  Controlling soft continuum manipulator arms is difficult due to their
infinite degrees of freedom, nonlinear material properties, and large
deflections under loading. This paper presents a data-driven approach to
identifying soft manipulator models that enables consistent control under
variable loading conditions. This is achieved by incorporating loads into a
linear Koopman operator model as states and estimating their values online via
an observer within the control loop. Using this approach, real-time, fully
autonomous control of a pneumatically actuated soft continuum manipulator is
achieved. In several trajectory following experiments, this controller is shown
to be more accurate and precise than controllers based on models that are
unable to explicitly account for loading. The manipulator also successfully
performs pick and place of objects with unknown mass, demonstrating the
efficacy of this approach in executing real-world manipulation tasks.
