Reproducible Dynamic Parameter Identification for a Low-Cost Robot Arm: A Positive-Definiteness Audit for Model Acceptance

Dynamic parameter identification of low-cost robot arms is challenging because limited sensing and drivetrain nonidealities can yield models that predict measured torques well but are physically unsuitable for model-based control. This paper presents a reproducible dynamic parameter identification pipeline for CRANE-X7, a low-cost seven-degree-of-freedom arm driven by modular smart actuators. A 39-parameter OpenSYMORO base-parameter model is identified without CAD inertial data using fully specified single-joint and adjacent-pair excitation, ordinary least squares, a conditional semidefinite-programming projection, and closed-loop input error refinement. Model acceptance is decided by a separate positive-definiteness audit of the identified inertia matrix over 221,875 sampled configurations. Experiments cover 40 identification trajectories at four sampling intervals and three held-out validation trajectories. The reduced model improves held-out prediction over the full 65-parameter model on all seven identification trajectories used for the model comparison. Fixed-configuration analyses show the distinct role of the feasibility audit: models with similar torque predictions can produce unstable acceleration-resolved dynamics and reverse the direction of the inertia inversion. For the selected identification trajectory, two executions separated by 26 days yield a 1.25% relative spread in held-out root-mean-square error. The accepted model passes audits with five random seeds, and sensitivity analyses quantify the effects of the sampling interval and of torque-constant uncertainty. The complete trajectory specification and numerical record support independent implementation and comparison. These results establish predictive performance, inertia-matrix feasibility, and repeatability as complementary criteria for evaluatingdynamic models of low-cost arms.