gazebo simulation

This study addresses the persistent gap between theoretical control performance and its practical realization in real-world robotic systems, often caused by inadequate discretization, insufficient real-time guarantees, and weak error handling in control software. For the first time from a software engineering perspective, the authors systematically analyze 184 open-source robotic controllers through code review, empirical analysis, and test evaluation, uncovering common deficiencies in application scenarios, implementation details, and verification practices. The findings reveal that most implementations fail to properly account for critical system constraints, and their testing strategies inadequately validate the theoretical assurances they claim. This work highlights a significant disconnect between implementation quality and theoretical promises, offering concrete directions and practical guidelines for developing reliable, verifiable robotic control software.

This work addresses the tight coupling between reference signal processing and control law implementation in ROS 2 controllers, which leads to code redundancy and poor reusability. To resolve this, the authors propose a modular architecture that decouples reference generation logic from the controller for the first time in ROS 2, introducing a dedicated Reference Generator component. This component uniformly handles reference acquisition, validation, and interpolation in both joint and Cartesian spaces, and integrates seamlessly with downstream controllers via the ros2_control chainable controller mechanism. Leveraging this architecture, several novel controllers—including PD with gravity compensation, Cartesian pose, and admittance controllers—were developed. Experimental validation on Universal Robots and Franka Emika manipulators demonstrates stable and reliable reference tracking, significantly reduced code duplication, and enhanced controller reusability and development efficiency.

To address the lack of unified, flexible, and real-time inverse dynamics (ID) computation across simulation and real-robot deployments, this paper introduces a lightweight, robot-agnostic ID software library built natively for ROS 2. Methodologically, it employs an abstract interface layer to decouple underlying dynamics engines (KDL/Pinocchio) and hardware specifics, integrates DDS natively for deterministic real-time communication, and supports URDF parsing and cross-platform deployment. Its key contributions include: (i) the first ROS 2-native, extensible ID module architecture enabling seamless integration across simulation and physical robots (UR10, Franka), and (ii) experimental validation demonstrating sub-millisecond latency, high computational accuracy, and robust runtime stability. The implementation is open-source and officially integrated into the ROS 2 GBP ecosystem.

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.

This work addresses the challenges commonly encountered in multirotor flight control research—namely, the complexity of simulation-to-hardware workflows, poor code readability, and limited extensibility—by presenting a lightweight, modular, full-stack open-source flight control system built on ROS 2 and ROSflight 2.0. The proposed architecture enables seamless deployment between simulation and real hardware while significantly simplifying code structure without compromising performance. Its high modularity enhances both readability and extensibility, facilitating rapid research iteration. Experimental results demonstrate that the system achieves waypoint tracking performance comparable to state-of-the-art flight controllers, yet with a smaller, cleaner codebase that streamlines development and validation in academic settings.

This work addresses the non-reproducibility of ROS 2 simulations across different hardware platforms, a critical issue stemming from its asynchronous multi-process architecture that undermines scientific evaluation and continuous integration. We propose a non-intrusive approach that enforces deterministic execution by unifying the callback scheduling order of ROS 2 C++ nodes without requiring any modifications to existing codebases. To our knowledge, this is the first method to achieve fully reproducible simulation for ROS 2 C++ nodes. We introduce the ROS Simulation Library for C++ (RSLCPP), which provides precise control over execution timing and communication sequencing. Experimental validation on both synthetic benchmarks and real-world robotic systems demonstrates that our approach yields bit-for-bit identical simulation results across diverse CPU architectures. The implementation is publicly available as open-source software.

This work proposes an open-source teleoperation system based on ROS 2 to address the limitations of dual-arm robotic teleoperation, including constrained workspace, unintuitive control, and inefficient data collection. The system introduces a relative motion control mechanism driven by pose changes from VR controllers, enabling intuitive operation independent of the robot’s initial configuration and effectively overcoming physical workspace boundaries. It features multiple control modes, a modular architecture, real-time visualization via RViz, and streamlined gripper control with safety reset functionality. Experimental results demonstrate that the proposed system significantly enhances the intuitiveness and safety of teleoperation while improving data acquisition efficiency and cross-platform scalability.

This study addresses the limitations of current robotic system validation, which relies heavily on manual selection of test scenarios, thereby hindering scalability and compromising reproducibility and reliability of conclusions. To overcome these challenges, this work proposes a compositional, scenario-based modeling approach that integrates declarative test specifications, plugin-driven scenario generation, containerized parallel simulation, and unified result analysis to establish the first modular and scalable automated verification framework. The framework enables systematic parameter variation across multiple dimensions and facilitates robust identification of systemic faults versus stochastic anomalies. Evaluated across 5,480 distinct scenario configurations with over 100,000 simulation runs, the approach accumulated 1,800 hours of simulated operation and 1,873 virtual kilometers, demonstrating its efficacy in discerning consistent system deficiencies from random irregularities.

This work addresses the challenge that traditional model-based testing is ill-suited for distributed robotic systems due to their high nondeterminism, dynamic reconfiguration, and inherent complexity. To overcome this limitation, the paper proposes the Scenario Specification Language (SCSL), which enables the construction of system-level tests by composing basic scenarios. The approach integrates runtime online test generation and execution with mechanisms for dynamic component joining/leaving and interface reconnection, thereby supporting automated testing and dynamic reconfiguration. The syntax and semantics of SCSL are validated through a robotic salvage mission case study, where automatically generated tests effectively demonstrate the feasibility and advantages of the proposed method.

This work addresses the limited generalizability and transferability of current foundation models in robotics, which often require custom integration of perception, actuation, and safety mechanisms. To overcome this, the authors propose ROSClaw—a model-agnostic execution layer that enables plug-and-play deployment of arbitrary foundation models on any ROS 2 robot by integrating the OpenClaw agent runtime with ROS 2. Key innovations include standardized capability discovery, multimodal observation normalization, action validation within configurable safety bounds, and structured audit logging. Experiments across three robotic platforms and four foundation models demonstrate up to a 4.8× difference in non-policy action proposal rates and show that the proposed execution layer significantly improves task success rates and safety across diverse frameworks.