system identification

This work addresses the challenge of accurately identifying parameters with minor contributions to robot dynamics under measurement noise and model inaccuracies, which often limits model fidelity. The authors propose a novel identification approach based on “correlated parameters,” starting from a complete dynamic model and integrating link geometry simplification with branch symmetry. Parameter estimation is performed via weighted least squares, followed by iterative model reduction guided by statistical criteria to ensure physical realizability. By uniquely combining structural symmetry with statistical selection, the method significantly enhances identification accuracy and robustness while maintaining feasibility. Experimental validation on two 3-DOF parallel robots demonstrates excellent agreement between predicted and measured forward and inverse dynamics, confirming the effectiveness and reliability of the proposed approach.

This work addresses the challenge of modeling tendon-driven continuum robots, whose dynamics are highly nonlinear, high-dimensional, and dominated by friction. To overcome these difficulties, a data-driven system identification approach is proposed, integrating the N4SID, ARX, and SINDYc algorithms to construct a low-dimensional dynamical model. The study reveals that the dynamic behavior of multi-segment continuum robots can be accurately captured by a two-degree-of-freedom model, uncovering strong kinematic coupling among joints and substantially reducing modeling complexity. Experimental validation demonstrates that the resulting model achieves high prediction accuracy and has been successfully implemented within a real-time model predictive controller, confirming its effectiveness and practical utility.

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 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.

Existing mathematical modeling lacks a rigorous, unambiguous ontological foundation, hindering a unified characterization of the mapping between models and real-world phenomena. This paper introduces, for the first time, an axiomatic definition of mathematical models grounded in Hilbert-space operator theory: a model is formalized as a computable operator acting on random variables, systematically unifying theoretical derivation, experimental implementation, and statistical identification. We further establish a geometric correspondence between the model manifold and the prediction surface, exposing intrinsic structural properties and the fundamental nature of model computability. This framework fills a critical gap in the formal ontology of modeling, providing a unified mathematical foundation for interdisciplinary model construction. It significantly enhances the logical rigor of theoretical inference and the reliability of empirical validation.

This study addresses the challenge of accurately identifying dynamic models for low-cost robotic arms, which often suffer from parameter redundancy and physical infeasibility. Focusing on the CRANE-X7 manipulator, the work proposes a reproducible, physically consistent parameter identification pipeline. It begins with a simplified rigid-body model and a structured excitation trajectory, then integrates inverse dynamics regression, ordinary least squares (OLS), semidefinite programming (SDP) feasibility projection, and closed-loop input error (CLIE) optimization. A novel two-stage filtering mechanism is introduced—combining statistical centrality-based selection with full-pose positive definiteness auditing—to ensure both statistical consistency and physical plausibility. The estimated parameters progressively converge from OLS through SDP to CLIE, achieving high prediction accuracy while rigorously adhering to physical constraints.

This study addresses the challenge of accurately modeling the dynamics of flexible two-degree-of-freedom robotic arms, where rigid-body assumptions fail to capture link flexibility and unmodeled residual dynamics. To overcome this limitation, the authors propose a semi-parametric hybrid dynamical modeling framework that augments rigid-body dynamics with a Gaussian Mixture Model (GMM) to learn residual terms, while employing a purely data-driven kinematic regression as a baseline. This approach synergistically integrates physical priors with data-driven mechanisms, transcending the constraints of conventional fully parametric models in flexible systems. Experimental results on an open-source dataset demonstrate that the data-driven component—combined with regularization and least-squares estimation—significantly enhances torque prediction accuracy, thereby validating the efficacy and superiority of the proposed hybrid model.

This work addresses the challenge of constructing high-fidelity actuator dynamics models from real robot data in the absence of torque sensors, current measurements, or internal motor information. The authors formulate actuator identification as a trajectory-matching problem, relying solely on joint position and velocity provided by encoders. By integrating differentiable simulation with gradient-based optimization, their unified framework accommodates a spectrum of model classes—from compact analytical formulations to neural actuator mappings—without requiring specialized test benches or torque sensing. Experimental results demonstrate that the proposed approach reduces position error from 14.20 mrad to 7.54 mrad. Furthermore, when applied to downstream motion policy training, it yields a 46% increase in robot travel distance and a 75% reduction in heading deviation.

This work proposes a novel experimental design framework for dynamic systems that addresses two key limitations of existing approaches: the neglect of process noise and the reliance on unknown true parameters for computing the Fisher information matrix (FIM). By integrating Bayesian averaging with an adaptive updating mechanism, the method jointly accounts for both process and measurement noise through Kalman filtering. The FIM is computed via Bayesian averaging over the parameter prior and is continuously updated in real time as new data become available, thereby optimizing subsequent experimental inputs. This approach achieves, for the first time, robust and real-time experimental design in linear dynamic systems with process noise without requiring knowledge of the true system parameters, significantly enhancing both the information efficiency and robustness of system identification.