# RoboEngine: Plug-and-Play Robot Data Augmentation with Semantic Robot Segmentation and Background Generation

arXiv: https://arxiv.org/abs/2503.18738 · HTML: https://mlsanity.com/paper/2503.18738/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Placed in Robot manipulation imitation learning, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 24 Mar 2025.

## Placed in

- Offline RL and imitation learning from data / [Robot manipulation imitation learning](https://mlsanity.com/offlinerl/robot-manipulation-imitation-learning/)

## Built on by (most cited first, 30 of 41)

- [EgoDex: Learning Dexterous Manipulation from Large-Scale Egocentric Video](https://mlsanity.com/paper/2505.11709/) (2025)
- [Vidar: Embodied Video Diffusion Model for Generalist Manipulation](https://mlsanity.com/paper/2507.12898/) (2025)
- [OmniWorld: A Multi-Domain and Multi-Modal Dataset for 4D World Modeling](https://mlsanity.com/paper/2509.12201/) (2025)
- [GigaWorld-0: World Models as Data Engine to Empower Embodied AI](https://mlsanity.com/paper/2511.19861/) (2025)
- [Novel Demonstration Generation with Gaussian Splatting Enables Robust One-Shot Manipulation](https://mlsanity.com/paper/2504.13175/) (2025)
- [GigaBrain-0: A World Model-Powered Vision-Language-Action Model](https://mlsanity.com/paper/2510.19430/) (2025)
- [EmbodieDreamer: Advancing Real2Sim2Real Transfer for Policy Training via Embodied World Modeling](https://mlsanity.com/paper/2507.05198/) (2025)
- [GigaBrain-0.5M*: a VLA That Learns From World Model-Based Reinforcement Learning](https://mlsanity.com/paper/2602.12099/) (2026)
- [Touch begins where vision ends: Generalizable policies for contact-rich manipulation](https://mlsanity.com/paper/2506.13762/) (2025)
- [PEEK: Guiding and Minimal Image Representations for Zero-Shot Generalization of Robot Manipulation Policies](https://mlsanity.com/paper/2509.18282/) (2025)
- [EMMA: Generalizing Real-World Robot Manipulation via Generative Visual Transfer](https://mlsanity.com/paper/2509.22407/) (2025)
- [Persistent Robot World Models: Stabilizing Multi-Step Rollouts via Reinforcement Learning](https://mlsanity.com/paper/2603.25685/) (2026)
- [AnchorDream: Repurposing Video Diffusion for Embodiment-Aware Robot Data Synthesis](https://mlsanity.com/paper/2512.11797/) (2025)
- [TwinAligner: Visual-Dynamic Alignment Empowers Physics-aware Real2Sim2Real for Robotic Manipulation](https://mlsanity.com/paper/2512.19390/) (2025)
- [Vision-Language-Action in Robotics: A Survey of Datasets, Benchmarks, and Data Engines](https://mlsanity.com/paper/2604.23001/) (2026)
- [Mem-World: Memory-Augmented Action-Conditioned World Models for Persistent Robot Manipulation](https://mlsanity.com/paper/2606.18960/) (2026)
- [RoboTransfer: Controllable Geometry-Consistent Video Diffusion for Manipulation Policy Transfer](https://mlsanity.com/paper/2505.23171/) (2025)
- [Robot Learning from Any Images](https://mlsanity.com/paper/2509.22970/) (2025)
- [RoboVIP: Multi-View Video Generation with Visual Identity Prompting Augments Robot Manipulation](https://mlsanity.com/paper/2601.05241/) (2026)
- [D-CODA: Diffusion for Coordinated Dual-Arm Data Augmentation](https://mlsanity.com/paper/2505.04860/) (2025)
- [IG-RFT: An Interaction-Guided RL Framework for VLA Models in Long-Horizon Robotic Manipulation](https://mlsanity.com/paper/2602.20715/) (2026)
- [Visual Generation in the New Era: An Evolution from Atomic Mapping to Agentic World Modeling](https://mlsanity.com/paper/2604.28185/) (2026)
- [Cloak: Zero-Shot Cross-Embodiment Manipulation by Masking the End-Effector from the VLA](https://mlsanity.com/paper/2606.22836/) (2026)
- [GAE: Unleashing Physical Potential of VLM with Generalizable Action Expert](https://mlsanity.com/paper/2510.03896/) (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)
- [Fidelity-Aware Data Composition for Robust Robot Generalization](https://mlsanity.com/paper/2509.24797/) (2025)
- [ST-VLA: Enabling 4D-Aware Spatiotemporal Understanding for General Robot Manipulation](https://mlsanity.com/paper/2603.13788/) (2026)
- [RoboDream: Compositional World Models for Scalable Robot Data Synthesis](https://mlsanity.com/paper/2606.02577/) (2026)
- [RobotSeg: A Model and Dataset for Segmenting Robots in Image and Video](https://mlsanity.com/paper/2511.22950/) (2025)

## Abstract (arXiv)

Visual augmentation has become a crucial technique for enhancing the visual robustness of imitation learning. However, existing methods are often limited by prerequisites such as camera calibration or the need for controlled environments (e.g., green screen setups). In this work, we introduce RoboEngine, the first plug-and-play visual robot data augmentation toolkit. For the first time, users can effortlessly generate physics- and task-aware robot scenes with just a few lines of code. To achieve this, we present a novel robot scene segmentation dataset, a generalizable high-quality robot segmentation model, and a fine-tuned background generation model, which together form the core components of the out-of-the-box toolkit. Using RoboEngine, we demonstrate the ability to generalize robot manipulation tasks across six entirely new scenes, based solely on demonstrations collected from a single scene, achieving a more than 200% performance improvement compared to the no-augmentation baseline. All datasets, model weights, and the toolkit are released https://roboengine.github.io/