Vision-Language-Action (VLA) models and world models have recently emerged as promising paradigms for general-purpose robotic intelligence, yet their progress is hindered by the lack of reliable evaluation protocols that reflect real-world deployment. Existing benchmarks are largely simulator-centric, which provide controllability but fail to capture the reality gap caused by perception noise, complex contact dynamics, hardware constraints, and system latency. Moreover, fragmented real-world evaluations across different robot platforms prevent fair and reproducible comparison. To address these challenges, we introduce ManipArena, a standardized evaluation framework designed to bridge simulation and real-world execution. ManipArena comprises 20 diverse tasks across 10,812 expert trajectories emphasizing reasoning-oriented manipulation tasks requiring semantic and spatial reasoning, supports multi-level generalization through controlled out-of-distribution settings, and incorporates long-horizon mobile manipulation beyond tabletop scenarios. The framework further provides rich sensory diagnostics, including low-level motor signals, and synchronized real-to-sim environments constructed via high-quality 3D scanning. Together, these features enable fair, realistic, and reproducible evaluation for both VLA and world model approaches, providing a scalable foundation for diagnosing and advancing embodied intelligence systems.

翻译：视觉-语言-动作（VLA）模型和世界模型近期已成为通用机器人智能领域颇具前景的范式，然而缺乏能够反映真实世界部署的可靠评估协议，阻碍了其进展。现有基准主要局限于仿真环境，虽具有可控性，却无法捕捉因感知噪声、复杂接触动力学、硬件约束及系统延迟所带来的现实差异。此外，不同机器人平台上零散的真实世界评估难以实现公平且可重复的比较。为应对这些挑战，我们提出ManipArena——一种旨在弥合仿真与真实世界执行鸿沟的标准化评估框架。ManipArena包含20项多样化任务，覆盖10,812条专家轨迹，侧重于需要语义与空间推理的推理导向型操控任务；通过受控分布外设置支持多层级泛化；并融入了超越桌面场景的长时域移动操控。该框架进一步提供了丰富的感知诊断数据（包括底层电机信号），以及通过高质量三维扫描构建的同步实到虚拟环境。这些特性共同为VLA模型与世界模型方法实现了公平、真实且可重复的评估，为诊断与推进具身智能系统提供了可扩展基础。