feedback loops

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 study addresses the challenges of control design in complex industrial processes characterized by multivariable coupled dynamics by proposing an automated control strategy generation framework that integrates large language models (LLMs) with Bayesian optimization. The approach decomposes control design into structured code generation steps, ensuring physical consistency through execution-based validation and feedback-driven repair. It pioneers the automatic synthesis of decentralized PI controller architectures and their tuning environments directly from dynamic process models. Evaluated on a nonlinear gas preheater benchmark, the generated control schemes—subsequently refined via Bayesian optimization—achieve a 26.5% improvement in closed-loop performance and significantly enhance the transient response of pressure loops, thereby demonstrating the method’s effectiveness and novelty.

This study addresses the degradation in tracking accuracy and high adaptation costs caused by dynamics mismatch in sim-to-real transfer. To this end, we propose the OSRAM framework, which innovatively models the robot and policy as a closed-loop system. Specifically, it employs meta-learning to train a closed-loop dynamics model that can be rapidly fine-tuned with minimal real-world interactions. Subsequently, reference commands are optimized online via model predictive control, enabling efficient adaptation to real environments without fine-tuning the underlying policy. Experimental results demonstrate that the proposed method significantly improves both prediction and tracking accuracy in bipedal velocity tracking and mobile manipulation tasks, effectively reducing residual errors across diverse hardware configurations.

Existing robotic systems predominantly rely on open-loop preprogramming or single-step reactive behaviors, limiting their ability to autonomously generate and execute multi-step, complex actions in dynamic environments. Method: We propose a fully closed-loop, perception-driven hierarchical planning framework that abstracts tasks as discrete, transient closed-loop controllers (“Tasks”). Integrating physics-inspired causal environmental modeling with task-level sequential planning, the framework enables online generation, simulation, and execution of multi-step task chains. Contribution: This work represents the first integration of closed-loop control with hierarchical causal reasoning, eliminating dependence on open-loop priors. We validate the approach end-to-end on two real-world robotic platforms, demonstrating robust multi-step behavioral generation and execution solely from closed-loop sensory inputs. Experimental results confirm both functional efficacy and operational robustness under realistic dynamic conditions.

Traditional robot design and control are typically decoupled, leading to morphologies poorly aligned with task requirements. This paper proposes a simulation-driven co-optimization framework for morphology and control, breaking the conventional “design-then-control” paradigm to enable task-oriented, end-to-end joint search. Our method employs gradient-free optimization to simultaneously evolve structural parameters and controller policies within a URDF-based multi-task reinforcement learning simulation environment. Key contributions include: (1) demonstrating that controller retraining significantly improves performance, yielding an average gain of 37%; and (2) revealing an inverse correlation between morphological complexity and controller training budget—providing theoretical justification for structural simplification under resource constraints. We validate the framework across four public simulation benchmarks, showing that co-optimization consistently yields more compact, robust, and task-adapted robot morphologies compared to sequential approaches.

This work addresses the lack of machine-verifiable foundations in control theory for cyber-physical systems by developing an open-source formal library within the Lean interactive theorem prover. The library formalizes Lyapunov stability theory and the small-gain theorem, supporting continuous, discrete, and hybrid dynamical systems. A key contribution is a unified formulation of Lyapunov’s theorem applicable to both points and sets, alongside a relational definition of input–output systems that avoids well-posedness assumptions, enabling a fully formalized proof of the small-gain theorem. Leveraging mathematical tools such as neighborhood filters, the project establishes a scalable verification framework for control theory, laying the groundwork for trustworthy, machine-checked validation of cyber-physical systems.

This study addresses the simulation-to-reality mismatch in robot control caused by physical disturbances despite accurate models, proposing a sampling-based disturbance observer (DOB). This method overcomes the limitation of classical DOBs that rely on explicit dynamics models by leveraging state rollout and cost query interfaces, thereby extending disturbance compensation to black-box simulators and learned world models. Furthermore, it innovatively decouples the state and cost disturbance channels for independent estimation and compensation. Experimental results demonstrate that the proposed approach effectively bridges the Sim-to-Real gap across diverse simulated and real-world robotic tasks, yielding significant improvements in control performance.

This study addresses the safety and precision challenges in automated slewing control of knuckle-boom cranes caused by payload oscillations. The authors propose an open-loop slewing trajectory generation method driven purely by behavioral input–output data, circumventing the need for explicit system modeling. Leveraging Willems’ behavioral theory and its extended formulations, the approach enables a non-parametric characterization of the underactuated system’s dynamics and generates smooth, optimal trajectories via convex optimization. Compared to conventional model-based strategies, the proposed method substantially reduces reliance on expert knowledge and large datasets. Experimental results demonstrate a 35% reduction in payload swing, a 43% decrease in tracking error, and a 50% shortening of execution time, highlighting its efficacy and practicality in real-world crane automation.

本文针对具有不可忽略物理响应的线性可变形物体，提出一种闭环控制架构，通过在多个固定点调节力和力矩来实现形状调节。

研究比较了外部、内部持续及控制生成的扰动对自适应调节的影响，发现内部持续扰动导致最大暴露和调节负担，并探讨了不同扰动源与时机如何影响模型中的暴露和控制器负担。