Robot Learning from Any Images

2025-09-26

AI Summary

This paper addresses the challenge of constructing an interactive, physics-enabled robotic simulation environment from a single natural image—without requiring additional hardware, 3D models, or annotations. Methodologically, it introduces RoLA, the first end-to-end framework that recovers physically consistent scenes from monocular images and integrates efficient vision-dynamics modeling to generate high-fidelity, executable visuomotor demonstrations from camera inputs, dataset samples, or even web-sourced images. Key contributions include: (1) the first purely image-driven paradigm for real-time interactive robotic simulation; (2) support for real-to-sim-to-real closed-loop deployment, validated on both robotic arms and humanoid robots; and (3) rapid generation of large-scale, high-quality training data within minutes—substantially improving data efficiency and generalization, and advancing embodied intelligence learning grounded in open-world imagery.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Low Level & Physics-based Vision
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

We introduce RoLA, a framework that transforms any in-the-wild image into an interactive, physics-enabled robotic environment. Unlike previous methods, RoLA operates directly on a single image without requiring additional hardware or digital assets. Our framework democratizes robotic data generation by producing massive visuomotor robotic demonstrations within minutes from a wide range of image sources, including camera captures, robotic datasets, and Internet images. At its core, our approach combines a novel method for single-view physical scene recovery with an efficient visual blending strategy for photorealistic data collection. We demonstrate RoLA's versatility across applications like scalable robotic data generation and augmentation, robot learning from Internet images, and single-image real-to-sim-to-real systems for manipulators and humanoids. Video results are available at https://sihengz02.github.io/RoLA .

Problem

• Transforms any image into interactive robotic environments
• Generates robotic data without hardware or digital assets
• Enables robot learning from diverse image sources

Innovation

• Transforms any image into interactive robotic environment
• Operates directly on single image without additional hardware
• Combines physical scene recovery with visual blending strategy