| 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} | |
| } | |
| ``` |