An Efficient Learning Control Framework With Sim-to-Real for String-Type Artificial Muscle-Driven Robotic Systems
<|reference_start|>An Efficient Learning Control Framework With Sim-to-Real for String-Type Artificial Muscle-Driven Robotic Systems: 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.<|reference_end|>
@article{tao2024an,
    title={An Efficient Learning Control Framework With Sim-to-Real for String-Type
  Artificial Muscle-Driven Robotic Systems},
    author={Jiyue Tao, Yunsong Zhang, Sunil Kumar Rajendran, Feitian Zhang, Dexin
  Zhao, Tongsheng Shen},
    journal={arXiv preprint arXiv:2405.10576},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10576},
    primaryClass={cs.RO}
}