GRAFT: Graph-Based Affordance Transfer via Part Correspondence

• URL: http://arxiv.org/abs/2606.25241v1
• Date: Tue, 23 Jun 2026 23:58:57 GMT
• Title: GRAFT: Graph-Based Affordance Transfer via Part Correspondence
• Abstract summary: Generalizing robotic manipulation to unseen objects remains challenging, as learning-based approaches require many demonstrations and fail in few-shot settings.<n>We propose GRAFT, a geometry-aware correspondence framework for zero-shot manipulation transfer using only one demonstration per object.<n>For an unseen object, our method first retrieves the most functionally and geometrically similar instance from the demonstration buffer with aligned functional parts, and finally propagates the contact points through point-wise correspondence.
• Score: 15.954352116733013
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Generalizing robotic manipulation to unseen objects remains challenging, as learning-based approaches require many demonstrations and fail in few-shot settings. Prior work transfers affordances through semantic retrieval, but semantics alone neglect geometric similarity, which is critical for manipulation. We propose GRAFT, a geometry-aware correspondence framework for zero-shot manipulation transfer using only one demonstration per object. Objects are represented as part-based graphs, where part-level descriptors support global instance retrieval and part correspondence, and vertex-level descriptors enable fine-grained contact point matching. For an unseen object, our method first retrieves the most functionally and geometrically similar instance from the demonstration buffer with aligned functional parts, and finally propagates the contact points through point-wise correspondence.

Related papers

• SemAnCorr: Semantic Anchored Correspondence for Zero-Shot Manipulation Skill Transfer [12.085313110793734]
  SemAnCorr is a training-free framework that establishes dense correspondence.<n>We show that SemAnCorr achieves 90.8% semantic accuracy in our benchmark evaluation.<n>We show that these improvements translate directly into real-world manipulation performance.
  arXiv  Detail & Related papers  (2026-07-30T15:41:39Z)
• Affordance Transfer Across Object Instances via Semantically Anchored Functional Map [4.786936073180585]
  We propose emphSemantic Anchored Functional Maps (SemFM), a framework for transferring affordances across objects from a single visual demonstration.<n>Our method identifies semantically corresponding functional regions between objects, selects mutually exclusive semantic anchors, and propagates these constraints over the surface.<n>Experiments on synthetic object categories and real-world robotic manipulation tasks show that our approach enables accurate affordance transfer with modest computational cost.
  arXiv  Detail & Related papers  (2026-02-16T16:04:47Z)
• PartHOI: Part-based Hand-Object Interaction Transfer via Generalized Cylinders [15.1049019475729]
  Learning-based methods to understand and model hand-object interactions (HOI) require a large amount of high-quality HOI data.<n>One way to create HOI data is to transfer hand poses from a source object to another based on the objects' geometry.<n>PartHOI establishes a robust geometric correspondence between object parts, and enables the transfer of contact points.
  arXiv  Detail & Related papers  (2025-04-29T09:56:29Z)
• Detection Based Part-level Articulated Object Reconstruction from Single RGBD Image [52.11275397911693]
  We propose an end-to-end trainable, cross-category method for reconstructing multiple man-made articulated objects from a single RGBD image.<n>We depart from previous works that rely on learning instance-level latent space, focusing on man-made articulated objects with predefined part counts.<n>Our method successfully reconstructs variously structured multiple instances that previous works cannot handle, and outperforms prior works in shape reconstruction and kinematics estimation.
  arXiv  Detail & Related papers  (2025-04-04T05:08:04Z)
• Aligning Instance-Semantic Sparse Representation towards Unsupervised Object Segmentation and Shape Abstraction with Repeatable Primitives [48.155145581663724]
  Understanding 3D object shapes requires shape representation by object parts abstracted from results of instance and semantic segmentation.<n>We introduce a one-stage, fully unsupervised framework towards semantic-aware shape representation.<n>This framework produces joint instance segmentation, semantic segmentation, and shape abstraction through sparse representation and feature alignment of object parts in a high-dimensional space.
  arXiv  Detail & Related papers  (2025-03-10T05:52:17Z)
• Open-Vocabulary Octree-Graph for 3D Scene Understanding [54.11828083068082]
  Octree-Graph is a novel scene representation for open-vocabulary 3D scene understanding. An adaptive-octree structure is developed that stores semantics and depicts the occupancy of an object adjustably according to its shape.
  arXiv  Detail & Related papers  (2024-11-25T10:14:10Z)
• ICGNet: A Unified Approach for Instance-Centric Grasping [42.92991092305974]
  We introduce an end-to-end architecture for object-centric grasping. We show the effectiveness of the proposed method by extensively evaluating it against state-of-the-art methods on synthetic datasets.
  arXiv  Detail & Related papers  (2024-01-18T12:41:41Z)
• Learning-based Relational Object Matching Across Views [63.63338392484501]
  We propose a learning-based approach which combines local keypoints with novel object-level features for matching object detections between RGB images. We train our object-level matching features based on appearance and inter-frame and cross-frame spatial relations between objects in an associative graph neural network.
  arXiv  Detail & Related papers  (2023-05-03T19:36:51Z)
• Marching-Primitives: Shape Abstraction from Signed Distance Function [29.7543198254021]
  We present a novel method, named Marching-Primitives, to obtain a primitive-based abstraction directly from an SDF. Our method grows geometric primitives iteratively by analyzing the connectivity of voxels. We evaluate the performance of our method on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-03-23T11:42:35Z)
• Continuous Surface Embeddings [76.86259029442624]
  We focus on the task of learning and representing dense correspondences in deformable object categories. We propose a new, learnable image-based representation of dense correspondences. We demonstrate that the proposed approach performs on par or better than the state-of-the-art methods for dense pose estimation for humans.
  arXiv  Detail & Related papers  (2020-11-24T22:52:15Z)
• A Graph-based Interactive Reasoning for Human-Object Interaction Detection [71.50535113279551]
  We present a novel graph-based interactive reasoning model called Interactive Graph (abbr. in-Graph) to infer HOIs. We construct a new framework to assemble in-Graph models for detecting HOIs, namely in-GraphNet. Our framework is end-to-end trainable and free from costly annotations like human pose.
  arXiv  Detail & Related papers  (2020-07-14T09:29:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.