An Effectiveness Study Across Baseline and Learning-Based Force Estimation Methods on the da Vinci Research Kit Si System

Robot-assisted minimally invasive surgery, such as through the da Vinci systems, improves precision and patient outcomes. However, da Vinci systems prior to da Vinci 5 lacked direct force-sensing capabilities, forcing surgeons to operate without the haptic feedback they get through laparoscopy. Our prior work restored force sensing through machine learning-based force estimation for an open-source surgical robotics research platform, the da Vinci Research Kit (dVRK) Classic. This study extends our previous method to the newer dVRK system, the dVRK-Si. Additionally, we benchmark the performance of the learning-based algorithm against baseline methods (which make simplifying assumptions on the torque) to study how the two systems differ. In both systems, the learning-based method outperforms baselines, but the difference is much larger in the dVRK-Si. Nonetheless, dVRK-Si force estimation accuracy lags behind the dVRK Classic, with Root Mean Square Error (RMSE) 2 to 3 times higher. Further analysis reveals suboptimal PID control in the dVRK-Si. We hypothesize that this is because, unlike the dVRK Classic, the dVRK-Si is not mechanically balanced and exhibits more complex internal dynamics. This study advances the understanding of learning-based force estimation and is the first work to implement learning-based dynamics estimation of the new dVRK-Si system.