拡散方向場による物体中心タスク表現と転移
Object-centric Task Representation and Transfer using Diffused Orientation Fields
曲面物体上でのタスク転移を可能にするため、拡散方向場（DOF）を用いて局所参照枠を滑らかに表現し、スパースなキーポイント対応でタスクを転移する手法を提案した。
詳しい要約
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著者: Cem Bilaloglu, Tobias Löw, Sylvain Calinon
分類: cs.RO
原文アブストラクト
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 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 tasks requiring continuous physical interaction such as inspection, slicing, and peeling across varied objects. We provide our open-source codes at our website https://github.com/idiap/diffused_fields_robotics
