Constructing an accurate simulation model of real-world environments requires reliable estimation of physical parameters such as mass, geometry, friction, and contact surfaces. Traditional real-to-simulation (Real2Sim) pipelines rely on manual measurements or fixed, pre-programmed exploration routines, which limit their adaptability to varying tasks and user intents. This paper presents a Real2Sim framework that autonomously generates and executes Behavior Trees for task-specific physical interactions to acquire only the parameters required for a given simulation objective, without relying on pre-defined task templates or expert-designed exploration routines. Given a high-level user request, an incomplete simulation description, and an RGB observation of the scene, a vision-language model performs multi-modal reasoning to identify relevant objects, infer required physical parameters, and generate a structured Behavior Tree composed of elementary robotic actions. The resulting behavior is executed on a torque-controlled Franka Emika Panda, enabling compliant, contact-rich interactions for parameter estimation. The acquired measurements are used to automatically construct a physics-aware simulation. Experimental results on the real manipulator demonstrate estimation of object mass, surface height, and friction-related quantities across multiple scenarios, including occluded objects and incomplete prior models. The proposed approach enables interpretable, intent-driven, and autonomously Real2Sim pipelines, bridging high-level reasoning with physically-grounded robotic interaction.

翻译：构建真实世界环境的精确仿真模型需要可靠地估计质量、几何形状、摩擦力和接触面等物理参数。传统的真实到仿真（Real2Sim）流程依赖于手动测量或固定的预编程探索程序，这限制了其对不同任务和用户意图的适应性。本文提出了一种Real2Sim框架，该框架能自主生成并执行面向任务特定物理交互的行为树，以仅获取给定仿真目标所需的参数，而不依赖于预定义的任务模板或专家设计的探索程序。给定一个高级用户请求、一个不完整的仿真描述以及场景的RGB观测，视觉语言模型执行多模态推理以识别相关物体、推断所需物理参数，并生成由基本机器人动作组成的结构化行为树。所生成的行为在扭矩控制的Franka Emika Panda机器人上执行，从而实现了用于参数估计的顺应性、密集接触的交互。获取的测量数据用于自动构建物理感知的仿真。在真实机械臂上的实验结果表明，该方法能在多种场景（包括被遮挡物体和不完整先验模型）下估计物体质量、表面高度和摩擦相关量。所提出的方法实现了可解释、意图驱动且自主的Real2Sim流程，将高层推理与基于物理的机器人交互相连接。