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GRS: Generating Robotic Simulation Tasks from Real-World Images

robotics › sim-to-real

📄 Abstract

Abstract: We introduce GRS (Generating Robotic Simulation tasks), a system addressing real-to-sim for robotic simulations. GRS creates digital twin simulations from single RGB-D observations with solvable tasks for virtual agent training. Using vision-language models (VLMs), our pipeline operates in three stages: 1) scene comprehension with SAM2 for segmentation and object description, 2) matching objects with simulation-ready assets, and 3) generating appropriate tasks. We ensure simulation-task alignment through generated test suites and introduce a router that iteratively refines both simulation and test code. Experiments demonstrate our system's effectiveness in object correspondence and task environment generation through our novel router mechanism.

Authors (6)

Alex Zook

Fan-Yun Sun

Josef Spjut

Valts Blukis

Stan Birchfield

Jonathan Tremblay

Submitted

October 20, 2024

arXiv Category

cs.RO

arXiv PDF

Key Contributions

GRS is a system that generates robotic simulation tasks from single RGB-D observations, creating digital twins with solvable tasks for virtual agent training. It uses VLMs and SAM2 for scene comprehension, object matching, and task generation, with a novel router for iterative refinement of simulation and test code.

Business Value

Accelerates the development and training of robotic systems by providing realistic and task-specific simulation environments, reducing the need for expensive and time-consuming real-world data collection.

Paper Metadata

Innovation Type

System/Algorithmic

Deployment Feasibility

Moderate. Requires integration of VLMs, segmentation models, and simulation engines. The iterative refinement router adds complexity but improves robustness.

Limitations Addressed

Difficulty in creating realistic and task-relevant robotic simulations,The sim-to-real gap in training robotic agents,Manual effort required for simulation setup and task design

Performance Gains

Automates the creation of robotic simulation tasks,Improves realism and task relevance of simulations,Facilitates sim-to-real transfer

Technical Tags

Robotic SimulationReal-to-SimDigital TwinsVision-Language Models (VLMs)Scene ComprehensionObject CorrespondenceTask GenerationSAM2

Research Topics

RoboticsSimulationSim-to-Real TransferComputer VisionArtificial Intelligence

Methods & Architectures

Three-stage pipeline (scene comprehension, object matching, task generation)Vision-Language Models (VLMs)SAM2 for segmentation and descriptionRouter for iterative refinement Vision-Language Models (VLMs)SAM2

Applications & Tasks

Robotics Simulation Environments Virtual Training Game Development Generating realistic robotic simulations from real-world dataCreating solvable tasks for virtual agentsAligning simulation environments with real-world scenesBridging the sim-to-real gap Generating robotic simulation tasksCreating digital twinsObject correspondenceTask environment generation

Related Fields

RoboticsComputer VisionSimulationArtificial IntelligenceMachine Learning

Keywords

Robotics SimulationSim-to-RealDigital TwinVision-Language ModelsScene UnderstandingTask GenerationSAM2Real-world DataVirtual TrainingRobotics

Academic Context

#Robotics#Simulation#Sim-to-Real Transfer#Computer Vision#Artificial Intelligence

Commercial Potential

Potential Products

Simulation environment generation toolsDigital twin creation platformsRobotic training simulators

Target Industries

RoboticsManufacturingAutomotiveLogisticsGaming

Use Case Examples

Generating a simulation of a warehouse shelf for training a picking robotCreating a virtual environment for testing autonomous driving algorithmsBuilding realistic scenarios for training virtual agents in games

Competitive Edge

Provides a comprehensive system for generating both the environment and the tasks within it from real-world observations, going beyond simple environment reconstruction to enable agent training.

Market Opportunity

Growing market for simulation tools in robotics and AI training.

Revenue Models

Licensing of the GRS systemplatform-as-a-service for simulation generation.

Resource Requirements

Compute Needs

High (VLM inference, segmentation, simulation rendering)

Data Requirements

Single RGB-D observations of real-world scenes

Deployment Constraints

Requires accurate 3D reconstruction and object recognition; performance depends on VLM and segmentation model quality.

Scalability

Scalability depends on the complexity of the scenes and the efficiency of the VLM and simulation engine.

Production Readiness

Maturity Level

Research

Time to Market

2-3 years

Patent Potential

Moderate (novel system architecture and router mechanism)

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