GigaBrain-0.7：采用三系统架构扩展具身基础模型以获得涌现能力

中文摘要 AI 辅助

视觉-语言-动作（VLA）模型已成为通用具身智能体的主流范式，在结构化场景中展现出强大的复杂及长周期任务完成能力。但当前VLA系统能否通过更有效的架构设计、扩展至更大且更异质的数据 regime，以及实现跨任务和具身的更广泛泛化仍是未解决的问题。为此，我们提出GigaBrain-0.7，一款在不同机器人具身间泛化能力显著提升的具身基础模型。具体而言，GigaBrain-0.7通过三系统架构统一理解、预测与动作，将预训练扩展至超过37000小时的异质具身数据，并引入单阶段对齐训练，联合优化视觉-语言理解与多具身动作生成。与前代GigaBrain-0系列及包括π₀.5在内的现有SOTA模型相比，GigaBrain-0.7在基础零样本能力、语言条件指令跟随及训练后任务成功率上实现显著提升。尤其在我们自研的Maker H01平台及主流机器人具身上，GigaBrain-0.7在家用和工业场景中均展现出强大的任务适应性与完成能力，所有训练代码及预训练模型权重将被发布。

英文摘要

Vision-language-action (VLA) models have become a dominant paradigm for generalist embodied agents, demonstrating strong complex and long-horizon task completion in structured settings. Yet it remains an open question whether current VLA systems can benefit from more effective architectural design, scale to substantially larger and more heterogeneous data regimes, and achieve broader generalization across tasks and embodiments. To this end, we present GigaBrain-0.7, an embodied foundation model with substantially improved generalization across diverse robot embodiments. Specifically, GigaBrain-0.7 unifies understanding, prediction, and action through a three-system architecture, scales pretraining to over 37,000 hours of heterogeneous embodied data, and introduces one-stage alignment training that jointly optimizes vision-language understanding and multi-embodiment action generation. Compared with the preceding GigaBrain-0 series and prior state-of-the-art models including $π_{0.5}$, GigaBrain-0.7 achieves substantial improvements in foundation zero-shot capabilities, language-conditioned instruction following, and post-training task success rates. In particular, on our in-house Maker H01 platform and mainstream robot embodiments, GigaBrain-0.7 demonstrates strong task adaptability and completion ability across both home and industrial scenarios. All training code and pretrained model weights will be released.

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https://gigaai.cc/blog/gigabrain07