With the development of embodied artificial intelligence, robotic research has increasingly focused on complex tasks. Existing simulation platforms, however, are often limited to idealized environments, simple task scenarios and lack data interoperability. This restricts task decomposition and multi-task learning. Additionally, current simulation platforms face challenges in dynamic pedestrian modeling, scene editability, and synchronization between virtual and real assets. These limitations hinder real world robot deployment and feedback. To address these challenges, we propose DVS (Dynamic Virtual-Real Simulation Platform), a platform for dynamic virtual-real synchronization in mobile robotic tasks. DVS integrates a random pedestrian behavior modeling plugin and large-scale, customizable indoor scenes for generating annotated training datasets. It features an optical motion capture system, synchronizing object poses and coordinates between virtual and real world to support dynamic task benchmarking. Experimental validation shows that DVS supports tasks such as pedestrian trajectory prediction, robot path planning, and robotic arm grasping, with potential for both simulation and real world deployment. In this way, DVS represents more than just a versatile robotic platform; it paves the way for research in human intervention in robot execution tasks and real-time feedback algorithms in virtual-real fusion environments. More information about the simulation platform is available on https://immvlab.github.io/DVS/.

翻译：随着具身人工智能的发展，机器人研究日益聚焦于复杂任务。然而，现有仿真平台通常局限于理想化环境、简单任务场景，且缺乏数据互操作性。这限制了任务分解与多任务学习。此外，当前仿真平台在动态行人建模、场景可编辑性以及虚实资产同步方面面临挑战，阻碍了机器人的实际部署与反馈。为应对这些挑战，我们提出DVS（动态虚实仿真平台），一个面向移动机器人任务的动态虚实同步平台。DVS集成了随机行人行为建模插件与大规模可定制室内场景，用于生成带标注的训练数据集。该平台采用光学动作捕捉系统，同步虚拟与现实世界中的物体位姿与坐标，以支持动态任务基准测试。实验验证表明，DVS支持行人轨迹预测、机器人路径规划及机械臂抓取等任务，具备仿真与实际部署的双重潜力。由此可见，DVS不仅是一个多功能机器人平台，更为研究机器人执行任务中的人为干预以及虚实融合环境中的实时反馈算法开辟了道路。更多关于该仿真平台的信息请访问 https://immvlab.github.io/DVS/。