Tendon drives paired with soft muscle actuation enable faster and safer robots while potentially accelerating skill acquisition. Still, these systems are rarely used in practice due to inherent nonlinearities, friction, and hysteresis, which complicate modeling and control. So far, these challenges have hindered policy transfer from simulation to real systems. To bridge this gap, we propose a sim-to-real pipeline that learns a neural network model of this complex actuation and leverages established rigid body simulation for the arm dynamics and interactions with the environment. Our method, called Generalized Actuator Network (GeAN), enables actuation model identification across a wide range of robots by learning directly from joint position trajectories rather than requiring torque sensors. Using GeAN on PAMY2, a tendon-driven robot powered by pneumatic artificial muscles, we successfully deploy precise goal-reaching and dynamic ball-in-a-cup policies trained entirely in simulation. To the best of our knowledge, this result constitutes the first successful sim-to-real transfer for a four-degrees-of-freedom muscle-actuated robot arm.

翻译：肌腱驱动结合软体肌肉作动能实现更快、更安全的机器人，同时可能加速技能习得。然而，由于固有的非线性、摩擦和迟滞效应，这类系统在实际中鲜少应用——这些特性使得建模与控制异常复杂。迄今为止，这些挑战阻碍了策略从仿真到真实系统的迁移。为弥合这一鸿沟，我们提出一种虚实迁移流水线，该流水线学习此类复杂执行机构的神经网络模型，并利用成熟的刚体仿真处理机械臂动力学及其与环境交互。我们所提出的方法——通用执行器网络（GeAN）——通过直接学习关节位置轨迹而非依赖力矩传感器，能够实现跨广泛机器人平台的驱动模型辨识。在采用气动人工肌肉驱动的肌腱驱动机器人PAMY2上应用GeAN后，我们成功部署了完全在仿真中训练的精准到位控制与动态接球策略。据我们所知，这一结果首次实现了四自由度肌肉驱动机器人臂的虚实迁移。