AGIBOT today introduces a major upgrade to its simulation development platform, Genie Sim 3.0 (github.com/AgibotTech/genie_sim), aiming to address three long-standing bottlenecks in embodied AI: environment generation, data scalability, and standardized evaluation.

The open-source materials for Genie Sim 3.0 are available at: github.com/AgibotTech/genie_sim

While recent progress in robotics has been driven by advances in models and algorithms, real-world deployment continues to be constrained by high data collection costs, limited scenario diversity, and fragmented benchmarking standards.

Genie Sim 3.0 is designed to fundamentally reshape this paradigm – integrating scene generation, simulation, data, and evaluation into a unified, reusable infrastructure.

1. Genie Sim World: LLM-Driven 3D Environment Generation

Genie Sim 3.0 introduces a Spatial World Model that allows users to generate fully interactive 3D environments from simple text or image inputs.

Key capabilities include:

• Multimodal input – No manual modeling or hardware setup required. Users can generate diverse environments with minimal input.

• Minute-level scene creation – Neural network inference enables scene generation in minutes, compared to hours in traditional pipelines.

• High-fidelity – Synchronized output of RGB, depth, LiDAR, and other multimodal data ensures seamless alignment with real robot perception.

2. Genie Sim Benchmark: A Comprehensive Evaluation Framework

For the five core capabilities of robot algorithms—instruction understanding, spatial reasoning, atomic skill operation, disturbance adaptation, and training-to-deployment generalization—Genie Sim Benchmark has designed five corresponding task suites which supporting mainstream models such as the GO-2, Pi series, and GR00T series, it provides a multi-dimensional, systematic evaluation of the models' comprehensive performance in complex scenarios.

The framework evaluates five core capabilities of embodied AI systems:

• Instruction Following (GenieSim-Instruction) – Measures alignment between natural language instructions and robot behavior.

• Spatial Understanding (GenieSim-Spatial) – Evaluates reasoning over geometric and semantic spatial relationships.

• Manipulation Skills (GenieSim-Manip) – Assesses execution of atomic skills and long-horizon task composition.

• Robustness (GenieSim-Robust) – Tests adaptability under real-world disturbances such as lighting changes, sensor noise, and environment variations.

• Sim2Real（GenieSim-Sim2Real) – Includes a series of evaluation tasks for zero-shot real-robot transfer with high success rates.

3. GenieSim x RLinf: Scaling Reinforcement Learning in Simulation

Genie Sim 3.0 also introduces deep integration with the RLinf framework, enabling a complete reinforcement learning (RL) pipeline for embodied AI.

This perfectly complements VLA models, using low-cost RL post-training to bridge the last mile from ‘generalized understanding’ to ‘precise micromanipulation’.

Key features include:

• Decoupled physics and rendering engines – Supporting high-frequency (1000Hz) physics simulation alongside high-fidelity visual observation.

• Massively parallel simulation – Significantly increasing data throughput and accelerating model convergence.

• Closed-loop training and evaluation – RL agents can be trained and evaluated directly within Genie Sim tasks, with built-in reward signals.

• Standardized Gym interfaces – Ensuring compatibility with RLinf and broader ecosystem tools.

This integration enables a seamless pipeline from large-scale simulation training to evaluation – bridging the gap between general understanding and precise control.

Toward a Unified Infrastructure for Embodied AI

By combining large-scale simulation data, LLM-driven environment generation, and standardized evaluation, Genie Sim 3.0 brings together the full development stack:

Environment → Data → Training → Evaluation

This significantly reduces the engineering overhead traditionally required for robotics development, enabling faster iteration and broader experimentation.

As the boundary between simulation and reality continues to narrow – and as environment generation scales from hours to minutes – Genie Sim 3.0 provides a critical foundation for the large-scale deployment of embodied AI.

AGIBOT believes that open, shared infrastructure like Genie Sim will play a key role in accelerating the evolution of the global robotics ecosystem. Let every line of code, every dataset, and every evaluation become a force that moves the industry forward.