mlsanityData to 14 Oct 2026

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

arXiv 2602.02402 ↗First posted 2 Feb 2026cs.RO cs.AI cs.CVcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 34 of its 48 references that are in the map. First posted on arXiv 2 Feb 2026.

Builds on

34 of 48 references.

References · 34

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22 more
  1. 2024WonderWorld: Interactive 3D Scene Generation from a Single Imagecited by 202
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  14. 2024A Survey of Embodied Learning for Object-Centric Robotic Manipulationcited by 35
  15. 2024GASP: Gaussian Splatting for Physic-Based Simulationscited by 24
  16. 2023Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Imagecited by 19
  17. 2023Learning 3D Particle-based Simulators from RGB-D Videoscited by 17
  18. 2024Modeling the Real World with High-Density Visual Particle Dynamicscited by 12
  19. 2023Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attentioncited by 10
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 4.

4 papers.