A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots

Traditional dynamic models of continuum robots are in general computationally expensive and not suitable for real-time control. Recent approaches using learning-based methods to approximate the dynamic model of continuum robots for control have been promising, although real data hungry—which may cause potential damage to robots and be time consuming—and getting poorer performance when trained with...