Affordance Transfer Across Object Instances via Semantically Anchored Functional Map

• URL: http://arxiv.org/abs/2602.14874v1
• Date: Mon, 16 Feb 2026 16:04:47 GMT
• Title: Affordance Transfer Across Object Instances via Semantically Anchored Functional Map
• Abstract summary: 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.
• Score: 4.786936073180585
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional learning from demonstration (LfD) generally demands a cumbersome collection of physical demonstrations, which can be time-consuming and challenging to scale. Recent advances show that robots can instead learn from human videos by extracting interaction cues without direct robot involvement. However, a fundamental challenge remains: how to generalize demonstrated interactions across different object instances that share similar functionality but vary significantly in geometry. In this work, we propose \emph{Semantic Anchored Functional Maps} (SemFM), a framework for transferring affordances across objects from a single visual demonstration. Starting from a coarse mesh reconstructed from an image, our method identifies semantically corresponding functional regions between objects, selects mutually exclusive semantic anchors, and propagates these constraints over the surface using a functional map to obtain a dense, semantically consistent correspondence. This enables demonstrated interaction regions to be transferred across geometrically diverse objects in a lightweight and interpretable manner. Experiments on synthetic object categories and real-world robotic manipulation tasks show that our approach enables accurate affordance transfer with modest computational cost, making it well-suited for practical robotic perception-to-action pipelines.

Related papers

• GIRAF: Towards Generalizable Human Interactions with Articulated Objects [50.52795413484619]
  We present a text-conditioned diffusion model that addresses the challenge of Synthesizing realistic full-body human interactions with articulated objects.<n>Our method demonstrated strong generalization to unseen object configurations, surpassing current state-of-the-art methods.
  arXiv  Detail & Related papers  (2026-07-08T19:30:44Z)
• GRAFT: Graph-Based Affordance Transfer via Part Correspondence [15.954352116733013]
  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.
  arXiv  Detail & Related papers  (2026-06-23T23:58:57Z)
• ArtiSG: Functional 3D Scene Graph Construction via Human-demonstrated Articulated Objects Manipulation [51.54082859171464]
  ArtiSG is a framework that constructs functional 3D scene graphs by encoding human demonstrations into structured robotic memory.<n>We show that ArtiSG significantly outperforms baselines in functional element recall and articulation estimation precision.
  arXiv  Detail & Related papers  (2025-12-31T13:10:40Z)
• Learning Video Generation for Robotic Manipulation with Collaborative Trajectory Control [72.00655365269]
  We present RoboMaster, a novel framework that models inter-object dynamics through a collaborative trajectory formulation.<n>Unlike prior methods that decompose objects, our core is to decompose the interaction process into three sub-stages: pre-interaction, interaction, and post-interaction.<n>Our method outperforms existing approaches, establishing new state-of-the-art performance in trajectory-controlled video generation for robotic manipulation.
  arXiv  Detail & Related papers  (2025-06-02T17:57:06Z)
• IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments [56.85804719947]
  We present IAAO, a framework that builds an explicit 3D model for intelligent agents to gain understanding of articulated objects in their environment through interaction.<n>We first build hierarchical features and label fields for each object state using 3D Gaussian Splatting (3DGS) by distilling mask features and view-consistent labels from multi-view images.<n>We then perform object- and part-level queries on the 3D Gaussian primitives to identify static and articulated elements, estimating global transformations and local articulation parameters along with affordances.
  arXiv  Detail & Related papers  (2025-04-09T12:36:48Z)
• SIGHT: Synthesizing Image-Text Conditioned and Geometry-Guided 3D Hand-Object Trajectories [124.24041272390954]
  Modeling hand-object interaction priors holds significant potential to advance robotic and embodied AI systems.<n>We introduce SIGHT, a novel task focused on generating realistic and physically plausible 3D hand-object interaction trajectories from a single image.<n>We propose SIGHT-Fusion, a novel diffusion-based image-text conditioned generative model that tackles this task by retrieving the most similar 3D object mesh from a database.
  arXiv  Detail & Related papers  (2025-03-28T20:53:20Z)
• Geometrically-Aware One-Shot Skill Transfer of Category-Level Objects [18.978751760636563]
  We propose a new skill transfer framework, which enables a robot to transfer complex object manipulation skills and constraints from a single human demonstration.<n>Our approach addresses the challenge of skill acquisition and task execution by deriving geometric representations from demonstrations focusing on object-centric interactions.<n>We validate the effectiveness and adaptability of our approach through extensive experiments, demonstrating successful skill transfer and task execution in diverse real-world environments without requiring additional training.
  arXiv  Detail & Related papers  (2025-03-19T16:10:17Z)
• Visual-Geometric Collaborative Guidance for Affordance Learning [63.038406948791454]
  We propose a visual-geometric collaborative guided affordance learning network that incorporates visual and geometric cues. Our method outperforms the representative models regarding objective metrics and visual quality.
  arXiv  Detail & Related papers  (2024-10-15T07:35:51Z)
• GEARS: Local Geometry-aware Hand-object Interaction Synthesis [38.75942505771009]
  We introduce a novel joint-centered sensor designed to reason about local object geometry near potential interaction regions. As an important step towards mitigating the learning complexity, we transform the points from global frame to template hand frame and use a shared module to process sensor features of each individual joint. This is followed by a perceptual-temporal transformer network aimed at capturing correlation among the joints in different dimensions.
  arXiv  Detail & Related papers  (2024-04-02T09:18:52Z)
• Grasp Transfer based on Self-Aligning Implicit Representations of Local Surfaces [10.602143478315861]
  This work addresses the problem of transferring a grasp experience or a demonstration to a novel object that shares shape similarities with objects the robot has previously encountered. We employ a single expert grasp demonstration to learn an implicit local surface representation model from a small dataset of object meshes. At inference time, this model is used to transfer grasps to novel objects by identifying the most geometrically similar surfaces to the one on which the expert grasp is demonstrated.
  arXiv  Detail & Related papers  (2023-08-15T14:33:17Z)
• Detection and Physical Interaction with Deformable Linear Objects [10.707804359932604]
  Deformable linear objects (e.g., cables, ropes, and threads) commonly appear in our everyday lives. There have already been successful methods to model and track deformable linear objects. We present our work on using the method for tasks such as routing and manipulation with the ground and aerial robots.
  arXiv  Detail & Related papers  (2022-05-17T01:17:21Z)
• Discovering Objects that Can Move [55.743225595012966]
  We study the problem of object discovery -- separating objects from the background without manual labels. Existing approaches utilize appearance cues, such as color, texture, and location, to group pixels into object-like regions. We choose to focus on dynamic objects -- entities that can move independently in the world.
  arXiv  Detail & Related papers  (2022-03-18T21:13:56Z)

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.