# One-Shot Affordance Grounding of Deformable Objects in Egocentric Organizing Scenes arXiv: https://arxiv.org/abs/2503.01092 ยท HTML: https://mlsanity.com/paper/2503.01092/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 29 of its 47 references that are in the map. - First posted on arXiv 3 Mar 2025. ## Builds on (most cited first, 29 of 29) - [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023) - [Conditional Prompt Learning for Vision-Language Models](https://mlsanity.com/paper/2203.05557/) (2022) - [Universal Manipulation Interface: In-The-Wild Robot Teaching Without In-The-Wild Robots](https://mlsanity.com/paper/2402.10329/) (2024) - [ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation](https://mlsanity.com/paper/2409.01652/) (2024) - [HumanPlus: Humanoid Shadowing and Imitation from Humans](https://mlsanity.com/paper/2406.10454/) (2024) - [DeepFashion2: A Versatile Benchmark for Detection, Pose Estimation, Segmentation and Re-Identification of Clothing Images](https://mlsanity.com/paper/1901.07973/) (2019) - [FlingBot: The Unreasonable Effectiveness of Dynamic Manipulation for Cloth Unfolding](https://mlsanity.com/paper/2105.03655/) (2021) - [Learning to Rearrange Deformable Cables, Fabrics, and Bags with Goal-Conditioned Transporter Networks](https://mlsanity.com/paper/2012.03385/) (2020) - [LOCATE: Localize and Transfer Object Parts for Weakly Supervised Affordance Grounding](https://mlsanity.com/paper/2303.09665/) (2023) - [FabricFlowNet: Bimanual Cloth Manipulation with a Flow-based Policy](https://mlsanity.com/paper/2111.05623/) (2021) - [SpeedFolding: Learning Efficient Bimanual Folding of Garments](https://mlsanity.com/paper/2208.10552/) (2022) - [One-Shot Open Affordance Learning with Foundation Models](https://mlsanity.com/paper/2311.17776/) (2023) - [One-Shot Object Affordance Detection in the Wild](https://mlsanity.com/paper/2108.03658/) (2021) - [Mesh-based Dynamics with Occlusion Reasoning for Cloth Manipulation](https://mlsanity.com/paper/2206.02881/) (2022) - [Dynamic Cloth Manipulation with Deep Reinforcement Learning](https://mlsanity.com/paper/1910.14475/) (2019) - [Cloth Funnels: Canonicalized-Alignment for Multi-Purpose Garment Manipulation](https://mlsanity.com/paper/2210.09347/) (2022) - [Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation](https://mlsanity.com/paper/2303.11057/) (2023) - [Learning Arbitrary-Goal Fabric Folding with One Hour of Real Robot Experience](https://mlsanity.com/paper/2010.03209/) (2020) - [UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence](https://mlsanity.com/paper/2405.06903/) (2024) - [One Policy to Dress Them All: Learning to Dress People with Diverse Poses and Garments](https://mlsanity.com/paper/2306.12372/) (2023) - [UniFolding: Towards Sample-efficient, Scalable, and Generalizable Robotic Garment Folding](https://mlsanity.com/paper/2311.01267/) (2023) - [Learning to Grasp Clothing Structural Regions for Garment Manipulation Tasks](https://mlsanity.com/paper/2306.14553/) (2023) - [Efficiently Learning Single-Arm Fling Motions to Smooth Garments](https://mlsanity.com/paper/2206.08921/) (2022) - [Learning Fabric Manipulation in the Real World with Human Videos](https://mlsanity.com/paper/2211.02832/) (2022) - [QDP: Learning to Sequentially Optimise Quasi-Static and Dynamic Manipulation Primitives for Robotic Cloth Manipulation](https://mlsanity.com/paper/2303.13320/) (2023) - [FabricFolding: Learning Efficient Fabric Folding without Expert Demonstrations](https://mlsanity.com/paper/2303.06587/) (2023) - [ClothPPO: A Proximal Policy Optimization Enhancing Framework for Robotic Cloth Manipulation with Observation-Aligned Action Spaces](https://mlsanity.com/paper/2405.04549/) (2024) - [Learning Correspondence for Deformable Objects](https://mlsanity.com/paper/2405.08996/) (2024) - [DeFNet: Deconstructed Strategy for Multi-step Fabric Folding Tasks](https://mlsanity.com/paper/2303.00323/) (2023) ## Built on by (most cited first, 3 of 3) - [TRACER: Texture-Robust Affordance Chain-of-Thought for Deformable-Object Refinement](https://mlsanity.com/paper/2601.20208/) (2026) - [arg-VU: Affordance Reasoning with Physics-Aware 3D Geometry for Visual Understanding in Robotic Surgery](https://mlsanity.com/paper/2603.26814/) (2026) - [Zero-shot 2D Grounding with Novel Affordance Types](https://mlsanity.com/paper/2608.08929/) (2026) ## Abstract (arXiv) Deformable object manipulation in robotics presents significant challenges due to uncertainties in component properties, diverse configurations, visual interference, and ambiguous prompts. These factors complicate both perception and control tasks. To address these challenges, we propose a novel method for One-Shot Affordance Grounding of Deformable Objects (OS-AGDO) in egocentric organizing scenes, enabling robots to recognize previously unseen deformable objects with varying colors and shapes using minimal samples. Specifically, we first introduce the Deformable Object Semantic Enhancement Module (DefoSEM), which enhances hierarchical understanding of the internal structure and improves the ability to accurately identify local features, even under conditions of weak component information. Next, we propose the ORB-Enhanced Keypoint Fusion Module (OEKFM), which optimizes feature extraction of key components by leveraging geometric constraints and improves adaptability to diversity and visual interference. Additionally, we propose an instance-conditional prompt based on image data and task context, which effectively mitigates the issue of region ambiguity caused by prompt words. To validate these methods, we construct a diverse real-world dataset, AGDDO15, which includes 15 common types of deformable objects and their associated organizational actions. Experimental results demonstrate that our approach significantly outperforms state-of-the-art methods, achieving improvements of 6.2%, 3.2%, and 2.9% in KLD, SIM, and NSS metrics, respectively, while exhibiting high generalization performance. Source code and benchmark dataset are made publicly available at https://github.com/Dikay1/OS-AGDO.