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Measurement of environment interaction forces during roboticminimally-invasive surgery would enable haptic feedback to the surgeon, therebysolving one long-standing limitation. Estimating this force from existingsensor data avoids the challenge of retrofitting systems with force sensors,but is difficult due to mechanical effects such as friction and compliance inthe robot mechanism. We have previously shown that neural networks can betrained to estimate the internal robot joint torques, thereby enablingestimation of external forces. In this work, we extend the method to estimateexternal Cartesian forces and torques, and also present a two-step approach toadapt to the specific surgical setup by compensating for forces due to theinteractions between the instrument shaft and cannula seal and between thetrocar and patient body. Experiments show that this approach provides estimatesof external forces and torques within a mean root-mean-square error (RMSE) of 2N and 0.08 Nm, respectively. Furthermore, the two-step approach can add aslittle as 5 minutes to the surgery setup time, with about 4 minutes to collectintraoperative training data and 1 minute to train the second-step network.

Estimation of Trocar and Tool Interaction Forces on the da Vinci Research Kit with Two-Step Deep Learning