机器人评测：别只看画面美，要看它能不能跑完
评测机器人AI比评测大语言模型难得多——后者跑个数字测试就行，前者得真让机器人动起来，又慢又贵。这篇论文想用「世界模型」来替代真实机器人做评测，但发现关键不是画面多逼真，而是模型能不能在长时间、多步骤的任务中保持动作一致。研究者建了一个新基准，分析了7个视频模型、4种动作编码，跑了32万次模拟，结论是：短期视觉真实感不重要，长期一致性才是评测质量的命门。他们据此造了一个专门用于评测的世界模型GigaWorld-1。这不是你明天能用的工具，但如果你关心机器人AI怎么落地，它告诉你一个反直觉的事实：别被漂亮的视频骗了，能稳定跑完任务的模型才是好模型。
📄 原文摘要(英文)
Evaluating embodied robot foundation models remains a critical bottleneck; unlike large language models efficiently assessed via digital benchmarks, robotic policies require slow, costly real-world rollouts limited by hardware and human supervision, which has driven interest in world models as surrogate policy evaluators, yet the key properties that make a world model reliable for policy assessment remain poorly understood. This work presents a systematic study of world models for robotic policy evaluation and introduces WMBench, a benchmark constructed from real-robot teleoperation data and matched policy rollouts covering diverse manipulation tasks to enable controlled comparisons across model families, action encodings, rollout horizons, and evaluation metrics. Using WMBench, we analyze 7 video world models, 4 action representation schemes, and over 324,000 simulated policy rollouts paired with real robot executions, further enriching our analysis with large-scale community submissions from the CVPR 2026 GigaBrain Challenge, curated synthetic trajectories, and a training videos spanning more than 12,000 hours. Our experiments deliver three core insights: evaluator quality is dominated by long-horizon, action-faithful rollout consistency rather than short-term visual realism; pretraining gains stem not only from data scale but from balancing general world knowledge with robot-specific controllability; and architectural choices including action encoding, memory design, and evaluator-focused post-training strongly determine alignment with real-world robot behavior. Drawing on these results, we derive a practical design roadmap and realize it in GigaWorld-1, a world model specially optimized for policy evaluation, and we fully release our code, models, datasets, and toolkits to advance scalable evaluation research for embodied foundation models.