# Using The Concept Hierarchy for Household Action Recognition

arXiv: https://arxiv.org/abs/2409.08853 · HTML: https://mlsanity.com/paper/2409.08853/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 12 references that are in the map.
- First posted on arXiv 13 Sep 2024.

## Builds on (most cited first, 4 of 4)

- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [Human Action Recognition and Prediction: A Survey](https://mlsanity.com/paper/1806.11230/) (2018)
- [Intel RealSense Stereoscopic Depth Cameras](https://mlsanity.com/paper/1705.05548/) (2017)
- [Learning Object-Action Relations from Bimanual Human Demonstration Using Graph Networks](https://mlsanity.com/paper/1908.08391/) (2019)

## Built on by (most cited first, 1 of 1)

- [Adaptation of Task Goal States from Prior Knowledge](https://mlsanity.com/paper/2502.03918/) (2025)

## Abstract (arXiv)

We propose a method to systematically represent both the static and the dynamic components of environments, i.e. objects and agents, as well as the changes that are happening in the environment, i.e. the actions and skills performed by agents. Our approach, the Concept Hierarchy, provides the necessary information for autonomous systems to represent environment states, perform action modeling and recognition, and plan the execution of tasks. Additionally, the hierarchical structure supports generalization and knowledge transfer to environments. We rigorously define tasks, actions, skills, and affordances that enable human-understandable action and skill recognition.