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Data scaling and standardized evaluation benchmarks have driven significantadvances in natural language processing and computer vision. However, roboticsfaces unique challenges in scaling data and establishing evaluation protocols.Collecting real-world data is resource-intensive and inefficient, whilebenchmarking in real-world scenarios remains highly complex. Synthetic data andsimulation offer promising alternatives, yet existing efforts often fall shortin data quality, diversity, and benchmark standardization. To address thesechallenges, we introduce RoboVerse, a comprehensive framework comprising asimulation platform, a synthetic dataset, and unified benchmarks. Oursimulation platform supports multiple simulators and robotic embodiments,enabling seamless transitions between different environments. The syntheticdataset, featuring high-fidelity physics and photorealistic rendering, isconstructed through multiple approaches. Additionally, we propose unifiedbenchmarks for imitation learning and reinforcement learning, enablingevaluation across different levels of generalization. At the core of thesimulation platform is MetaSim, an infrastructure that abstracts diversesimulation environments into a universal interface. It restructures existingsimulation environments into a simulator-agnostic configuration system, as wellas an API aligning different simulator functionalities, such as launchingsimulation environments, loading assets with initial states, stepping thephysics engine, etc. This abstraction ensures interoperability andextensibility. Comprehensive experiments demonstrate that RoboVerse enhancesthe performance of imitation learning, reinforcement learning, world modellearning, and sim-to-real transfer. These results validate the reliability ofour dataset and benchmarks, establishing RoboVerse as a robust solution foradvancing robot learning.

RoboVerse: Towards a Unified Platform, Dataset and Benchmark for Scalable and Generalizable Robot Learning