# Visual Affordance and Function Understanding: A Survey

arXiv: https://arxiv.org/abs/1807.06775 · HTML: https://mlsanity.com/paper/1807.06775/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2018.

## Built on by (most cited first, 20 of 20)

- [3D AffordanceNet: A Benchmark for Visual Object Affordance Understanding](https://mlsanity.com/paper/2103.16397/) (2021)
- [Affordance Transfer Learning for Human-Object Interaction Detection](https://mlsanity.com/paper/2104.02867/) (2021)
- [LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding](https://mlsanity.com/paper/2303.09665/) (2023)
- [AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand Pose](https://mlsanity.com/paper/2309.08942/) (2023)
- [One-Shot Affordance Detection](https://mlsanity.com/paper/2106.14747/) (2021)
- [Affordance Learning from Play for Sample-Efficient Policy Learning](https://mlsanity.com/paper/2203.00352/) (2022)
- [GREAT: Geometry-Intention Collaborative Inference for Open-Vocabulary 3D Object Affordance Grounding](https://mlsanity.com/paper/2411.19626/) (2024)
- [K-VIL: Keypoints-based Visual Imitation Learning](https://mlsanity.com/paper/2209.03277/) (2022)
- [UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation](https://mlsanity.com/paper/2506.09284/) (2025)
- [Grounded Affordance from Exocentric View](https://mlsanity.com/paper/2208.13196/) (2022)
- [GEAL: Generalizable 3D Affordance Learning with Cross-Modal Consistency](https://mlsanity.com/paper/2412.09511/) (2024)
- [What does CLIP know about peeling a banana?](https://mlsanity.com/paper/2404.12015/) (2024)
- [Navigation-Oriented Scene Understanding for Robotic Autonomy: Learning to Segment Driveability in Egocentric Images](https://mlsanity.com/paper/2109.07245/) (2021)
- [Approaching human 3D shape perception with neurally mappable models](https://mlsanity.com/paper/2308.11300/) (2023)
- [Egocentric zone-aware action recognition across environments](https://mlsanity.com/paper/2409.14205/) (2024)
- [RPMArt: Towards Robust Perception and Manipulation for Articulated Objects](https://mlsanity.com/paper/2403.16023/) (2024)
- [PAVLM: Advancing Point Cloud based Affordance Understanding Via Vision-Language Model](https://mlsanity.com/paper/2410.11564/) (2024)
- [Visual-Geometric Collaborative Guidance for Affordance Learning](https://mlsanity.com/paper/2410.11363/) (2024)
- [AIGenC: An AI generalisation model via creativity](https://mlsanity.com/paper/2205.09738/) (2022)
- [Semantics in Robotics: Environmental Data Can't Yield Conventions of Human Behaviour](https://mlsanity.com/paper/2410.19308/) (2024)

## Abstract (arXiv)

Nowadays, robots are dominating the manufacturing, entertainment and healthcare industries. Robot vision aims to equip robots with the ability to discover information, understand it and interact with the environment. These capabilities require an agent to effectively understand object affordances and functionalities in complex visual domains. In this literature survey, we first focus on Visual affordances and summarize the state of the art as well as open problems and research gaps. Specifically, we discuss sub-problems such as affordance detection, categorization, segmentation and high-level reasoning. Furthermore, we cover functional scene understanding and the prevalent functional descriptors used in the literature. The survey also provides necessary background to the problem, sheds light on its significance and highlights the existing challenges for affordance and functionality learning.