run real-robot experiments

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.

To address safety risks, high training costs, and the sim-to-real gap in deploying reinforcement learning (RL) on physical robots, this paper proposes a four-stage progressive RL training framework: system identification → core simulation training → high-fidelity simulation → real-robot deployment. The framework integrates domain randomization, policy distillation, and online fine-tuning, and is implemented using PyTorch, MuJoCo, and ROS2, specifically optimized for the Boston Dynamics Spot platform. Its key innovation lies in enabling cross-fidelity policy transfer and iterative refinement, substantially improving sim-to-real generalization. Evaluated on a robotic inspection task, the approach achieves high-precision control of position and orientation, with >92% success rate in real-world deployment, 60% reduction in training cost, and 3.5× faster convergence compared to baseline methods.

This work addresses the lack of effective automated testing mechanisms in the collaborative development of open-source cyber-physical systems (CPS) software on robotic platforms, which often leads to critical bugs going undetected. To bridge this gap, the paper introduces ACT—a novel framework that brings automated continuous testing into the collaborative development of open-source CPS and robotics for the first time, thereby filling a key void in the community’s testing toolchain. ACT integrates a GitHub-driven automated testing pipeline, hardware-in-the-loop testing, and multi-module co-validation techniques. A case study on an educational robotics platform demonstrates that the framework significantly enhances test coverage and reliability of open-source CPS software.

This study addresses the absence of a unified and widely accepted formalism for specifying robotic tasks, which hinders non-experts from defining single- or multi-robot missions in complex, dynamic environments. For the first time, it systematically compares four prominent task specification paradigms—Behavior Trees, Finite State Machines, Hierarchical Task Networks (HTN), and Business Process Model and Notation (BPMN)—from the perspective of task-level description. The evaluation focuses on expressiveness, control structures, tooling support, and integration with human workflows. Through expert validation, the work clarifies the strengths, limitations, and suitable application contexts of each approach, offering researchers and practitioners a principled basis for method selection to enhance the robustness and adaptability of robotic task systems.

Safety-critical small Unmanned Aircraft Systems (sUAS) lack systematic, standardized testing processes that are tightly integrated with safety analysis. Method: This paper proposes a requirement-driven coupled testing framework, introducing the novel triadic paradigm of “requirements–simulation testing–safety analysis.” It employs formal requirement modeling with bidirectional traceability, a simulation–hardware-in-the-loop cooperative testing architecture, scenario-driven test case generation, and deep integration of safety analysis methods (e.g., Fault Tree Analysis and System-Theoretic Process Analysis). Contribution/Results: Evaluated on an sUAS case study, the framework significantly improves simulation fidelity coverage and requirement coverage, enables end-to-end safety evidence generation, fills the gap in standardized sUAS testing procedures, and delivers reproducible, verifiable testing assets to support airworthiness certification.

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 challenges of scaling robotic education in higher education, where high costs of commercial digital twins and the steep learning curve of open-source middleware like ROS hinder widespread adoption. To overcome these barriers, the authors propose an education-oriented, four-layer extensible communication architecture that abstracts complex communication protocols and enables seamless integration between graphical modeling environments and physical robots. Notably, this architecture is the first in an open-source platform to support hardware-agnostic, high-fidelity virtual–physical mapping. By integrating 3D visual modeling, a ROS backend, and efficient data serialization and routing mechanisms, the system significantly reduces deployment complexity. Experimental validation through multi-axis spatial trajectory tasks demonstrates its effectiveness in facilitating practical robotics instruction in engineering education.

This study evaluates the reliability and adaptability of large language models in executing scientific tasks within real-world physical environments, with a focus on their ability to generate executable experimental protocols and iteratively refine them based on empirical evidence. Leveraging a robotic chemistry laboratory comprising 45 modular workstations and conducting 4,608 trials, this work extends scientific agent evaluation beyond pure reasoning to encompass physical executability and evidence-driven closed-loop adaptation, introducing a quantifiable framework for assessing deployment readiness. Results reveal that only 3.3% of generated protocols were deemed executable by expert reviewers, with the best-performing system achieving a success rate of 28.1%. Most generated workflows contained no more than 30 steps and generally lacked capabilities for workflow-level replanning or methodological reconfiguration in response to experimental outcomes.

This work addresses the reliability challenges inherent in deploying autonomous mobile robots from simulation to real-world environments by proposing and implementing an end-to-end development and validation framework. Building upon an existing mechatronic platform, the system integrates onboard sensing and computing units to achieve self-localization and autonomous navigation. The complete architecture is first developed and rigorously validated in a high-fidelity simulation environment before being seamlessly transferred to a physical robot. Experimental results demonstrate that the real-world system successfully replicates the performance observed in simulation, thereby confirming the effectiveness and robustness of the proposed approach. These findings substantiate the use of high-fidelity simulation as a trustworthy foundation for robotic development, significantly enhancing deployment efficiency and system credibility.