GigaWorld-1: 构建用于机器人策略评估的世界模型的路线图

摘要

评估具身机器人基础模型仍是一个关键瓶颈：与可通过数字基准高效评估的大语言模型不同，机器人策略需要缓慢且昂贵的真实世界部署，受限于硬件和人工监督，这促使研究者将世界模型作为替代性策略评估工具，但使世界模型能够可靠进行策略评估的关键属性仍未被充分理解。本工作对用于机器人策略评估的世界模型进行了系统性研究，并提出了WMBench基准——该基准基于真实机器人遥操作数据和匹配的策略执行轨迹构建，涵盖多种操作任务，从而支持跨模型家族、动作编码、执行时间步和评估指标的受控比较。利用WMBench，我们分析了7个视频世界模型、4种动作表示方案以及超过324,000次模拟策略执行（与真实机器人执行轨迹配对），并通过CVPR 2026 GigaBrain挑战赛的大规模社区提交、精选合成轨迹以及超过12,000小时的训练视频进一步丰富了分析内容。我们的实验得出了三个核心洞见：评估器质量主要取决于长周期、对动作忠实执行的一致性，而非短期的视觉逼真度；预训练收益不仅来自数据规模，更源于通用世界知识与机器人特定可控性之间的平衡；架构选择（包括动作编码、记忆设计以及针对评估器的后训练）强烈决定了与真实机器人行为的一致性。基于这些结果，我们推导出实用的设计路线图，并在GigaWorld-1中实现了该路线图——这是一个专门为策略评估优化的世界模型。我们完整开源了代码、模型、数据集和工具包，以推动具身基础模型的可扩展评估研究。

English

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.