One-Shot Cross-Geometry Skill Transfer through Part Decomposition

• URL: http://arxiv.org/abs/2604.15455v1
• Date: Thu, 16 Apr 2026 18:16:49 GMT
• Title: One-Shot Cross-Geometry Skill Transfer through Part Decomposition
• Abstract summary: Given a demonstration, a robot should be able to generalize a skill to any object it encounters.<n>Existing approaches to skill transfer often fail to adapt to objects with unfamiliar shapes.<n>We propose a method for improving transfer by decomposing objects into their constituent semantic parts.
• Score: 12.531440065339458
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Given a demonstration, a robot should be able to generalize a skill to any object it encounters-but existing approaches to skill transfer often fail to adapt to objects with unfamiliar shapes. Motivated by examples of improved transfer from compositional modeling, we propose a method for improving transfer by decomposing objects into their constituent semantic parts. We leverage data-efficient generative shape models to accurately transfer interaction points from the parts of a demonstration object to a novel object. We autonomously construct an objective to optimize the alignment of those points on skill-relevant object parts. Our method generalizes to a wider range of object geometries than existing work, and achieves successful one-shot transfer for a range of skills and objects from a single demonstration, in both simulated and real environments.

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)
• 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)
• 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)
• Zero-Shot Object-Centric Representation Learning [72.43369950684057]
  We study current object-centric methods through the lens of zero-shot generalization. We introduce a benchmark comprising eight different synthetic and real-world datasets. We find that training on diverse real-world images improves transferability to unseen scenarios.
  arXiv  Detail & Related papers  (2024-08-17T10:37:07Z)
• Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations [8.130629735939895]
  We introduce a novel framework that disentangles the part motion of articulated objects by predicting the transformation matrix of points on the part surface. Our proposed framework is generic to different kinds of joint motions in that the transformation matrix can model diverse kinds of joint motions in the space.
  arXiv  Detail & Related papers  (2023-11-21T07:54:40Z)
• ROAM: Robust and Object-Aware Motion Generation Using Neural Pose Descriptors [73.26004792375556]
  This paper shows that robustness and generalisation to novel scene objects in 3D object-aware character synthesis can be achieved by training a motion model with as few as one reference object. We leverage an implicit feature representation trained on object-only datasets, which encodes an SE(3)-equivariant descriptor field around the object. We demonstrate substantial improvements in 3D virtual character motion and interaction quality and robustness to scenarios with unseen objects.
  arXiv  Detail & Related papers  (2023-08-24T17:59:51Z)
• 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)
• Plug and Play, Model-Based Reinforcement Learning [60.813074750879615]
  We introduce an object-based representation that allows zero-shot integration of new objects from known object classes. This is achieved by representing the global transition dynamics as a union of local transition functions. Experiments show that our representation can achieve sample-efficiency in a variety of set-ups.
  arXiv  Detail & Related papers  (2021-08-20T01:20:15Z)

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.