用神经网络建模肌腱驱动机器人，实现仿真到现实的精准控制迁移
Sim-to-Real Transfer for Muscle-Actuated Robots via Generalized Actuator Networks
- 通过关节轨迹直接学习复杂肌腱驱动模型，无需扭矩传感器
- 在四自由度气动肌肉臂上成功部署仿真训练的精细控制策略
- 首次实现肌腱驱动机械臂的仿真到现实完整迁移，适合机器人控制研究者
肌腱驱动结合软体肌肉执行器可提升机器人的速度与安全性，并可能加速技能学习。然而，由于固有的非线性、摩擦和迟滞效应，建模与控制变得复杂，阻碍了从仿真到真实系统的策略迁移。为此，我们提出一种仿真到现实的迁移框架，通过学习神经网络模型来表征复杂的执行机构特性，同时利用成熟的刚体动力学仿真处理机械臂运动及环境交互。该方法称为广义执行器网络（GenAN），能通过关节位置轨迹直接识别执行器模型，无需扭矩传感器。在由气动人工肌肉驱动的四自由度机械臂PAMY2上，我们成功部署了完全在仿真中训练的动态精确控制策略，包括目标达、球入杯和乒乓球控制任务。据我们所知，这是首个成功实现四自由度肌肉驱动机械臂的仿真到现实迁移案例。
原文摘要 · Abstract (English)
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 (GenAN), enables actuation model identification across a wide range of robots by learning directly from joint position trajectories rather than requiring torque sensors. Using GenAN on PAMY2, a tendon-driven robot powered by pneumatic artificial muscles, we successfully deploy dynamic but precise goal-reaching, ball-in-a-cup, and table tennis 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.
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