发表机构
Robotics Institute, Carnegie Mellon University; College of Connected Computing, Vanderbilt University; College of Engineering, University of California, Berkeley; School of Computer Science, The University of Sydney（卡内基梅隆大学机器人研究所; 范德堡大学互联计算学院; 加州大学伯克利分校工程学院; 悉尼大学计算机学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
SemAnCorr是一种无需训练的机器人密集对应框架，通过联合优化建立语义与几何一致的对应关系，在PartNet-Mobility基准上获90.8%语义准确率，可实现可靠的零样本操作技能迁移。
AI 中文摘要
在机器人学习中，跨具有相同功能但几何形状不同的物体实例迁移操作技能仍是一项基础挑战。尽管近期的对应方法利用了密集视觉描述符和3D特征场，但最近邻特征匹配常产生空间不一致的对应关系，无法恢复可靠技能迁移所需的局部几何框架。我们提出SemAnCorr，这是一种无需训练的框架，通过联合位姿-对应优化选择语义一致的锚定区域，再利用功能映射将这些约束传播到物体表面，从而建立密集对应关系。所得对应关系同时保留语义一致性和几何连贯性，使以物体为中心的操作技能能够跨几何形状多样的实例迁移。我们在基于PartNet-Mobility构建的密集对应基准上评估SemAnCorr，在基准评估中达到90.8%的语义准确率，同时相比近期的最先进基线提升了几何连贯性。最后，我们证明这些改进直接转化为实际操作性能：利用单个演示，SemAnCorr相比现有对应方法能实现更可靠的零样本操作技能迁移至此前未见过的物体。视频和额外可视化内容可在[this https URL]获取。
英文摘要
Transferring manipulation skills across object instances that share functionality but differ in geometry remains a fundamental challenge in robot learning. While recent correspondence methods leverage dense visual descriptors and 3D feature fields, nearest-neighbor feature matching often produces spatially incoherent correspondences that fail to recover the local geometric frames required for reliable skill transfer. We introduce SemAnCorr, a training-free framework that establishes dense correspondence by selecting semantically consistent anchor regions through joint pose-correspondence optimization and propagating these constraints over the object surface using functional maps. The resulting correspondences preserve both semantic consistency and geometric coherence, enabling object-centric manipulation skills to transfer across geometrically diverse instances. We evaluate SemAnCorr on a dense correspondence benchmark built on PartNet-Mobility, achieving 90.8% semantic accuracy in our benchmark evaluation while improving geometric coherence over recent state-of-the-art baselines. Finally, we show that these improvements translate directly into real-world manipulation performance: using a single demonstration, SemAnCorr enables substantially more reliable zero-shot manipulation skill transfer to previously unseen objects than existing correspondence methods. Videos and additional visualizations are available at [https://semancorr.github.io](https://semancorr.github.io) .
