Recent studies have demonstrated the immense potential of exploiting muscle actuator morphology for natural and robust movement -- in simulation. A validation on real robotic hardware is yet missing. In this study, we emulate muscle actuator properties on hardware in real-time, taking advantage of modern and affordable electric motors. We demonstrate that our setup can emulate a simplified muscle model on a real robot while being controlled by a learned policy. We improve upon an existing muscle model by deriving a damping rule that ensures that the model is not only performant and stable but also tuneable for the real hardware. Our policies are trained by reinforcement learning entirely in simulation, where we show that previously reported benefits of muscles extend to the case of quadruped locomotion and hopping: the learned policies are more robust and exhibit more regular gaits. Finally, we confirm that the learned policies can be executed on real hardware and show that sim-to-real transfer with real-time emulated muscles on a quadruped robot is possible. These results show that artificial muscles can be highly beneficial actuators for future generations of robust legged robots.

翻译：近期研究表明，利用肌肉致动器形态实现自然且鲁棒的运动具有巨大潜力——但目前仅限于仿真环境，尚缺少在真实机器人硬件上的验证。在本研究中，我们利用现代且价格合理的电动马达，在硬件上实时仿真肌肉致动器特性。我们证明，该实验装置能在受学习策略控制的真实机器人上仿真简化的肌肉模型。我们通过推导阻尼规则改进了现有肌肉模型，确保该模型不仅具备高性能和稳定性，还能针对真实硬件进行参数调节。我们的策略完全在仿真环境中通过强化学习训练，结果表明，先前报道的肌肉优势可拓展至四足机器人的行走与跳跃任务：学习所得策略具有更强的鲁棒性，并展现出更规律的步态。最后，我们确认学习策略可在真实硬件上执行，并证明在实时仿真肌肉控制下，四足机器人可实现仿真到真实的迁移。这些结果表明，人工肌肉可成为未来一代鲁棒腿式机器人的高收益致动器。