RynnBrain 1.1：迈向更强大且更具通用性的具身基础模型

摘要

我们发布了RynnBrain 1.1，这是一个涵盖2B、9B和122B-A10B参数规模的具身基础模型家族。通过统一的时空与物理基础框架进行训练，RynnBrain 1.1支持具身感知、空间推理、定位和规划。与RynnBrain 1.0相比，新版本进一步引入了跨模型家族的接触点预测，以及2B和9B模型的原生3D定位能力，使得其表征和输出更直接地服务于机器人操作任务。我们还开发了具有统一跨本体动作空间和本体特定掩码的RynnBrain-VLA，并将其部署在宇树G1、Astribot-S1和天玑·无骥平台上。RynnBrain 1.1在具身认知、定位和3D定位方面取得了强劲成果，其中122B-A10B模型在VSI-Bench、MMSI和RefSpatial-Bench基准测试上超越了所有被评估的专有和开源模型。真实机器人实验表明，基于RynnBrain初始化的策略性能优于基于Qwen的代表性通才VLA，而联合多任务和多本体训练相比单任务训练提升了过程评分和成功率。

English

We present RynnBrain 1.1, a family of embodied foundation models spanning 2B, 9B, and 122B-A10B scales. Trained with a unified spatio-temporal and physically grounded framework, RynnBrain 1.1 supports embodied perception, spatial reasoning, localization, and planning. Compared with RynnBrain 1.0, it further introduces contact-point prediction across the model family and native 3D grounding for the 2B and 9B models, yielding representations and outputs that are more directly aligned with robot manipulation. We also develop RynnBrain-VLA with a unified cross-embodiment action space and embodiment-specific masking, and deploy it on Unitree G1, Astribot-S1, and Tianji-Wuji. RynnBrain 1.1 achieves strong results on embodied cognition, localization, and 3D grounding, with the 122B-A10B model outperforming all evaluated proprietary and open-source models on VSI-Bench, MMSI, and RefSpatial-Bench. Real-robot experiments show that RynnBrain-initialized policies outperform Qwen-based and representative generalist VLAs, while joint multi-task and multi-embodiment training improves process scores and success rates over per-task training.