Genie Sim 3.0 is a high-fidelity simulation platform from AgiBot Innovation (Shanghai) Technology Co., Ltd. for embodied AI. It provides a complete toolchain for environment reconstruction, scene generalization, data collection and automated evaluation of robotics models. Its core module is Genie Sim Benchmark — a standardized tool for the most authoritative evaluation of embodied intelligence.
The platform integrates 3D reconstruction (3D Gaussian Splatting, 3DGS) with a generative visual model to create a high-fidelity simulation environment with precise meshes. Pioneering LLM-based technology enables generating massive amounts of simulation scenes and evaluation configurations in minutes. The evaluation system covers over 200 tasks across more than 100,000 scenarios, building a comprehensive capability profile for models. Genie Sim also opens a synthetic dataset of over 10,000 hours of robotic operation in near-real-world conditions.
Version 3.0 (January 7, 2026) introduced: Isaac Sim update to v5.1.0 and RTX 50-series GPU support, USD and URDF files of the AgiBot Genie G2 robot with whole body control, 3DGS-based scene reconstruction with USD format conversion for Isaac Sim, a synthetic dataset together with a data collection pipeline, and LLM-based features for generating scenarios, task instructions and evaluation configurations. Update v3.1 (April 8, 2026) added Genie Sim World — a multimodal spatial world model generating photorealistic 3D worlds from diverse input types in minutes, new benchmarks for instruction-following and spatial understanding, and RLinf integration (human-in-the-loop + distributed RL).
Genie Sim 3.0 includes four benchmark families: GenieSim-Instruction (10 instruction-following tasks), GenieSim-Robust (12 generalization dimensions — lighting, background, camera noise, control delay), GenieSim-Manipulation (10 manipulation tasks), GenieSim-Sim2Real (8 tasks comparing sim-to-sim, real-to-sim, sim-to-real and real-to-real performance). Reference models tested on the platform: π0.5 (leader), GR00T-N1.6, π0. Discrepancy between simulation and real-world results is less than 10%.
The entire platform — simulation assets, dataset and source code — is fully open-source. Code in source/geniesim and source/data_collection is available under the Mozilla Public License 2.0. The AgibotTech/genie_sim GitHub repository has 1,000+ stars and 92+ forks. Datasets are hosted on HuggingFace (agibot-world/GenieSimAssets) and ModelScope (agibot_world/GenieSim3.0-Dataset). A model trained on synthetic data from Genie Sim exhibits zero-shot sim-to-real transfer with a higher success rate than models trained on real data.
Simulation software is used for modelling, testing, and validating robot behaviours, sensor characteristics, environments, and algorithms without requiring physical hardware. It enables safe, repeatable, and cost-effective development cycles. Common robotics simulators include Gazebo, Isaac Sim (NVIDIA), MuJoCo, PyBullet, and Webots, each offering different trade-offs between physics accuracy, rendering fidelity, and integration with middleware frameworks such as ROS 2.
A Developer Tool is software designed to support the development workflow, including configuration, debugging, testing, monitoring, validation, and integration of robotic and embedded systems. Examples include IDE plugins, visual debuggers, log analysers, hardware-in-the-loop (HIL) test harnesses, and code-generation utilities specific to robotics platforms.
The simulation role describes software that reproduces the physical behavior of a robot and its environment in a computer environment. The simulation component implements: deterministic integration of rigid-body and soft-body dynamics (typically via PhysX, Bullet, MuJoCo, Newton engines), photo-realistic scene rendering (RTX, ray-tracing), sensor models (RGB/D cameras, LiDAR, IMU, F/T), synthetic data generation for training machine learning policies, comparative evaluation of multiple solution variants under identical conditions. Simulation enables massively parallel RL training without physical hardware and is the foundation of the sim-to-real workflow.
The benchmarking and evaluation role describes software responsible for standardized measurement of robot and AI model capabilities. The benchmark component includes: a set of defined tasks (manipulation, locomotion, perception, instruction), automated success and execution quality metrics, test scenarios covering generalization dimensions (lighting, background, camera noise, control delay), reproducible processes for running multiple trials with result aggregation, model leaderboards (e.g. π0.5, GR00T, π0). Modern systems use VLM (Vision-Language Models) for auto-evaluation of complex qualitative criteria inaccessible to simple numerical metrics.
The synthetic data generation role describes software for automated production of large training datasets for robot perception and control models. The component implements: programmed simulation scenarios with parameterized objects and motions, automatic ground-truth labeling (segmentation, bounding boxes, 6-DoF poses, depth maps), demonstration trajectory collection via in-simulation teleoperation or expert automated policies, domain augmentation (lighting, textures, materials) for domain randomization, error-recovery mechanisms for producing correct trials despite failures. Generates data scale unattainable in the physical world — typically 10,000+ hours of trajectories.
The robot learning role describes software for training a robot's control policies and manipulation/locomotion skills using machine learning methods. It covers: reinforcement learning in simulation with sim-to-real transfer, imitation learning and learning from demonstration, training Vision-Language-Action (VLA) models, and fine-tuning robotics foundation models. It typically uses massively parallelized simulation environments (Isaac Lab, MuJoCo) to generate training data, then deploys the trained policies on a physical robot.
AgiBot Genie family of embodied AI software: Genie Studio (one-stop development platform), Genie Sim (simulator), Genie Operator (GO-1 foundation model), Genie Envisioner (GE-1 world model).
AgiBot Genie G2 (production humanoid with USD+URDF in v3.0 + whole body control), AgiBot World Challenge 2025/2026, academic ecosystem testing π0.5 / GR00T-N1.6 / π0 models on GenieSim benchmarks. Genie Sim is published in arXiv paper 2601.02078 (Yin et al., 2026).
AgibotTech/genie_sim GitHub repository: over 1,000 stars and 92 forks (June 2026). Datasets on HuggingFace (agibot-world/GenieSimAssets) and ModelScope (agibot_world/GenieSim3.0-Dataset).
Full installation with assets and dataset requires 200+ GB. Genie Sim is based on NVIDIA Isaac Sim 5.1.0 (Omniverse) and inherits its hardware requirements (RTX-capable GPU for ray-tracing, Linux as preferred OS via Docker).
License family: Weak Copyleft
