dynamic parameter identification

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 work addresses the challenge of high-precision joint estimation of robotic states and physical parameters under constraints and disturbances by proposing a Bayesian system identification framework. The approach embeds physically consistent inverse dynamics, contact and closed-loop kinematic constraints, and a comprehensive joint friction model as hard equality constraints within the estimation process. Energy-based regressors are incorporated to enhance parameter observability, while prior constraints on inertial and actuation parameters are explicitly supported. A constrained Riccati recursion algorithm preserving banded matrix structure is devised to achieve linear time complexity for efficient computation. Evaluations in simulation and experiments on the Unitree B1 robot demonstrate that, compared to forward-dynamics and decoupled estimation methods, the proposed framework significantly accelerates convergence, reduces errors in inertial and friction parameter estimates, improves contact consistency, and substantially enhances trajectory tracking performance in complex terrains when integrated into model predictive control (MPC).

System re-identification becomes challenging under insufficient excitation data—i.e., when persistent excitation conditions are not satisfied. Method: This paper proposes a physics-informed, data-driven control framework that integrates physical prior knowledge—specifically, boundedness of system matrix norms—to construct an interpolation-based data generalization mechanism. It combines physics embedding with robust system identification to jointly ensure safety constraint satisfaction, energy-optimal policy synthesis, and high-accuracy prediction of unmodeled dynamics—even with limited data. Contribution/Results: The approach significantly improves control reliability and generalization in low-data regimes. Experiments demonstrate that physical priors effectively compensate for data scarcity, enabling safe, optimal, and robust control under stringent data limitations. This work establishes a novel paradigm for safety-critical control in data-constrained settings.

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 coupled challenges of inertial parameter estimation and simultaneous pose estimation for legged robots operating in multi-contact scenarios. Methodologically, it proposes an efficient nonlinear optimal estimation framework featuring: (1) a parametrized Riccati recursion-based multi-stage solver to enhance real-time performance; (2) a physically consistent inertia manifold model incorporating a null-space mechanism to avoid manifold singularities; and (3) analytically derived contact dynamics modeling for high-fidelity force interaction representation. Compared to conventional least-squares approaches, the method achieves significantly improved accuracy in both state estimation and inertial parameter identification during complex dynamic tasks—e.g., brachiation on humanoid platforms. Experimental validation on the Unitree Go1 quadruped confirms the algorithm’s robustness, real-time capability (<5 ms per iteration), and engineering practicality under varying contact configurations and terrain conditions.

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 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 study addresses the challenge of accurately identifying impact parameters—velocity, mass, and energy—in aerospace composites under data degradation or noise interference. To this end, the authors propose a unified modeling framework that synergistically integrates physical priors with data-driven learning. By constructing an input space based on a physics-informed energy metric, designing a decoupled surrogate model, and incorporating a hybrid physics-constrained loss function within a neural network architecture, the approach systematically embeds observational, inductive, and learning-based physical biases. This enables decoupled inference of impact parameters while ensuring kinetic energy consistency. Experimental results demonstrate that the method achieves mean absolute percentage errors below 8% for both impact velocity and mass, and below 10% for energy, exhibiting robust generalization and stability even under data sparsity, high noise levels, and damage-induced conditions.

This study addresses the challenge of unreliable parameter estimation in sampling-based system identification, where trajectories fail to disentangle individual parameter effects. To overcome this limitation, we propose an information-decoupled trajectory design framework. By constructing a normalized objective based on the Schur complement of the Fisher information matrix, our method integrates logarithmic aggregation with a piecewise selection mechanism to optimize active exploration strategies. This approach generates complementary trajectory segments that enable unambiguous separation of parameter effects. Experimental results demonstrate that the proposed framework reduces parameter identification error by 39.6% on average and significantly enhances downstream policy transfer performance. Furthermore, the accuracy of Sim-to-Real dynamic capture is successfully validated on the K1 humanoid robot, confirming the practical effectiveness of our approach.

本文提出了一种为大型现实物体生成物理一致模型的工作流程，通过分离物体并识别手柄和主体来解决仿真中缺乏物理一致性动态模型的问题。