  With worldwide implementation, millions of surgeries are assisted by surgical
robots. The cable-drive mechanism on many surgical robots allows flexible,
light, and compact arms and tools. However, the slack and stretch of the cables
and the backlash of the gears introduce inevitable errors from motor poses to
joint poses, and thus forwarded to the pose and orientation of the
end-effector. In this paper, a learning-based calibration using a deep neural
network is proposed, which reduces the unloaded pose RMSE of joints 1, 2, 3 to
0.3003 deg, 0.2888 deg, 0.1565 mm, and loaded pose RMSE of joints 1, 2, 3 to
0.4456 deg, 0.3052 deg, 0.1900 mm, respectively. Then, removal ablation and
inaccurate ablation are performed to study which features of the DNN model
contribute to the calibration accuracy. The results suggest that raw joint
poses and motor torques are the most important features. For joint poses, the
removal ablation shows that DNN model can derive this information from
end-effector pose and orientation. For motor torques, the direction is much
more important than amplitude.
