv: https://arxiv.org/abs/1807.02192 · HTML: https://mlsanity.com/paper/1807.02192/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 18 of 18)

- [Semantics for Robotic Mapping, Perception and Interaction: A Survey](https://mlsanity.com/paper/2101.00443/) (2021)
- [A Memory System of a Robot Cognitive Architecture and its Implementation in ArmarX](https://mlsanity.com/paper/2206.02241/) (2022)
- [From Cooking Recipes to Robot Task Trees -- Improving Planning Correctness and Task Efficiency by Leveraging LLMs with a Knowledge Network](https://mlsanity.com/paper/2309.09181/) (2023)
- [Evaluating Recipes Generated from Functional Object-Oriented Network](https://mlsanity.com/paper/2106.00728/) (2021)
- [Consolidating Trees of Robotic Plans Generated Using Large Language Models to Improve Reliability](https://mlsanity.com/paper/2401.07868/) (2024)
- [Generalizable task representation learning from human demonstration videos: a geometric approach](https://mlsanity.com/paper/2202.13604/) (2022)
- [Long-Horizon Planning and Execution with Functional Object-Oriented Networks](https://mlsanity.com/paper/2207.05800/) (2022)
- [When Prolog meets generative models: a new approach for managing knowledge and planning in robotic applications](https://mlsanity.com/paper/2309.15049/) (2023)
- [STAR: A Foundation Model-driven Framework for Robust Task Planning and Failure Recovery in Robotic Systems](https://mlsanity.com/paper/2503.06060/) (2025)
- [Cooking Object's State Identification Without Using Pretrained Model](https://mlsanity.com/paper/2103.02305/) (2021)
- [A Road-map to Robot Task Execution with the Functional Object-Oriented Network](https://mlsanity.com/paper/2106.00158/) (2021)
- [Functional Task Tree Generation from a Knowledge Graph to Solve Unseen Problems](https://mlsanity.com/paper/2112.02433/) (2021)
- [World Knowledge from AI Image Generation for Robot Control](https://mlsanity.com/paper/2503.16579/) (2025)
- [Enhancing Explainability with Multimodal Context Representations for Smarter Robots](https://mlsanity.com/paper/2503.16467/) (2025)
- [Bridging Bots: from Perception to Action via Multimodal-LMs and Knowledge Graphs](https://mlsanity.com/paper/2507.09617/) (2025)
- [An Ontology for Unified Modeling of Tasks, Actions, Environments, and Capabilities in Personal Service Robotics](https://mlsanity.com/paper/2509.22434/) (2025)
- [FlatLands: Generative Floormap Completion From a Single Egocentric View](https://mlsanity.com/paper/2603.16016/) (2026)
- [Embodied Semantic Communication for Collective Autonomous Agents: A Tutorial on Representation, Wireless Delivery, and Closed-Loop Coordination](https://mlsanity.com/paper/2609.35936/) (2026)

## Abstract (arXiv)

Within the realm of service robotics, researchers have placed a great amount of effort into learning, understanding, and representing motions as manipulations for task execution by robots. The task of robot learning and problem-solving is very broad, as it integrates a variety of tasks such as object detection, activity recognition, task/motion planning, localization, knowledge representation and retrieval, and the intertwining of perception/vision and machine learning techniques. In this paper, we solely focus on knowledge representations and notably how knowledge is typically gathered, represented, and reproduced to solve problems as done by researchers in the past decades. In accordance with the definition of knowledge representations, we discuss the key distinction between such representations and useful learning models that have extensively been introduced and studied in recent years, such as machine learning, deep learning, probabilistic modelling, and semantic graphical structures. Along with an overview of such tools, we discuss the problems which have existed in robot learning and how they have been built and used as solutions, technologies or developments (if any) which have contributed to solving them. Finally, we discuss key principles that should be considered when designing an effective knowledge representation.