A Hybrid Sensing-Prediction Framework for Soft Robot Deformation Perception: Coupling Field Simulation With Gated Recurrent Unit

Current deformation perception methods for soft robots primarily rely on external vision systems, which exhibit limited adaptability. To overcome this limitation, this study proposes a self-sensing approach that enables soft robots to perceive deformation using their own electric potential differences, eliminating reliance on external devices. The proposed method consists of two key components: a deformation data acquisition strategy based on Coupling Field Simulation (CFS), and a predictive model trained using a Gated Recurrent Unit (GRU) neural network. The CFS-based method builds upon prior work, where CFS models were successfully applied to capacitive sensors on soft manipulators. In this study, we extend this framework to design a GRU-based prediction system, train the GRU model with Adam optimization to learn the nonlinear deformation-electric signal mapping. This integrated approach ultimately yields a deformation prediction model with enhanced accuracy through multi-physics data synthesis and iterative parameter adjustment. The model achieved a low mean squared error and good robustness during testing. This closed-loop self-sensing framework offers a promising alternative to vision-dependent approaches, potentially enhancing soft robot autonomy and adaptive control in complex environments.