  Soft robotic manipulators provide numerous advantages over conventional rigid
manipulators in fragile environments such as the marine environment. However,
developing analytic inverse models necessary for shape, motion, and force
control of such robots remains a challenging problem. As an alternative to
analytic models, numerical models can be learned using powerful machine learned
methods. In this paper, the Kinematics Transformer is proposed for developing
accurate and precise inverse kinematic models of soft robotic limbs. The
proposed method re-casts the inverse kinematics problem as a sequential
prediction problem and is based on the transformer architecture. Numerical
simulations reveal that the proposed method can effectively be used in
controlling a soft limb. Benchmark studies also reveal that the proposed method
has better accuracy and precision compared to the baseline feed-forward neural
network
