AI 中文总结
本文提出将常规诊所图像转换为可操作的数字孪生，用于评估具身人工智能，验证了其在机器人测试、策略学习等方面的有效性，填补了具身AI医疗应用离线开发与物理部署间的空白。
AI 中文摘要
具身人工智能（AI）必须在其将运行的临床环境中接受测试，但构建逼真的、可用于机器人测试的场景成本高昂且难以扩展。本文展示了常规的诊所图像可被转换为可操作的数字孪生，用于具身人工智能的任务型评估。我们使用39个眼科诊所场景，将单张照片转换为可编辑、可用于模拟器的环境，并评估了重建质量、房间尺度几何结构、网格贴合度、多机器人可行性、扰动敏感性以及闭环策略性能。重建的场景保留了工作空间结构，而局部编辑支持受控的设备重新配置。设备网格、碰撞代理和语义锚点将视觉重建转换为可感知接触的模拟场景。在三种机器人形态中，共享任务目标呈现出不同的可达性和接触可行性模式。小幅度的设备平移和旋转会产生任务特定的接触余量变化，而这种变化仅通过视觉相似性无法捕捉。数字孪生轨迹还支持局部策略学习和闭环评估。这些发现确立了操作有效性作为临床数字孪生的关键原则，并在医疗保健领域具身人工智能的离线开发与物理部署之间提供了中间层。
英文摘要
Embodied artificial intelligence (AI) must be tested in the clinical environments where it will operate, but building realistic, robot-testable settings is costly and difficult to scale. Here we show that routine clinic images can be transformed into operational digital twins for task-based evaluation of embodied AI. Using 39 ophthalmic clinic scenes, we converted single photographs into editable, simulator-ready environments and assessed reconstruction quality, room-scale geometry, mesh grounding, multi-robot feasibility, perturbation sensitivity and closed-loop policy performance. The reconstructed scenes preserved workspace structure, while local editing enabled controlled device reconfiguration. Device meshes, collision proxies and semantic anchors converted visual reconstructions into contact-aware simulation scenes. Across three robot embodiments, shared task targets showed different patterns of reachability and contact feasibility. Small device translations and rotations produced task-specific changes in contact margins that were not captured by visual similarity alone. Digital-twin trajectories also supported local policy learning and closed-loop evaluation. These findings establish operational validity as a key principle for clinical digital twins and provide an intermediate layer between offline development and physical deployment of embodied AI in healthcare.
