用扩散方向场实现曲面物体间操作任务的精准迁移。
Object-centric Task Representation and Transfer using Diffused Orientation Fields
- 基于扩散过程构建平滑的局部参考系，支持曲面物体上的任务表达。
- 在几何、拓扑和定位扰动下仍能成功迁移连续物理交互任务。
- 适合需要跨形状操作的机器人应用，如检测、切割和剥离。
曲面物体给机器人技能迁移带来根本挑战：与平面不同，它们不具有全局参考系。因此，“朝向”或“沿表面”等任务相关方向随位置和几何形状变化，导致以物体为中心的任务难以跨形状迁移。为此，我们提出使用扩散方向场（DOF），一种平滑的局部参考框架表示方法，用于在曲面物体间进行迁移学习。通过在这些平滑变化的局部框架中表达操作任务，我们将跨曲面任务迁移问题转化为建立稀疏关键点对应关系的问题。DOF通过偏微分方程控制的扩散过程，从原始点云数据在线计算，以关键点为条件。我们在几何、拓扑和定位扰动下评估了DOF，并证明其在多种需连续物理交互的任务（如检查、切割、剥皮）中实现了跨多样化物体的成功迁移。代码已开源，详见 https://github.com/idiap/diffused_fields_robotics。
原文摘要 · Abstract (English)
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
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