机器人有了通用大脑：一个模型搞定看、想、动
机器人通常一个任务训练一个模型，换个环境就废。这篇论文做了一个通用大脑，一个模型同时处理感知、空间推理、定位和规划，还能预测机器手该抓哪里。在122B参数版本上，它击败了所有商业和开源模型，在真实机器人上，它比Qwen等通用模型成功率更高。更关键的是，同时训练多个任务和多种机器人，反而比单独训练效果更好——这意味着机器人AI可能正在走向通用化。虽然你明天用不上，但这是机器人从专用走向通用的关键一步。
📄 原文摘要(英文)
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.