通过分解物体部件，实现单次演示后对陌生形状的技能迁移。
One-Shot Cross-Geometry Skill Transfer through Part Decomposition
- 将物体拆解为语义部件，精准迁移交互点
- 单次演示即可在模拟与真实环境中成功迁移多种技能
- 适用于复杂形状物体，提升跨几何泛化能力
给定一次示范，机器人应能将技能泛化到任意遇到的物体，但现有方法在面对陌生形状时常失效。受组合建模提升迁移效果的启发，我们提出一种新方法：将物体分解为其语义部件，并利用数据高效的生成式形状模型，将示范物体部件上的交互点准确转移到新物体上。我们自主构建目标函数，优化技能相关部件上的点对齐。该方法在模拟与真实环境中的多种技能和物体上，均实现了单次演示的成功迁移，显著优于现有工作，在更广泛的物体几何形态下表现出更强的泛化能力。
原文摘要 · Abstract (English)
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.
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