  Robotic systems driven by artificial muscles present unique challenges due to
the nonlinear dynamics of actuators and the complex designs of mechanical
structures. Traditional model-based controllers often struggle to achieve
desired control performance in such systems. Deep reinforcement learning (DRL),
a trending machine learning technique widely adopted in robot control, offers a
promising alternative. However, integrating DRL into these robotic systems
faces significant challenges, including the requirement for large amounts of
training data and the inevitable sim-to-real gap when deployed to real-world
robots. This paper proposes an efficient reinforcement learning control
framework with sim-to-real transfer to address these challenges. Bootstrap and
augmentation enhancements are designed to improve the data efficiency of
baseline DRL algorithms, while a sim-to-real transfer technique, namely
randomization of muscle dynamics, is adopted to bridge the gap between
simulation and real-world deployment. Extensive experiments and ablation
studies are conducted utilizing two string-type artificial muscle-driven
robotic systems including a two degree-of-freedom robotic eye and a parallel
robotic wrist, the results of which demonstrate the effectiveness of the
proposed learning control strategy.
