Cerebellum-inspired approach for adaptive kinematic control of soft robots

Soft robots are diverse time varying dynamical systems. Hence models describing their behavior have to constantly adapt to accommodate these changes. This is a challenging problem due to the complexities involved with modelling a soft robot and the diverse array of available modelling approaches. Adaptive controllers that can accommodate for model uncertainties are a viable option in this case. This paper presents a cerebellum-inspired adaptive kinematic controller. The adaptive controller is built on top of an approximate inverse kinematic model. The proposed architecture guarantees error convergence without a priori knowledge about the approximate inverse kinematic controller and learns using only task space error information. Simulation results show how such an architecture can provide monotonously decreasing tracking error reductions online. The robustness of the algorithm to changes in the inverse kinematic component and morphology of the soft robot itself is shown.