Real2Sim2Real plays a critical role in robotic arm control and reinforcement learning, yet bridging this gap remains a significant challenge due to the complex physical properties of robots and the objects they manipulate. Existing methods lack a comprehensive solution to accurately reconstruct real-world objects with spatial representations and their associated physics attributes. We propose a Real2Sim pipeline with a hybrid representation model that integrates mesh geometry, 3D Gaussian kernels, and physics attributes to enhance the digital asset representation of robotic arms. This hybrid representation is implemented through a Gaussian-Mesh-Pixel binding technique, which establishes an isomorphic mapping between mesh vertices and Gaussian models. This enables a fully differentiable rendering pipeline that can be optimized through numerical solvers, achieves high-fidelity rendering via Gaussian Splatting, and facilitates physically plausible simulation of the robotic arm's interaction with its environment using mesh-based methods. The code,full presentation and datasets will be made publicly available at our website https://robostudioapp.com
@article{arxiv.2408.14873,
title = {Robo-GS: A Physics Consistent Spatial-Temporal Model for Robotic Arm with Hybrid Representation},
author = {Haozhe Lou and Yurong Liu and Yike Pan and Yiran Geng and Jianteng Chen and Wenlong Ma and Chenglong Li and Lin Wang and Hengzhen Feng and Lu Shi and Liyi Luo and Yongliang Shi},
journal= {arXiv preprint arXiv:2408.14873},
year = {2025}
}