SemAnCorr: Semantic Anchored Correspondence for Zero-Shot Manipulation Skill Transfer

2026-07-30 · 15:41 UTC ·Xiaoxiang Dong, William Baron, Hongyi Chen... ·1 min read

Brief

SemAnCorr tackles cross-instance manipulation transfer by producing dense, semantically consistent and geometrically coherent correspondences without training. It selects semantic anchor regions through joint pose–correspondence optimization and spreads those constraints via functional maps, outperforming recent baselines on a PartNet-Mobility benchmark (90.8% semantic accuracy) and enabling substantially more reliable single-demonstration zero-shot manipulation on real unseen objects.

Why it matters

SemAnCorr is a training-free framework that establishes dense correspondences by selecting semantically consistent anchor regions via joint pose–correspondence optimization and propagating constraints over the object surface using functional maps to preserve semantic consistency and geometric coherence.

Key details

On a dense correspondence benchmark built on PartNet-Mobility, SemAnCorr achieves 90.8% semantic accuracy and improves geometric coherence over recent state-of-the-art baselines; in real-world tests a single demonstration yields substantially more reliable zero-shot manipulation transfer to unseen objects.

ArXiv preprint by Xiaoxiang Dong, William Baron, Hongyi Chen, Uksang Yoo, Jeffrey Ichnowski, and Weiming Zhi (posted 2026-07-30); project page and videos available at https://semancorr.github.io.

Abstract

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 .