Genie Sim 3.0 - An open-source simulation platform launched by AgiBot

What is Genie Sim 3.0

Genie Sim 3.0 is the first large language model-driven open-source simulation platform launched by AgiBot. Built on NVIDIA Isaac Sim, the platform integrates 3D reconstruction and visual generation technologies to achieve digital twin-level high-fidelity simulation environments. Through natural language instructions, developers can generate tens of thousands of scenes within minutes and conduct multi-turn dialogues to optimize scenes. Genie Sim 3.0 has simultaneously open-sourced over ten thousand hours of simulation datasets containing real robot operation scenarios, and constructed a multi-dimensional intelligent evaluation system covering over 100,000 scenarios. The full-process functional loop covers everything from digital asset generation to automatic evaluation, significantly accelerating model training and validation while reducing dependence on physical hardware.

Main Features of Genie Sim 3.0

• High-fidelity simulation environment: Built on NVIDIA Isaac Sim, combining 3D reconstruction and visual generation technologies to provide digital twin-level high-fidelity simulation scenes, covering five major real operation domains including retail, industrial, food service, home, and office.
• LLM-driven scene generation: Through natural language instructions, tens of thousands of simulation scenes can be generated within minutes, with support for multi-turn dialogue scene optimization.
• Large-scale open-source dataset: Open-sources over 10,000 hours of synthetic datasets, covering 200+ tasks and multi-sensor modalities, supporting multi-dimensional variations.
• Multi-dimensional intelligent evaluation system: Constructs an evaluation system with over 100,000 scenes, covering multiple dimensions such as operation skills and cognitive understanding, supporting zero-shot sim-to-real transfer.
• Efficient data collection tools: Provides low-latency teleoperation and automated data programming support, equipped with error recovery mechanisms.
• Zero-shot sim-to-real transfer: After training on simulation data, models demonstrate zero-shot sim-to-real transfer capability, with task success rates superior to models trained on real data.

Technical Principles of Genie Sim 3.0

• High-fidelity environment construction based on NVIDIA Isaac Sim: Genie Sim 3.0 deeply integrates 3D reconstruction and visual generation technologies, relying on the NVIDIA Isaac Sim architecture to create digital twin-level high-fidelity simulation environments.
• First-ever large language model-driven scene generation: Through natural language instructions, the platform can generate tens of thousands of simulation scenes within minutes, supporting multi-turn dialogue for scene optimization.
• 3D Gaussian Splatting (3DGS) technology: Uses 3DGS technology for environment reconstruction, combined with high-resolution RGB images, 360° LiDAR point clouds, and centimeter-level RTK positioning to achieve millimeter-level precise replication of real environments.
• Visual generation and asset generation: Utilizes visual generation models to intelligently synthesize novel view images, enhancing the visual realism of 3D reconstruction, and can quickly generate simulation models with precise meshes from any object through 60-second surround-shot videos.
• Full-process functional loop: Covers the entire process from digital asset generation, scene generalization, data collection to automatic evaluation, significantly accelerating model training and validation.

Genie Sim 3.0 Project Address

• Project official website：https://agibot-world.com/genie-sim
• Github repository：https://github.com/AgibotTech/genie_sim
• arXiv technical paper：https://arxiv.org/pdf/2601.02078

Application Scenarios of Genie Sim 3.0

• Industrial scenario simulation: Deeply integrates real industrial scenarios such as supermarket stocking, logistics sorting, and power inspection, supporting full-process simulation verification from algorithm research and development to industrial application, achieving "zero hardware deployment, fully authentic verification" and reducing R&D costs and verification cycles.
• Embodied AI Research: Provides large-scale simulation datasets, covering more than 200 tasks and tens of thousands of hours, including multi-sensor information and multiple generalization dimensions. Supports generating training datasets based on reconstructed assets and a full-process evaluation system, facilitating the research and development of embodied AI technologies.
• Model Evaluation and Optimization: Builds a multidimensional and comprehensive evaluation system based on more than 100,000 simulation scenarios, uses large language models (LLM) to automatically generate evaluation processes, and leverages vision-language models (VLM) for in-depth evaluation from multiple dimensions such as semantic understanding and spatial reasoning, clearly revealing the boundaries of model capabilities.
• Natural Language-Driven Scene Generation: Through natural language instructions, automatically generates and generalizes thousands of training and testing scenarios within minutes. Generated scenarios support conversational editing, allowing flexible addition or removal of details and layout adjustments, with efficient and convenient operation.
• Data Collection and Augmentation: Provides dual-mode collection tools and automated annotation functions, with an original error recovery mechanism, enabling zero-shot Sim2Real transfer, with task success rates exceeding those of training with real data.