---
license: cc-by-4.0
pipeline_tag: robotics
---

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

SoMA is a 3D Gaussian Splat simulator for soft-body manipulation. It 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.

- **Paper:** [https://arxiv.org/abs/2602.02402](https://arxiv.org/abs/2602.02402)
- **Project Page:** [https://city-super.github.io/SoMA/](https://city-super.github.io/SoMA/)

## Description

SoMA is a Gaussian splat neural simulator that models deformable object dynamics from real-world robot manipulation, enabling action-conditioned, stable long-horizon simulation with high-fidelity, multi-view–consistent rendering. By coupling deformable dynamics, environmental forces, and robot joint actions in a unified latent neural space, 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.

## Citation

If you find this work useful, please cite:

```bibtex
@article{huang2026soma,
  title={SoMA: A Real-to-Sim Neural Simulator for Robotic Soft-body Manipulation},
  author={Huang, Mu and Wang, Hui and Kerui Ren and Linning Xu and Yunsong Zhou and Mulin Yu and Bo Dai and Jiangmiao Pang},
  journal={arXiv preprint arXiv:2602.02402},
  year={2026}
}
```