SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912868869341184 |
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| author | Huang, Mu Wang, Hui Ren, Kerui Xu, Linning Zhou, Yunsong Yu, Mulin Dai, Bo Pang, Jiangmiao |
| author_facet | Huang, Mu Wang, Hui Ren, Kerui Xu, Linning Zhou, Yunsong Yu, Mulin Dai, Bo Pang, Jiangmiao |
| contents | Simulating deformable objects under rich interactions remains a fundamental challenge for real-to-sim robot manipulation, with dynamics jointly driven by environmental effects and robot actions. Existing simulators rely on predefined physics or data-driven dynamics without robot-conditioned control, limiting accuracy, stability, and generalization. This paper presents SoMA, a 3D Gaussian Splat simulator for soft-body manipulation. SoMA couples deformable dynamics, environmental forces, and robot joint actions in a unified latent neural space for end-to-end real-to-sim simulation. Modeling interactions over learned Gaussian splats enables controllable, stable long-horizon manipulation and generalization beyond observed trajectories without predefined physical models. SoMA improves resimulation accuracy and generalization on real-world robot manipulation by 20%, enabling stable simulation of complex tasks such as long-horizon cloth folding. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_02402 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation Huang, Mu Wang, Hui Ren, Kerui Xu, Linning Zhou, Yunsong Yu, Mulin Dai, Bo Pang, Jiangmiao Robotics Artificial Intelligence Computer Vision and Pattern Recognition Applied Physics 68T05, 68T40, 68U05 I.2.9; I.2.6; I.2.10 Simulating deformable objects under rich interactions remains a fundamental challenge for real-to-sim robot manipulation, with dynamics jointly driven by environmental effects and robot actions. Existing simulators rely on predefined physics or data-driven dynamics without robot-conditioned control, limiting accuracy, stability, and generalization. This paper presents SoMA, a 3D Gaussian Splat simulator for soft-body manipulation. SoMA couples deformable dynamics, environmental forces, and robot joint actions in a unified latent neural space for end-to-end real-to-sim simulation. Modeling interactions over learned Gaussian splats enables controllable, stable long-horizon manipulation and generalization beyond observed trajectories without predefined physical models. SoMA improves resimulation accuracy and generalization on real-world robot manipulation by 20%, enabling stable simulation of complex tasks such as long-horizon cloth folding. |
| title | SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Applied Physics 68T05, 68T40, 68U05 I.2.9; I.2.6; I.2.10 |
| url | https://arxiv.org/abs/2602.02402 |