The nonlinearity and hysteresis of soft robot motions have posed challenges in control. The Jacobian controller is transferred from rigid robot controllers and exhibits conciseness, but the improper assumption of soft robots induces the feasibility only in a small local area. Accurate controllers like neural networks can deal with delayed and nonlinear motion, achieving high accuracy, but they suffer from the high data amount requirement and black-box property. Inspired by these approaches, we propose an adaptive generalized Jacobian controller for soft robots. This controller is constructed by the concise format of the Jacobian controller but includes more states and independent matrices, which is suitable for soft robotics. In addition, the initialization leverages the motor babbling strategy and batch optimization from neural network controllers. In experiments, we first analyze the online controllers, including the Jacobian controller, the Gaussian process regression, and our controller. Real experiments have validated that our controller outperforms the RNN controller even with fewer data samples, and it is adaptive to various situations without fine-tuning, like different control frequencies, softness, and even manufacturing errors. Future work may include online adjustment of the controller format and adaptability validation in more scenarios.

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