Abstract

Curved objects pose a fundamental challenge for skill transfer in robotics: unlike planar surfaces, they do not admit a global reference frame. As a result, task-relevant directions—such as “toward” or “along” the surface—vary with position and geometry, making object-centric tasks difficult to transfer across shapes. To address this, we introduce an approach using Diffused Orientation Fields (DOF), a smooth representation of local reference frames, for transfer learning of tasks across curved objects. By expressing manipulation tasks in these smoothly varying local frames, we reduce the problem of transferring tasks across different curved objects to establishing sparse keypoint correspondences. DOF is computed online from raw point cloud data using diffusion processes governed by partial differential equations, conditioned on keypoints. We evaluate DOF under geometric, topological, and localization perturbations, and demonstrate successful transfer of contact-rich tasks such as inspection, slicing, and peeling across varied objects.

Bibtex reference

@article{Bilaloglu25arXiv,
	author={Bilaloglu, C. and L{\"o}w, T. and Calinon, S.},
	title={Object-centric Task Representation and Transfer using Diffused Orientation Fields},
	journal={arXiv:2511.18563},
	year={2025}
}

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