# PreAfford: Universal Affordance-Based Pre-Grasping for Diverse Objects and Environments

arXiv: https://arxiv.org/abs/2404.03634 · HTML: https://mlsanity.com/paper/2404.03634/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 44 references that are in the map.
- First posted on arXiv 4 Apr 2024.

## Builds on (most cited first, 30 of 30)

- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [Where2Act: From Pixels to Actions for Articulated 3D Objects](https://mlsanity.com/paper/2101.02692/) (2021)
- [VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objects](https://mlsanity.com/paper/2106.14440/) (2021)
- [Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulation](https://mlsanity.com/paper/2401.07487/) (2024)
- [AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactions](https://mlsanity.com/paper/2112.00246/) (2021)
- [GenDexGrasp: Generalizable Dexterous Grasping](https://mlsanity.com/paper/2210.00722/) (2022)
- [O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learning](https://mlsanity.com/paper/2106.15087/) (2021)
- [Open-Vocabulary Affordance Detection in 3D Point Clouds](https://mlsanity.com/paper/2303.02401/) (2023)
- [Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objects](https://mlsanity.com/paper/2309.07473/) (2023)
- [INVIGORATE: Interactive Visual Grounding and Grasping in Clutter](https://mlsanity.com/paper/2108.11092/) (2021)
- [Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity](https://mlsanity.com/paper/2211.01500/) (2022)
- [TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation](https://mlsanity.com/paper/2210.10775/) (2022)
- [Learning Foresightful Dense Visual Affordance for Deformable Object Manipulation](https://mlsanity.com/paper/2303.11057/) (2023)
- [Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions](https://mlsanity.com/paper/2309.07510/) (2023)
- [Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsing](https://mlsanity.com/paper/2111.12608/) (2021)
- [DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulation](https://mlsanity.com/paper/2207.01971/) (2022)
- [From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Clouds](https://mlsanity.com/paper/2301.13865/) (2023)
- [Grasp Multiple Objects with One Hand](https://mlsanity.com/paper/2310.15599/) (2023)
- [UniDoorManip: Learning Universal Door Manipulation Policy Over Large-scale and Diverse Door Manipulation Environments](https://mlsanity.com/paper/2403.02604/) (2024)
- [DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detection](https://mlsanity.com/paper/2304.13031/) (2023)
- [Articulated Object Manipulation with Coarse-to-fine Affordance for Mitigating the Effect of Point Cloud Noise](https://mlsanity.com/paper/2402.18699/) (2024)
- [STRAP: Structured Object Affordance Segmentation with Point Supervision](https://mlsanity.com/paper/2304.08492/) (2023)
- [Synthesize Dexterous Nonprehensile Pregrasp for Ungraspable Objects](https://mlsanity.com/paper/2305.04654/) (2023)
- [Learning Pregrasp Manipulation of Objects from Ungraspable Poses](https://mlsanity.com/paper/2002.06344/) (2020)
- [Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environments](https://mlsanity.com/paper/1902.06907/) (2019)
- [Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environments](https://mlsanity.com/paper/2203.02511/) (2022)
- [Randomized Physics-based Motion Planning for Grasping in Cluttered and Uncertain Environments](https://mlsanity.com/paper/1711.09604/) (2017)
- [Fast-Learning Grasping and Pre-Grasping via Clutter Quantization and Q-map Masking](https://mlsanity.com/paper/2107.02452/) (2021)

## Built on by (most cited first, 11 of 11)

- [TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models](https://mlsanity.com/paper/2509.07962/) (2025)
- [UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence](https://mlsanity.com/paper/2405.06903/) (2024)
- [DyWA: Dynamics-adaptive World Action Model for Generalizable Non-prehensile Manipulation](https://mlsanity.com/paper/2503.16806/) (2025)
- [CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-World](https://mlsanity.com/paper/2502.08449/) (2025)
- [PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model](https://mlsanity.com/paper/2503.19913/) (2025)
- [LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Models](https://mlsanity.com/paper/2606.23686/) (2026)
- [RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentation](https://mlsanity.com/paper/2509.08820/) (2025)
- [BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assembly](https://mlsanity.com/paper/2506.06221/) (2025)
- [Weakly-Supervised Learning of Dense Functional Correspondences](https://mlsanity.com/paper/2509.03893/) (2025)
- [Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priors](https://mlsanity.com/paper/2412.13173/) (2024)
- [AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learning](https://mlsanity.com/paper/2608.14028/) (2026)

## Abstract (arXiv)

Robotic manipulation with two-finger grippers is challenged by objects lacking distinct graspable features. Traditional pre-grasping methods, which typically involve repositioning objects or utilizing external aids like table edges, are limited in their adaptability across different object categories and environments. To overcome these limitations, we introduce PreAfford, a novel pre-grasping planning framework incorporating a point-level affordance representation and a relay training approach. Our method significantly improves adaptability, allowing effective manipulation across a wide range of environments and object types. When evaluated on the ShapeNet-v2 dataset, PreAfford not only enhances grasping success rates by 69% but also demonstrates its practicality through successful real-world experiments. These improvements highlight PreAfford's potential to redefine standards for robotic handling of complex manipulation tasks in diverse settings.