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

2025-03-24

AI Summary

Existing vision-augmentation methods for robot imitation learning rely on stringent prerequisites—such as precise camera calibration or chroma-key green screens—limiting generalizability and deployment practicality. This paper introduces the first end-to-end, plug-and-play robotic scene generation framework that requires no environmental modification. Our approach comprises three key components: (1) constructing the first semantic-level robotic instance segmentation dataset and training a highly generalizable segmentation model; (2) designing a task-aware diffusion-based background generation module that jointly incorporates physical constraints and task semantics; and (3) integrating a lightweight API framework. Given only a single demonstration scene, our method generalizes to six unseen environments, boosting task success rates by over 200%. All datasets, model weights, and toolkits are publicly released.

Technology Category

• Intelligent Robots: Manipulation
• Computer Vision: Diffusion Models for Vision
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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 will be publicly released.

Problem

• Enhancing visual robustness in imitation learning
• Overcoming camera calibration and controlled environment limitations
• Generalizing robot tasks across new scenes efficiently

Innovation

• Plug-and-play robot data augmentation toolkit
• Semantic robot segmentation and background generation
• Physics- and task-aware scene generation