论文/arXiv 2610.19336·14 天前

用于人机交互的概率物理数字孪生

针对物理属性难精确获取的问题，提出概率物理数字孪生，并在人机交互场景中验证其指导行动与规划。

AI 整理

• 仿真与训练

原文链接：Probabilistic Physics Digital Twin for Human-Robot Interaction（arXiv 2610.19336 · 发布日期：2026-10-14）

Probabilistic Physics Digital Twin for Human-Robot Interaction

针对物理属性难精确获取的问题，提出概率物理数字孪生，并在人机交互场景中验证其指导行动与规划。

核心要点

• 构建准确数字孪生依赖质量、摩擦、质心等物理属性的精确估计，而现实中往往难以获得。
• 方法将不确定物理属性表示为概率分布，而非固定数值，并在这些分布上仿真动作。
• 孪生体预测可能结果及其概率，用于识别在物理不确定性下仍有效的动作。
• 通过人机交互场景展示，包括向用户传达仿真结果的界面和显式考虑不确定性的机器人规划策略。

验证范围与局限：验证范围为摘要所述的人机交互场景，包括用户界面和机器人规划策略。

英文摘要 (Abstract)

Digital twins are increasingly used in robotics to simulate interactions, predict action outcomes, and support planning. However, constructing accurate digital twins requires precise estimates of physical properties such as mass, friction, and center of mass, which are often difficult to obtain in real-world environments. We propose probabilistic digital twins, which represent uncertain physical properties as probability distributions rather than fixed values. By simulating actions across these distributions, the twin predicts a range of possible outcomes and their likelihoods. These probabilistic simulations can guide both human and robot actions by identifying actions that are effective despite physical uncertainty. We demonstrate this framework through human-robot interaction scenarios, including interfaces that communicate simulated outcomes to users and robot planning strategies that explicitly account for uncertainty.

作者：Yushen Hu, Harshvardhan Chandirasekar, Apurv Kushwaha, Jonathan King, Zhutian Chen

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2610.19336：Probabilistic Physics Digital Twin for Human-Robot Interaction访问 2026-10-21