Embodied AI Simulation Benchmark Platform RoboColiseum Launches with 89.5% Sim-to-Real Alignment

In the field of embodied intelligence, model architectures and algorithms iterate rapidly, but the industry lacks a unified, reproducible evaluation benchmark that truly reflects model capabilities, making it difficult for developers to accurately measure the true boundaries and shortcomings of their models.

The release of RoboColiseum is precisely aimed at this common challenge, attempting to build a multi-dimensional systematic evaluation system, helping developers identify capability strengths and weaknesses through simulation evaluations that are highly consistent with real-world performance.

The platform is built on high-fidelity simulation environments, highly replicating the real world in terms of environmental rendering and physical interaction. Verified through testing, the correlation between simulation evaluation and real-world deployment reaches 89.5%, and the difference in evaluation results for the same model in simulation and the real world is less than 10%.

This verification chain is bidirectional: models trained on real-world data can be directly submitted for simulation evaluation, and models trained on simulation data can also be verified for transfer effects through real robots, thereby shortening the 'training-evaluation-improvement-deployment' iteration cycle.

Traditional evaluations often use overall success rate to summarize model performance. RoboColiseum instead sets task sets around different capability dimensions, having launched 4 capability sub-rankings and 78 high-fidelity simulation evaluation tasks, with plans for continuous updates based on industry, academia, and research needs.

The four sub-rankings include: instruction following (understanding natural language information such as color, quantity, shape), spatial understanding (absolute position, relative relationships, stacking, etc.), perturbation adaptation (changing lighting, materials, camera positions to test stability), and general manipulation (multi-step tasks such as opening doors, pouring, sorting).

Each task is broken down into multiple sub-steps, not only judging final success but also recording completed steps, failure points, and generalization ability, making failure causes traceable.

The platform encapsulates complex environment configuration, asset adaptation, and computational cost into automated services. Developers can go from registration to submission in as fast as 5 minutes, and after one-click model deployment, complete simulation evaluation in as fast as 30 minutes, automatically returning sub-scores, step results, and simulation videos.

With the help of AI Agents, developers can complete data download, model training, local verification, and evaluation submission through natural language dialogue. Model code and weights do not need to be uploaded; only local deployment of inference services and connection via standard interfaces are required.

The platform has provided baseline scores for international embodied foundation models such as ACoT-VLA, π0, π0.5, and GR00T, and open-sourced training code and weights, allowing developers to reproduce and verify results under unified conditions.

The name RoboColiseum is derived from the ancient Roman Colosseum, symbolizing openness, neutrality, and that anyone can take the stage. The platform serves both as an 'arena' for models to compete on equal footing and as a 'training ground' for developers to repeatedly test and identify shortcomings.

The long-term goal is to transform complex real-world tasks into a standardized, reproducible, and continuously updated evaluation system, making evaluation a fundamental tool in the development process of embodied models, and driving embodied intelligence from carefully selected successful demonstrations toward reliable, transparent, and verifiable progress.