A general soft robotic controller inspired by neuronal structural and plastic synapses that adapts to diverse arms, tasks, and perturbations.

Human intelligence arises from the interplay between a compliant morphology and a cognitive system that is capable of adaptive learning. Soft robots exhibit similar mechanical compliance, but they still need learning capabilities that can be generalized across tasks and adapted to unknown conditions. We present a neuron-inspired control framework that couples a paired offline-online decomposition with a learned contraction metric. Offline "structural synapses" encode task-agnostic features, while online "plastic synapses" are configuration-specific parameters updated by error-gated rules consistent with long-term potentiation and depression. The contraction metric serves as a homeostatic constraint, providing a stability guarantee. We validate our approach on cable-driven and shape-memory-alloy soft arms across trajectory tracking, pick-and-place, and whole-body shaping tasks. Compared with baseline methods, our approach reduces tracking error by 44 to 55% and maintains more than 92% shape accuracy under perturbations, including varying payloads, dynamic airflow, and actuator failures. These results establish a general controller that adapts to diverse soft arms, tasks, and perturbations.