让AI看懂物理世界：从聊天到动手
现在的AI能聊天、能看图，但让它理解“物体掉下来会怎样”“手伸过去能不能碰到”这类物理常识，它常常抓瞎。这篇论文把“看懂环境、生成动作、预测下一步”三件事揉进同一个模型：它把语言、机械臂的运动轨迹、以及下一帧的画面都编码成同一种“语言”，用预测下一个词的方式一起训练。训练数据全部来自人类日常互动视频——人怎么拿杯子、怎么推门，模型从中学会动作和后果的对应。在28项物理理解测试里，这个8B参数的开源模型平均得分72.5，追平了GPT-6-Astra和Gemini 3.6 Flash这类闭源巨头，14项上刷新开源纪录。它还能直接输出机械臂轨迹、预测未来画面（带深度和机器人掩码）。这不是你明天能用的产品，但它指向一个关键拐点：AI正在从“会说话”走向“会动手”，而开源模型第一次在这个赛道上和闭源平起平坐。
📄 原文摘要(英文)
We present PhysBrain 1.5, a unified model for understanding physical environments, generating actions, and predicting future states. Motivated by the physical loop of observation, interaction, and environmental change, we bring these capabilities into a common learning framework. Starting from a general vision--language model, we encode language responses, end-effector motion, and dense visual targets as discrete sequences and jointly optimize them with autoregressive next-token prediction. Pre-training draws its embodied supervision entirely from human interaction videos, using task-centered episodes to pair semantic and spatial context with recovered motion and subsequent observations. We then adapt the model through supervised fine-tuning on a mixture of human demonstrations, robot trajectories, and simulated experience. Across 28 embodied understanding benchmarks, our 8B model achieves an average score of 72.5, setting a new open-source state of the art and performing on par with leading proprietary models such as GPT-6-Astra and Gemini 3.6 Flash. It achieves the best open-source results on 14 benchmarks while retaining general multimodal capabilities. Beyond these understanding evaluations, qualitative examples show the model's ability to produce end-effector trajectories and predict future scenes through spatially aligned RGB, depth, and robot-mask outputs.