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arXiv · 2602.02402

SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation

Mu Huang·Hui Wang·Kerui Ren·Linning Xu·Yunsong Zhou·Mulin Yu·Bo Dai·Jiangmiao Pang
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Abstract

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.

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Mu Huang, Hui Wang, Kerui Ren, Linning Xu, Yunsong Zhou, Mulin Yu, Bo Dai, Jiangmiao Pang. 2026-02-02. SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation. https://arxiv.org/abs/2602.02402

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