Effectively manipulating articulated objects in household scenarios is a crucial step toward achieving general embodied artificial intelligence. Mainstream research in 3D vision has primarily focused on manipulation through depth perception and pose detection. However, in real-world environments, these methods often face challenges due to imperfect depth perception, such as with transparent lids and reflective handles. Moreover, they generally lack the diversity in part-based interactions required for flexible and adaptable manipulation. To address these challenges, we introduced a large-scale part-centric dataset for articulated object manipulation that features both photo-realistic material randomizations and detailed annotations of part-oriented, scene-level actionable interaction poses. We evaluated the effectiveness of our dataset by integrating it with several state-of-the-art methods for depth estimation and interaction pose prediction. Additionally, we proposed a novel modular framework that delivers superior and robust performance for generalizable articulated object manipulation. Our extensive experiments demonstrate that our dataset significantly improves the performance of depth perception and actionable interaction pose prediction in both simulation and real-world scenarios.

本研究解决了在家庭场景中操控关节物体的挑战，特别是在现有方法面对深度感知不足时的局限。通过构建一个大规模的部件中心数据集，提供了照片真实感的材料随机化和可操作的交互姿势注释，显著提升了深度感知和交互姿势预测的性能，推动了普适化的关节物体操控研究。

GAPartManip：一个大规模部件中心数据集用于材料无关的关节物体操控