想象⼀下这样的场景：⼀个机器⼈不仅能够理解你的语⾔指令，还能在复杂的现实环境中灵活移动，精 确操作各种物体，甚至在面对突发情况时做出智能决策。这不再是科幻电影中的幻想，⽽是正在快速成 为现实的技术⾰命。从Tesla的Optimus人形机器人到Boston Dynamics的Atlas，从OpenAI的机械手到 Google的RT-X项目，全球顶尖的科技公司都在竞相布局这⼀颠覆性领域。具身智能的核⼼理念在于让 AI系统不仅拥有"大脑"，更要拥有能够感知和改变物理世界的"身体"。这种AI不再局限于虚拟的数字空 间，⽽是能够真正理解物理定律、掌握运动技能、适应复杂环境。它们可以在工厂中进行精密装配，在 医院⾥协助手术操作，在家庭中提供贴⼼服务，在危险环境中执⾏救援任务。这种技术的潜在影响力是 革命性的：它将彻底改变制造业、服务业、医疗健康、太空探索等几乎所有行业。 然⽽，要实现真正的具身智能，还⾯临着前所未有的技术挑战。机器人需要在复杂的物理世界中学习和 适应，这要求我们不仅要掌握先进的具身算法，更要深入理解物理仿真、机器⼈控制、感知融合等多个 技术领域。⽽在这个技术栈的核⼼位置，有一个名字正在被越来越多的研究者和 ...

点击下方 卡片 ，关注" 具身智能 之心 "公众号 一、你的3D Grounding 模型真的work吗？ 在具身智能系统中，智能体需要依靠第一视角的3D感知算法来理解周边环境。作为其中的核心任务之一，Embodied 3D Grounding是指根据ego-centric的RGB- D图像序列以及语言描述在三维空间中定位目标对象，要求模型能够融合语言与三维视觉信息，准确识别出语句中所指代的物体。当前主流方法多采用两阶段策 略，即先利用检测模型提取三维区域特征，再进行语言引导的grounding微调。这自然引出一个疑问： 第二阶段这种针对 Grounding 的微调，其效果究竟如何，它 真的work吗？ 令人颇感意外的是，实证结果显示，即便是当前最先进的Grounding模型，其实际表现也远未达到预期。相反，那些完全未接受语言监督、仅依赖目标类别进行筛 选的检测模型，在Grounding任务的评估中竟取得了更优的结果。具体而言，考虑到任务中的语言指令为模板生成，本文通过规则解析提取出目标物体的类别标 签，之后使用该类别从检测模型中筛选对应预测框，直接作为Grounding的输出。理论上，这种做法缺乏语言理解过 ...

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