# Learning Grasp Affordance Reasoning through Semantic Relations

arXiv: https://arxiv.org/abs/1906.09836 · HTML: https://mlsanity.com/paper/1906.09836/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2019.

## Built on by (most cited first, 23 of 23)

- [LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding](https://mlsanity.com/paper/2303.09665/) (2023)
- [One-Shot Open Affordance Learning with Foundation Models](https://mlsanity.com/paper/2311.17776/) (2023)
- [Splat-MOVER: Multi-Stage, Open-Vocabulary Robotic Manipulation via Editable Gaussian Splatting](https://mlsanity.com/paper/2405.04378/) (2024)
- [Learning Visual Affordance Grounding from Demonstration Videos](https://mlsanity.com/paper/2108.05675/) (2021)
- [Multi-label affordance mapping from egocentric vision](https://mlsanity.com/paper/2309.02120/) (2023)
- [Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding](https://mlsanity.com/paper/2301.11564/) (2023)
- [Composable Part-Based Manipulation](https://mlsanity.com/paper/2405.05876/) (2024)
- [Towards Open-World Grasping with Large Vision-Language Models](https://mlsanity.com/paper/2406.18722/) (2024)
- [INTRA: Interaction Relationship-aware Weakly Supervised Affordance Grounding](https://mlsanity.com/paper/2409.06210/) (2024)
- [Sample-Efficient Learning of Novel Visual Concepts](https://mlsanity.com/paper/2306.09482/) (2023)
- [Building Affordance Relations for Robotic Agents - A Review](https://mlsanity.com/paper/2105.06706/) (2021)
- [Egocentric zone-aware action recognition across environments](https://mlsanity.com/paper/2409.14205/) (2024)
- [AffordanceSAM: Segment Anything Once More in Affordance Grounding](https://mlsanity.com/paper/2504.15650/) (2025)
- [Selective Contrastive Learning for Weakly Supervised Affordance Grounding](https://mlsanity.com/paper/2508.07877/) (2025)
- [Learning Any-View 6DoF Robotic Grasping in Cluttered Scenes via Neural Surface Rendering](https://mlsanity.com/paper/2306.07392/) (2023)
- [Graph learning in robotics: a survey](https://mlsanity.com/paper/2310.04294/) (2023)
- [Universal Dexterous Functional Grasping via Demonstration-Editing Reinforcement Learning](https://mlsanity.com/paper/2512.13380/) (2025)
- [Visual Imitation Learning of Task-Oriented Object Grasping and Rearrangement](https://mlsanity.com/paper/2403.14000/) (2024)
- [ContactHandover: Contact-Guided Robot-to-Human Object Handover](https://mlsanity.com/paper/2404.01402/) (2024)
- [Learning Multimodal AI Algorithms for Amplifying Limited User Input into High-dimensional Control Space](https://mlsanity.com/paper/2505.11366/) (2025)
- [RealVLG-R1: A Large-Scale Real-World Visual-Language Grounding Benchmark for Robotic Perception and Manipulation](https://mlsanity.com/paper/2603.14880/) (2026)
- [Semantics in Robotics: Environmental Data Can't Yield Conventions of Human Behaviour](https://mlsanity.com/paper/2410.19308/) (2024)
- [Efficiently Linking Real Scenes with Synthetic Data Generation for AI-based Cognitive Robotics and Computer Vision Applications](https://mlsanity.com/paper/2606.20272/) (2026)

## Abstract (arXiv)

Reasoning about object affordances allows an autonomous agent to perform generalised manipulation tasks among object instances. While current approaches to grasp affordance estimation are effective, they are limited to a single hypothesis. We present an approach for detection and extraction of multiple grasp affordances on an object via visual input. We define semantics as a combination of multiple attributes, which yields benefits in terms of generalisation for grasp affordance prediction. We use Markov Logic Networks to build a knowledge base graph representation to obtain a probability distribution of grasp affordances for an object. To harvest the knowledge base, we collect and make available a novel dataset that relates different semantic attributes. We achieve reliable mappings of the predicted grasp affordances on the object by learning prototypical grasping patches from several examples. We show our method's generalisation capabilities on grasp affordance prediction for novel instances and compare with similar methods in the literature. Moreover, using a robotic platform, on simulated and real scenarios, we evaluate the success of the grasping task when conditioned on the grasp affordance prediction.