Bridging the Sim-to-Real Gap with multipanda ros2: A Real-Time ROS2 Framework for Multimanual Systems

2026-02-02

AI Summary

This work proposes multipanda_ros2, an open-source multi-arm control framework built on ROS 2 to address the challenges of control accuracy, real-time performance, and dynamic consistency in sim-to-real transfer for multi-manipulator systems. The framework enables single-process real-time control of an arbitrary number of Franka arms, integrates high-fidelity MuJoCo simulation, and enhances force/torque accuracy through inertial parameter identification. It introduces a novel controllet-feature design pattern that achieves controller switching latency of ≤2 ms and extends soft robotics methodologies to rigid dual-arm contact tasks, establishing quantitative metrics to bridge the sim-to-real gap. Experimental results demonstrate stable 1 kHz torque control and significantly improved consistency between simulation and physical execution, offering a reproducible, high-fidelity platform for complex multi-arm cooperative tasks.

Technology Category

• Intelligent Robots: Multi-Robot Systems
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

Abstract

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.

Problem

• sim-to-real gap
• real-time control
• multi-robot systems
• dynamic consistency
• torque control

Innovation

• multipanda_ros2
• sim-to-real
• real-time control
• controllet design pattern
• MuJoCo simulation