给机器人一双能看清小东西的眼睛
机器人总在“看不清”上栽跟头：一堆杂物里的小零件、要抓的细管子，通用视觉模型经常认不出、定位不准，而控制又必须快到闭环。这篇提出一个 4B 参数的“接地”基础模型，把目标位置直接编码成共享的坐标词表，在 34 个基准上平均超过 GPT-6 Astra，更关键的是：当它当机器人的视觉骨干，抓取成功率在分布外场景最高比最强基线提升 24.8%，自动驾驶的轨迹误差也明显下降。它不是你明天能装进自家机器人用的，但它指向一个趋势：给物理智能单独训练“感知底座”，比直接拿通用视觉模型凑合更靠谱。
📄 原文摘要(英文)
Precise grounding matters. It specifies which object is the target and where that object is, even in clutter and for tiny objects, and it has to be fast enough for closed-loop control. Yet vision-language-action (VLA) and world-action models (WAMs) take perception from general-purpose vision-language and video-generation backbones, which still fail in these settings. We introduce GroundingPI, a 4B grounding foundation model that generates points and boxes as quantized coordinates in a shared vocabulary. Training combines multimodal and spatial pretraining, supervised fine-tuning, and reinforcement learning with GRPO, using supervision from public datasets and dedicated data engines. Against 44 baselines across 34 grounding benchmarks spanning 11 perceptual capabilities, GroundingPI establishes a new state of the art, averaging 73.68%, above the larger GPT-6 Astra (71.54%). As a downstream visual backbone, GroundingPI improves performance on robotic manipulation and autonomous driving. On RoboTwin 2.0, it outperforms every mainstream backbone we evaluate in all four out-of-distribution settings, by up to 24.8% relative to the strongest backbone. On RoboCasa-GR1, GroundingPI trained with 50% of the demonstrations outperforms those baselines trained with 75%. On nuScenes, used as the visual backbone, GroundingPI attains an average open-loop L2 error of 0.296 m. We systematically analyze GroundingPI's pretraining in scale and data composition. Downstream autonomous driving and robotic manipulation improve as the pretraining is scaled. Analyzing the data recipe across these 11 perceptual capabilities shows dense grounding's substantial benefits for both, and OCR's potential as a catalyst for perceptual learning. These results support grounding as a perceptual foundation, and dedicated perceptual pretraining as a promising direction for foundation models of physical intelligence.