# Novel Demonstration Generation with Gaussian Splatting Enables Robust One-Shot Manipulation

arXiv: https://arxiv.org/abs/2504.13175 · HTML: https://mlsanity.com/paper/2504.13175/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Apr 2025.

## Built on by (most cited first, 30 of 48)

- [GraspVLA: a Grasping Foundation Model Pre-trained on Billion-scale Synthetic Action Data](https://mlsanity.com/paper/2505.03233/) (2025)
- [Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions](https://mlsanity.com/paper/2511.04665/) (2025)
- [GSWorld: Closed-Loop Photo-Realistic Simulation Suite for Robotic Manipulation](https://mlsanity.com/paper/2510.20813/) (2025)
- [StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision](https://mlsanity.com/paper/2512.21970/) (2025)
- [Do You Know Where Your Camera Is? View-Invariant Policy Learning with Camera Conditioning](https://mlsanity.com/paper/2510.02268/) (2025)
- [ManipDreamer3D : Synthesizing Plausible Robotic Manipulation Video with Occupancy-aware 3D Trajectory](https://mlsanity.com/paper/2509.05314/) (2025)
- [EMMA: Generalizing Real-World Robot Manipulation via Generative Visual Transfer](https://mlsanity.com/paper/2509.22407/) (2025)
- [VGGT-DP: Generalizable Robot Control via Vision Foundation Models](https://mlsanity.com/paper/2509.18778/) (2025)
- [OXE-AugE: A Large-Scale Robot Augmentation of OXE for Scaling Cross-Embodiment Policy Learning](https://mlsanity.com/paper/2512.13100/) (2025)
- [TwinAligner: Visual-Dynamic Alignment Empowers Physics-aware Real2Sim2Real for Robotic Manipulation](https://mlsanity.com/paper/2512.19390/) (2025)
- [RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation](https://mlsanity.com/paper/2601.05241/) (2026)
- [Reconstruction by Generation: 3D Multi-Object Scene Reconstruction from Sparse Observations](https://mlsanity.com/paper/2604.27106/) (2026)
- [Learning Primitive Embodied World Models: Towards Scalable Robotic Learning](https://mlsanity.com/paper/2508.20840/) (2025)
- [See Once, Then Act: Vision-Language-Action Model with Task Learning from One-Shot Video Demonstrations](https://mlsanity.com/paper/2512.07582/) (2025)
- [TwinRL: Digital Twin-Driven Reinforcement Learning for Real-World Robotic Manipulation](https://mlsanity.com/paper/2602.09023/) (2026)
- [ViTaS: Visual Tactile Soft Fusion Contrastive Learning for Visuomotor Learning](https://mlsanity.com/paper/2602.11643/) (2026)
- [ViDAR: Video Diffusion-Aware 4D Reconstruction From Monocular Inputs](https://mlsanity.com/paper/2506.18792/) (2025)
- [OMP: One-step Meanflow Policy with Directional Alignment](https://mlsanity.com/paper/2512.19347/) (2025)
- [Real2Edit2Real: Generating Robotic Demonstrations via a 3D Control Interface](https://mlsanity.com/paper/2512.19402/) (2025)
- [CRAFT: Video Diffusion for Bimanual Robot Data Generation](https://mlsanity.com/paper/2604.03552/) (2026)
- [ROPA: Synthetic Robot Pose Generation for RGB-D Bimanual Data Augmentation](https://mlsanity.com/paper/2509.19454/) (2025)
- [D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping](https://mlsanity.com/paper/2603.01151/) (2026)
- [RoboDream: Compositional World Models for Scalable Robot Data Synthesis](https://mlsanity.com/paper/2606.02577/) (2026)
- [IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion](https://mlsanity.com/paper/2508.13153/) (2025)
- [Learning in ImaginationLand: Omnidirectional Policies through 3D Generative Models (OP-Gen)](https://mlsanity.com/paper/2509.06191/) (2025)
- [Observer-Actor: Active Vision Imitation Learning with Sparse-View Gaussian Splatting](https://mlsanity.com/paper/2511.18140/) (2025)
- [AOMGen: Photoreal, Physics-Consistent Demonstration Generation for Articulated Object Manipulation](https://mlsanity.com/paper/2512.18396/) (2025)
- [M^3: Dense Matching Meets Multi-View Foundation Models for Monocular Gaussian Splatting SLAM](https://mlsanity.com/paper/2603.16844/) (2026)
- [An Efficient Metric for Data Quality Measurement in Imitation Learning](https://mlsanity.com/paper/2605.01544/) (2026)
- [DynamicManip: Enabling Dynamic Manipulation from a Single Static Demonstration](https://mlsanity.com/paper/2608.01452/) (2026)

## Abstract (arXiv)

Visuomotor policies learned from teleoperated demonstrations face challenges such as lengthy data collection, high costs, and limited data diversity. Existing approaches address these issues by augmenting image observations in RGB space or employing Real-to-Sim-to-Real pipelines based on physical simulators. However, the former is constrained to 2D data augmentation, while the latter suffers from imprecise physical simulation caused by inaccurate geometric reconstruction. This paper introduces RoboSplat, a novel method that generates diverse, visually realistic demonstrations by directly manipulating 3D Gaussians. Specifically, we reconstruct the scene through 3D Gaussian Splatting (3DGS), directly edit the reconstructed scene, and augment data across six types of generalization with five techniques: 3D Gaussian replacement for varying object types, scene appearance, and robot embodiments; equivariant transformations for different object poses; visual attribute editing for various lighting conditions; novel view synthesis for new camera perspectives; and 3D content generation for diverse object types. Comprehensive real-world experiments demonstrate that RoboSplat significantly enhances the generalization of visuomotor policies under diverse disturbances. Notably, while policies trained on hundreds of real-world demonstrations with additional 2D data augmentation achieve an average success rate of 57.2%, RoboSplat attains 87.8% in one-shot settings across six types of generalization in the real world.