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

• URL: http://arxiv.org/abs/2602.02402v1
• Date: Mon, 02 Feb 2026 17:59:31 GMT
• Title: SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation
• Authors: Mu Huang, Hui Wang, Kerui Ren, Linning Xu, Yunsong Zhou, Mulin Yu, Bo Dai, Jiangmiao Pang,
• Abstract summary: Existing simulators rely on predefined physics or data-driven dynamics without robot-conditioned control.<n>This paper presents SoMA, a 3D Gaussian Splat simulator for soft-body manipulation.
• Score: 33.51083722346151
• License: http://creativecommons.org/licenses/by/4.0/
• 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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