  This paper addresses a new strategy called Simulation-to-Real-to-Simulation
(Sim2Real2Sim) to bridge the gap between simulation and real-world, and
automate a flexible object manipulation task. This strategy consists of three
steps: (1) using the rough environment with the estimated models to develop the
methods to complete the manipulation task in the simulation; (2) applying the
methods from simulation to real-world and comparing their performance; (3)
updating the models and methods in simulation based on the differences between
the real world and the simulation. The Plug Task from the 2015 DARPA Robotics
Challenge Finals is chosen to evaluate our Sim2Real2Sim strategy. A new
identification approach for building the model of the linear flexible objects
is derived from real-world to simulation. The automation of the DRC plug task
in both simulation and real-world proves the success of the Sim2Real2Sim
strategy. Numerical experiments are implemented to validate the simulated
model.
