ArtGS:3D Gaussian Splatting for Interactive Visual-Physical Modeling and Manipulation of Articulated Objects
Qiaojun Yu, Xibin Yuan, Yu jiang, Junting Chen, Dongzhe Zheng, Ce Hao, Yang You, Yixing Chen, Yao Mu, Liu Liu, Cewu Lu

TL;DR
ArtGS introduces a novel framework that combines 3D Gaussian Splatting with visual-physical reasoning to improve articulated object understanding and manipulation in robotics, demonstrating superior accuracy and success rates.
Contribution
The paper presents ArtGS, a new approach integrating 3D Gaussian Splatting with visual-physical modeling for scalable, generalizable articulated object manipulation.
Findings
Outperforms previous methods in joint estimation accuracy
Achieves higher manipulation success rates in diverse environments
Demonstrates effectiveness in both simulation and real-world tests
Abstract
Articulated object manipulation remains a critical challenge in robotics due to the complex kinematic constraints and the limited physical reasoning of existing methods. In this work, we introduce ArtGS, a novel framework that extends 3D Gaussian Splatting (3DGS) by integrating visual-physical modeling for articulated object understanding and interaction. ArtGS begins with multi-view RGB-D reconstruction, followed by reasoning with a vision-language model (VLM) to extract semantic and structural information, particularly the articulated bones. Through dynamic, differentiable 3DGS-based rendering, ArtGS optimizes the parameters of the articulated bones, ensuring physically consistent motion constraints and enhancing the manipulation policy. By leveraging dynamic Gaussian splatting, cross-embodiment adaptability, and closed-loop optimization, ArtGS establishes a new framework for…
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