In this paper, a compliant five-bar leg mechanism is proposed, designed and manufactured for heavy-load legged robots, by using two magneto-rheological actuators (MRAs) that are capable of offering a maximal torque of 78Nm. To address the rate-dependent hysteresis of the MRA, a hybrid rate-dependent hysteresis model is derived based on the idea of mappings between different hysteresis loops. With integrating the classical Preisach model and the NARX neural network, the hybrid model is able to model hysteresis nonlinearity of the magneto-rheological clutch (MRC). It is then used to estimate and control the output torque of the MRA at the absent of external force/torque sensors. High fidelity force control and variable compliance of the leg mechanism are realized and validated in various experiments with using the MRAs.