mlsanityData to 7 Oct 2026

EqvAfford: SE(3) Equivariance for Point-Level Affordance Learning

arXiv 2408.01953 ↗First posted 4 Aug 2024cs.RO cs.CV cs.LGcited by 6

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Abstract (arXiv)

Humans perceive and interact with the world with the awareness of equivariance, facilitating us in manipulating different objects in diverse poses. For robotic manipulation, such equivariance also exists in many scenarios. For example, no matter what the pose of a drawer is (translation, rotation and tilt), the manipulation strategy is consistent (grasp the handle and pull in a line). While traditional models usually do not have the awareness of equivariance for robotic manipulation, which might result in more data for training and poor performance in novel object poses, we propose our EqvAfford framework, with novel designs to guarantee the equivariance in point-level affordance learning for downstream robotic manipulation, with great performance and generalization ability on representative tasks on objects in diverse poses.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 35 of its 41 references that are in the map. First posted on arXiv 4 Aug 2024.

Builds on

35 of 41 references.

References · 35

  1. 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 602
  2. 2020SAPIEN: A SimulAted Part-based Interactive ENvironmentcited by 584
  3. 2021Vector Neurons: A General Framework for SO(3)-Equivariant Networkscited by 304
  4. 2021Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulationcited by 204
  5. 2021Where2Act: From Pixels to Actions for Articulated 3D Objectscited by 179
  6. 2019ContactDB: Analyzing and Predicting Grasp Contact via Thermal Imagingcited by 145
  7. 2024General Flow as Foundation Affordance for Scalable Robot Learningcited by 80
  8. 2021VAT-Mart: Learning Visual Action Trajectory Proposals for Manipulating 3D ARTiculated Objectscited by 78
  9. 2024EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learningcited by 72
  10. 2020ScrewNet: Category-Independent Articulation Model Estimation From Depth Images Using Screw Theorycited by 70
  11. 2024Robo-ABC: Affordance Generalization Beyond Categories via Semantic Correspondence for Robot Manipulationcited by 69
  12. 2021AdaAfford: Learning to Adapt Manipulation Affordance for 3D Articulated Objects via Few-shot Interactionscited by 56
23 more
  1. 2024RAM: Retrieval-Based Affordance Transfer for Generalizable Zero-Shot Robotic Manipulationcited by 55
  2. 2022Equivariant Descriptor Fields: SE(3)-Equivariant Energy-Based Models for End-to-End Visual Robotic Manipulation Learningcited by 54
  3. 2022SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fieldscited by 51
  4. 2023Diffusion-EDFs: Bi-equivariant Denoising Generative Modeling on SE(3) for Visual Robotic Manipulationcited by 51
  5. 2023EquivAct: SIM(3)-Equivariant Visuomotor Policies beyond Rigid Object Manipulationcited by 37
  6. 2022Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detectioncited by 34
  7. 2023Where2Explore: Few-shot Affordance Learning for Unseen Novel Categories of Articulated Objectscited by 33
  8. 2022Neural Grasp Distance Fields for Robot Manipulationcited by 31
  9. 2024DiffAssemble: A Unified Graph-Diffusion Model for 2D and 3D Reassemblycited by 31
  10. 2022USEEK: Unsupervised SE(3)-Equivariant 3D Keypoints for Generalizable Manipulationcited by 27
  11. 2023Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assemblycited by 27
  12. 2023Learning Foresightful Dense Visual Affordance for Deformable Object Manipulationcited by 25
  13. 2023Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusionscited by 23
  14. 2024UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondencecited by 22
  15. 2022DualAfford: Learning Collaborative Visual Affordance for Dual-gripper Manipulationcited by 18
  16. 2023EFEM: Equivariant Neural Field Expectation Maximization for 3D Object Segmentation Without Scene Supervisioncited by 18
  17. 2024UniDoorManip: Learning Universal Door Manipulation Policy Over Large-scale and Diverse Door Manipulation Environmentscited by 14
  18. 2024Articulated Object Manipulation with Coarse-to-fine Affordance for Mitigating the Effect of Point Cloud Noisecited by 12
  19. 2024NaturalVLM: Leveraging Fine-grained Natural Language for Affordance-Guided Visual Manipulationcited by 10
  20. 2023Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representationscited by 5
  21. 2023SAGE: Bridging Semantic and Actionable Parts for GEneralizable Manipulation of Articulated Objectscited by 5
  22. 2024RPMArt: Towards Robust Perception and Manipulation for Articulated Objectscited by 5
  23. 2024Broadcasting Support Relations Recursively from Local Dynamics for Object Retrieval in Clutterscited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 4.

6 papers.

  1. 2024ET-SEED: Efficient Trajectory-Level SE(3) Equivariant Diffusion Policycited by 24
  2. 2026AffordanceVLA: A Vision-Language-Action Model Empowering Action Generation through Affordance-Aware Understandingcited by 8
  3. 2026Diffusion Model as a Generalist Segmentation Learnercited by 4
  4. 2026Learning Part-Aware Dense 3D Feature Field for Generalizable Articulated Object Manipulationcited by 3
  5. 2025Eq.Bot: Enhance Robotic Manipulation Learning via Group Equivariant Canonicalizationcited by 1
  6. 2026SUGAR: A Scalable Human-Video-Driven Generalizable Humanoid Loco-Manipulation Learning Frameworkcited by 1