Embedded soft sensors can significantly impact the design and control of soft-bodied robots. Al-though, there have been considerable advances in technology behind these novel sensing materials, their application in real-world tasks, especially in closed-loop control tasks have been severely limited. This is mainly because of the challenge involved with modelling a nonlinear time-variant sensor embedded in a complex soft-bodied system. This paper presents a learning-based approach for closed-loop force control with embedded soft sensors and recurrent neural networks. We present learning protocols for training a class of recurrent neural networks called long-short term memory (LSTMs) that allows us to develop accurate and robust state estimation models of these complex dynamical systems within a short period of time. Using this model, we develop a simple feedback force controller for a soft anthropomorphic finger even with significant drift and hysteresis in our feedback signal. Simulation and experimental studies are conducted to analyze the capabilities and generalizability of the control architecture. Experimentally, we are able to develop a closed-loop controller with a control frequency of25Hz and an average accuracy of 0.17 N. Our results indicate that current soft sensing technologies can already be employed in real-world applications with the aid of machine learning techniques and an appropriate training methodology