Marjaninejad Ali, Tan Jie, Valero-Cuevas Francisco
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4680-4686. doi: 10.1109/EMBC44109.2020.9176089.
Passive elastic elements can contribute to stability, energetic efficiency, and impact absorption in both biological and robotic systems. They also add dynamical complexity which makes them more challenging to model and control. The impact of this added complexity to autonomous learning has not been thoroughly explored. This is especially relevant to tendon-driven limbs whose cables and tendons are inevitably elastic. Here, we explored the efficacy of autonomous learning and control on a simulated bio-plausible tendon-driven leg across different tendon stiffness values. We demonstrate that increasing stiffness of the simulated muscles can require more iterations for the inverse map to converge but can then perform more accurately, especially in discrete tasks. Moreover, the system is robust to subsequent changes in muscle stiffnesses and can adapt on-the-go within 5 attempts. Lastly, we test the system for the functional task of locomotion and found similar effects of muscle stiffness to learning and performance. Given that a range of stiffness values led to improved learning and maximized performance, we conclude the robot bodies and autonomous controllers-at least for tendon-driven systems-can be co-developed to take advantage of elastic elements. Importantly, this opens also the door to development efforts that recapitulate the beneficial aspects of the co-evolution of brains and bodies in vertebrates.
被动弹性元件有助于生物和机器人系统的稳定性、能量效率及冲击吸收。它们还增加了动力学复杂性，使得对其进行建模和控制更具挑战性。这种增加的复杂性对自主学习的影响尚未得到充分探索。这对于肌腱驱动的肢体尤为相关，其缆线和肌腱不可避免地具有弹性。在此，我们探讨了在不同肌腱刚度值下，自主学习和控制对模拟的具有生物合理性的肌腱驱动腿部的效果。我们证明，增加模拟肌肉的刚度可能需要更多迭代以使逆映射收敛，但随后可以更准确地执行，特别是在离散任务中。此外，该系统对肌肉刚度的后续变化具有鲁棒性，并且可以在5次尝试内即时适应。最后，我们测试了该系统的运动功能任务，发现肌肉刚度对学习和性能有类似影响。鉴于一系列刚度值可导致学习改善和性能最大化，我们得出结论，机器人身体和自主控制器——至少对于肌腱驱动系统而言——可以共同开发以利用弹性元件。重要的是，这也为重现脊椎动物大脑和身体共同进化有益方面的开发工作打开了大门。
