Null space redundancy learning for a flexible surgical robot

A new challenge for surgical robotics is placed in the use of flexible manipulators, to perform procedures that are impossible for currently available rigid robots. Since the surgeon only controls the end-effector of the manipulator, new control strategies need to be developed to correctly move its flexible body without damaging the surrounding environment. This paper shows how a positional controller for a new surgical robot (STIFF-FLOP) can be learnt from the demonstrations given by an expert user. The proposed algorithm exploits the variability of the task to comply with the constraints only when needed, by implementing a minimal intervention principle control strategy. The results are applied to scenarios involving movements inside a constrained environment and disturbance rejection.