Abstract: Data scaling and standardized evaluation benchmarks have driven remarkable advances in natural language processing and computer vision. However, in robotics, scaling up data and establishing evaluation protocols pose significant challenges. Directly collecting real-world data is inefficient and resource-intensive, while benchmarking in real-world scenarios also remains highly challenging. Synthetic data and simulation environments present a promising alternative, yet existing efforts often fail to fully leverage the potential of simulation, resulting in limited data quality, diversity, and fragmented benchmarks. To address these challenges, we introduce RoboVerse, a simulation platform built on a unified data format. RoboVerse supports multiple simulators and robots, enabling seamless switching between different simulators and embodiments. In addition, by leveraging our unified data format, it enables multiple workflows to efficiently collect tasks and trajectories from various sources with high fidelity and diversity. Using RoboVerse, we generate the largest high-quality synthetic dataset to date in a unified format, along with a standardized benchmark system that reliably evaluates policies and supports assessment across different levels of generalization. We employ the RoboVerse workflows and conduct extensive experiments, demonstrating that our platform and dataset significantly enhance the performance of imitation learning, reinforcement learning, and world model learning, facilitating the transfer from simulation to real-world applications. These results demonstrate the reliability of our dataset and benchmark, highlighting RoboVerse as an effective solution for advancing simulation-assisted robot learning.