Hybrid Dynamics Modeling for a Flexible 2-DoF Robotic Arm
For robotics researchers modeling flexible-link systems, this work provides a comparative analysis showing that data-driven identification outperforms purely parametric models, but the results are incremental and dataset-specific.
This paper compares physics-informed and data-driven models for torque prediction in a flexible 2-DoF robotic arm, finding that regularized and least-squares estimators outperform physics-based parameters, with the best model achieving a 30% reduction in RMSE over the physics-based baseline.
This paper examines three approaches for modeling the dynamics of a flexible-link 2-DoF robotic arm to address unmodeled dynamics not captured by rigid-body models. Two physics informed models combine rigid-body dynamics (RBD) formulations with a Gaussian Mixture Model (GMM) to capture residual model errors and linkage flexibility. A kinematics-based regression model serves as a purely data-driven baseline. Using an open-source dataset, torque predictions are first estimated using Ridge regression on kinematic features, while the physicsbased baseline is constructed from published specifications, and ordinary least-squares regression is subsequently used to estimate the same parameter set directly from data. Results show that the physics-based parameters yield the poorest accuracy, while regularized and least-squares estimators align more closely with measured torques. Residual analysis and error metrics highlight the limitations of purely parametric models for flexible-link systems and underscore the value of regularization and data-driven identification, supporting developments of semi-parametric residual learning methods.