We present $multipanda\_ros2$, a novel open-source ROS2 architecture for multi-robot control of Franka Robotics robots. Leveraging ros2 control, this framework provides native ROS2 interfaces for controlling any number of robots from a single process. Our core contributions address key challenges in real-time torque control, including interaction control and robot-environment modeling. A central focus of this work is sustaining a 1kHz control frequency, a necessity for real-time control and a minimum frequency required by safety standards. Moreover, we introduce a controllet-feature design pattern that enables controller-switching delays of $\le 2$ ms, facilitating reproducible benchmarking and complex multi-robot interaction scenarios. To bridge the simulation-to-reality (sim2real) gap, we integrate a high-fidelity MuJoCo simulation with quantitative metrics for both kinematic accuracy and dynamic consistency (torques, forces, and control errors). Furthermore, we demonstrate that real-world inertial parameter identification can significantly improve force and torque accuracy, providing a methodology for iterative physics refinement. Our work extends approaches from soft robotics to rigid dual-arm, contact-rich tasks, showcasing a promising method to reduce the sim2real gap and providing a robust, reproducible platform for advanced robotics research.

翻译：本文提出$multipanda\_ros2$，一种用于Franka Robotics机器人多机控制的新型开源ROS2架构。该框架利用ros2_control，为从单一进程控制任意数量机器人提供原生ROS2接口。我们的核心贡献在于解决实时力矩控制中的关键挑战，包括交互控制与机器人-环境建模。本工作的核心重点是维持1kHz控制频率，这是实时控制的必要条件，也是安全标准要求的最低频率。此外，我们引入控制单元（controllet）特征设计模式，实现控制器切换延迟$\le 2$ ms，为可复现的基准测试和复杂多机器人交互场景提供支持。为弥合仿真与现实（sim2real）鸿沟，我们集成高保真MuJoCo仿真，并提供运动学精度与动力学一致性（力矩、力及控制误差）的量化指标。进一步，我们证明现实世界惯性参数辨识能显著提升力和力矩精度，为迭代式物理模型优化提供方法论。本工作将软机器人领域的方法拓展至刚性双臂、密集接触任务，展示了缩小仿真与现实鸿沟的有效途径，并为先进机器人研究提供鲁棒且可复现的平台。