  In this study, we further investigate the robustness and generalization
ability of an neural network (NN) based force estimation method, using the da
Vinci Research Kit Si (dVRK-Si). To evaluate our method's performance, we
compare the force estimation accuracy with several baseline methods. We conduct
comparative studies between the dVRK classic and dVRK-Si systems to benchmark
the effectiveness of these approaches.
  We conclude that the NN-based method provides comparable force estimation
accuracy across the two systems, as the average root mean square error (RMSE)
over the average range of force ratio is approximately 3.07% for the dVRK
classic, and 5.27% for the dVRK-Si. On the dVRK-Si, the force estimation RMSEs
for all the baseline methods are 2 to 4 times larger than the NN-based method
in all directions. One possible reason is, we made assumptions in the baseline
methods that static forces remain the same or dynamics is time-invariant. These
assumptions may hold for the dVRK Classic, as it has pre-loaded weight and
maintains horizontal self balance. Since the dVRK-Si configuration does not
have this property, assumptions do not hold anymore, therefore the NN-based
method significantly outperforms.
