  Tendon-driven robots, a type of continuum robot, have the potential to reduce
the invasiveness of surgery by enabling access to difficult-to-reach anatomical
targets. In the future, the automation of surgical tasks for these robots may
help reduce surgeon strain in the face of a rapidly growing population.
However, directly encoding surgical tasks and their associated context for
these robots is infeasible. In this work we take steps toward a system that is
able to learn to successfully perform context-dependent surgical tasks by
learning directly from a set of expert demonstrations. We present three models
trained on the demonstrations conditioned on a vector encoding the context of
the demonstration. We then use these models to plan and execute motions for the
tendon-driven robot similar to the demonstrations for novel context not seen in
the training set. We demonstrate the efficacy of our method on three
surgery-inspired tasks.
