closed-loop control

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 paper addresses the prescribed-time arrival-avoidance-standby task for nonlinear pure-feedback systems operating in unknown dynamic environments. Method: We propose a real-time spatiotemporal tubular framework that online adjusts the center and radius of a time-varying sphere in the state space to dynamically characterize and avoid unsafe sets. It introduces, for the first time, an exact closed-form control law—without approximation—that integrates adaptive spatiotemporal modeling, predefined-time convergence, and state-constraint satisfaction. Contribution/Results: Rigorous theoretical analysis guarantees both obstacle-avoidance safety and task deadline compliance. Extensive simulations and hardware experiments on mobile robots and UAVs demonstrate millisecond-level online updates and timely, safe navigation in dense, dynamic obstacle fields—validating the framework’s strong real-time performance and scalability.

To address the lack of systematic benchmarks for evaluating the safety of obstacle-avoidance controllers in dynamic environments, this paper proposes the first three-dimensional evaluation framework tailored to object-perception-based controllers. The framework systematically assesses three core dimensions: kinematic completeness, control-point continuity, and trajectory stability. Grounded in representative robot–obstacle interaction scenarios, it introduces an experiment-driven, quantitative evaluation methodology to comparatively analyze three mainstream controller classes. Results expose common deficiencies across controllers—particularly in motion smoothness and trajectory stability. Crucially, this work establishes the first reproducible, extensible, and standardized assessment infrastructure for obstacle avoidance. By unifying evaluation criteria and metrics, it provides both theoretical foundations and practical tools for performance benchmarking, defect diagnosis, and safety-oriented optimization of navigation algorithms.

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 nonlinear systems subject to unknown dynamics and external disturbances. Methodologically, it proposes an integrated online system identification and model predictive control (MPC) framework that combines reproducing kernel Hilbert space (RKHS) modeling, random Fourier feature approximation, online least-squares parameter adaptation, and learning-based receding-horizon MPC—compatible with control-affine structures. The approach achieves sublinear dynamic regret against an adversarial clairvoyant controller for the first time, while ensuring finite-time near-optimality and asymptotic convergence to optimality. To jointly handle modeling errors and exogenous disturbances, it introduces self-supervised learning and state- and input-adaptive disturbance modeling. Extensive validation is conducted on an inverted pendulum, quadrotor simulation, and real-world quadrotor hardware under challenging conditions—including wind gusts, ground effect, and aerodynamic drag—demonstrating robustness and high-precision trajectory tracking performance.

Traditional engineering control systems struggle to replicate the robustness and agility exhibited by biological organisms in complex environments, primarily due to the neglect of active perception as a core component of task-level control. This work proposes that active perception serves not merely to reduce perceptual uncertainty but is intrinsically integrated into control at the task level. The study introduces a novel dual-mode “explore-exploit” control strategy that synergistically combines adaptive sensing, sensorimotor coupling, and dynamic behavioral mode switching. Through control-theoretic modeling, behavioral dynamical analysis, and biological empirical validation, the research elucidates the emergent mechanisms underlying biological active perception and establishes a new paradigm for enhancing perceptual and control capabilities in robotic systems.

This work addresses the scarcity of scalable, physically plausible motion data for quadrupedal robots and the fragility of cross-morphology motion transfer by constructing a behavior foundation integrating multi-source data—including video generation, motion capture, teleoperation, and hand-designed trajectories—and establishes, for the first time, a scaling law for quadrupedal motion tracking. The approach introduces a conditional video generation–based data pyramid, a Flow-Matching universal policy, and a three-stage privileged-to-perceptual learning framework, augmented with temporal LiDAR memory and terrain prediction supervision. This enables zero-shot tracking of unseen motions and robust locomotion across diverse terrains. The system demonstrates product-level behavioral intelligence in urban autonomous navigation and multimodal companion interaction, supporting coordinated multi-policy execution, smooth transitions, energy-efficient control, and safety assurance.

Traditional robotic perception is constrained by fixed or onboard sensors, limiting the ability to flexibly acquire task-optimal viewpoints. This work proposes SensorPerch, which decouples sensing from the robot body and environmental infrastructure by introducing autonomous, deployable, and retrievable sensor units as independent physical entities. Built upon a lightweight, wireless, reconfigurable sensor platform and a task-driven viewpoint selection framework, SensorPerch enables stable attachment to diverse surfaces and on-demand sensor placement. Experimental results demonstrate its effectiveness in both object-coupled and policy-coupled tasks, achieving persistent long-range state monitoring and policy success rates comparable to those obtained with prior knowledge of the optimal viewpoint.

This work addresses the problem of feedback motion planning for continuous-time stochastic nonlinear systems under Signal Temporal Logic (STL) specifications by proposing a novel framework that integrates predicate erosion with probabilistic reachable tubes. Predicate erosion is employed to transform stochastic STL constraints into tightened deterministic ones, while probabilistic reachable tubes quantify the deviation of stochastic trajectories from their nominal counterparts. Leveraging contraction theory, a tracking controller is designed to establish a closed-loop planning pipeline. The proposed approach significantly reduces the conservatism inherent in conventional methods, achieving high STL satisfaction probability without compromising planning performance. Simulations and real-world experiments on a quadrupedal robot demonstrate that the method outperforms baseline approaches in both STL satisfaction rate and computational efficiency.