mlsanityData to 7 Oct 2026

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

arXiv 2404.03634 ↗First posted 4 Apr 2024cs.RO cs.CVcited by 11

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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.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 30 of its 44 references that are in the map. First posted on arXiv 4 Apr 2024.

Builds on

30 of 44 references.

References · 30

  1. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  2. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  3. 2020SAPIEN: A SimulAted Part-based Interactive ENvironmentcited by 584
  4. 2021Where2Act: From Pixels to Actions for Articulated 3D Objectscited by 179
  5. 2021VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objectscited by 78
  6. 2024Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulationcited by 69
  7. 2021AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactionscited by 56
  8. 2022GenDexGrasp: Generalizable Dexterous Graspingcited by 54
  9. 2021O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance Learningcited by 46
  10. 2023Open-Vocabulary Affordance Detection in 3D Point Cloudscited by 41
  11. 2023Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objectscited by 33
  12. 2021INVIGORATE: Interactive Visual Grounding and Grasping in Cluttercited by 32
18 more
  1. 2022TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillationcited by 25
  2. 2022Learning to Grasp the Ungraspable with Emergent Extrinsic Dexteritycited by 25
  3. 2023Learning Foresightful Dense Visual Affordance for Deformable Object Manipulationcited by 25
  4. 2023Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusionscited by 23
  5. 2021Cerberus Transformer: Joint Semantic, Affordance and Attribute Parsingcited by 22
  6. 2022DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulationcited by 18
  7. 2023From Semi-supervised to Omni-supervised Room Layout Estimation Using Point Cloudscited by 18
  8. 2023Grasp Multiple Objects with One Handcited by 18
  9. 2023DQS3D: Densely-matched Quantization-aware Semi-supervised 3D Detectioncited by 16
  10. 2024UniDoorManip: Learning Universal Door Manipulation Policy Over Large-scale and Diverse Door Manipulation Environmentscited by 14
  11. 2024Articulated Object Manipulation with Coarse-to-fine Affordance for Mitigating the Effect of Point Cloud Noisecited by 12
  12. 2023STRAP: Structured Object Affordance Segmentation with Point Supervisioncited by 10
  13. 2023Synthesize Dexterous Nonprehensile Pregrasp for Ungraspable Objectscited by 10
  14. 2020Learning Pregrasp Manipulation of Objects from Ungraspable Posescited by 8
  15. 2019Efficient Obstacle Rearrangement for Object Manipulation Tasks in Cluttered Environmentscited by 7
  16. 2022Self-Supervised Learning for Joint Pushing and Grasping Policies in Highly Cluttered Environmentscited by 4
  17. 2017Randomized Physics-based Motion Planning for Grasping in Cluttered and Uncertain Environmentscited by 3
  18. 2021Fast-Learning Grasping and Pre-Grasping via Clutter Quantization and Q-map Maskingcited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 2.

11 papers.

  1. 2025TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Modelscited by 30
  2. 2024UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondencecited by 22
  3. 2025DyWA: Dynamics-adaptive World Action Model for Generalizable Non-prehensile Manipulationcited by 21
  4. 2025CordViP: Correspondence-based Visuomotor Policy for Dexterous Manipulation in Real-Worldcited by 16
  5. 2025PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Modelcited by 15
  6. 2025RoboChemist: Long-Horizon and Safety-Compliant Robotic Chemical Experimentationcited by 9
  7. 2026LIBERO-Safety: A Comprehensive Benchmark for Physical and Semantic Safety in Vision-Language-Action Modelscited by 8
  8. 2025BiAssemble: Learning Collaborative Affordance for Bimanual Geometric Assemblycited by 3
  9. 2025Weakly-Supervised Learning of Dense Functional Correspondencescited by 2
  10. 2024Locate n' Rotate: Two-stage Openable Part Detection with Foundation Model Priorscited by 1
  11. 2026AdvDex: Learning Dexterous Manipulation from Human Demonstrations via Joint-Aligned Actions and Adversarial Learningcited by 0