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In surgery, the application of appropriate force levels is critical for thesuccess and safety of a given procedure. While many studies are focused onmeasuring in situ forces, little attention has been devoted to relating theseobserved forces to surgical techniques. Answering questions like "Can certainchanges to a surgical technique result in lower forces and increased safetymargins?" could lead to improved surgical practice, and importantly, patientoutcomes. However, such studies would require a large number of trials andprofessional surgeons, which is generally impractical to arrange. Instead, weshow how robots can learn several variations of a surgical technique from asmaller number of surgical demonstrations and interpolate learnt behaviour viaa parameterised skill model. This enables a large number of trials to beperformed by a robotic system and the analysis of surgical techniques and theirdownstream effects on tissue. Here, we introduce a parameterised model of theelliptical excision skill and apply a Bayesian optimisation scheme to optimisethe excision behaviour with respect to expert ratings, as well as individualcharacteristics of excision forces. Results show that the proposed frameworkcan successfully align the generated robot behaviour with subjects acrossvarying levels of proficiency in terms of excision forces.

Generating robotic elliptical excisions with human-like tool-tissue interactions