  In surgery, the application of appropriate force levels is critical for the
success and safety of a given procedure. While many studies are focused on
measuring in situ forces, little attention has been devoted to relating these
observed forces to surgical techniques. Answering questions like "Can certain
changes to a surgical technique result in lower forces and increased safety
margins?" could lead to improved surgical practice, and importantly, patient
outcomes. However, such studies would require a large number of trials and
professional surgeons, which is generally impractical to arrange. Instead, we
show how robots can learn several variations of a surgical technique from a
smaller number of surgical demonstrations and interpolate learnt behaviour via
a parameterised skill model. This enables a large number of trials to be
performed by a robotic system and the analysis of surgical techniques and their
downstream effects on tissue. Here, we introduce a parameterised model of the
elliptical excision skill and apply a Bayesian optimisation scheme to optimise
the excision behaviour with respect to expert ratings, as well as individual
characteristics of excision forces. Results show that the proposed framework
can successfully align the generated robot behaviour with subjects across
varying levels of proficiency in terms of excision forces.
