机器人学几何，不再只看2D图
现在的机器人学操作，大多还是把3D世界压成2D图片来理解，就像你透过猫眼看人——能看见动作，但算不准距离和角度。这篇直接把一个预训练好的3D几何模型（原本用来理解物体形状和空间关系）当成机器人的大脑主干：浅层负责看，中间插一个预测模块，根据你的指令和当前状态，提前算出未来几秒的几何变化，然后深层再把这些预测转成具体动作。结果是在模拟和真实场景里，比那些用大语言模型或视频模型做底座的方案更准、更稳、更快，模型还更小。它不是你明天就能用的产品，但指明了一个方向：让机器人用3D的脑子想3D的事，而不是硬把3D翻译成2D再猜。
📄 原文摘要(英文)
Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world. Recent vision-language-action models (VLAs) and video world-action models (WAMs) inherit strong semantic or temporal priors from large-scale foundation models, but they still operate primarily on 2D image frames or 2D-derived latent spaces, leaving implicit the 3D geometry required for contact-rich manipulation. We propose the Geometric Action Model (GAM), a language-conditioned manipulation policy that directly repurposes a pretrained geometric foundation model (GFM) as a shared substrate for perception, temporal prediction, and action decoding. GAM splits the GFM at an intermediate layer: the shallow layers serve as an observation encoder, and a causal future predictor inserted at the split layer forecasts future latent tokens conditioned on language, proprioception, and action history. The predicted future tokens are then routed through the remaining GFM blocks for feature propagation and decoding, allowing a single backbone to produce both future geometry and actions. This design equips the GFM with language-conditioned temporal world modeling through minimal architectural modification while preserving its rich geometric priors. Across a broad suite of simulation and real-robot manipulation benchmarks, GAM is more accurate, more robust, faster, and lighter than current foundation-model-scale baselines.