Marjaninejad Ali, Urbina-Melendez Dario, Valero-Cuevas Francisco
Annu Int Conf IEEE Eng Med Biol Soc. 2020 Jul;2020:4687-4693. doi: 10.1109/EMBC44109.2020.9176182.
Error feedback is known to improve performance by correcting control signals in response to perturbations. Here we show how adding simple error feedback can also accelerate and robustify autonomous learning in a tendon-driven robot. We have implemented two versions of the General-to-Particular (G2P) autonomous learning algorithm using a tendon-driven leg with two joints and three tendons: one with and one without real-time kinematic feedback. We have performed a rigorous study on the performance of each system, for both simulation and physical implementation cases, over a wide range of tasks. As expected, feedback improved performance in simulation and hardware. However, we see these improvements even in the presence of sensory delays of up to 100 ms and when experiencing substantial contact collisions. Importantly, feedback accelerates learning and enhances G2P's continual refinement of the initial inverse map by providing the system with more relevant data to train on. This allows the system to perform well even after only 60 seconds of initial motor babbling.
已知错误反馈可通过响应扰动校正控制信号来提高性能。在此，我们展示了添加简单的错误反馈如何还能加速并增强肌腱驱动机器人的自主学习。我们使用具有两个关节和三根肌腱的肌腱驱动腿实现了通用到特定（G2P）自主学习算法的两个版本：一个带有实时运动学反馈，另一个没有。我们针对模拟和物理实现情况，在广泛的任务范围内对每个系统的性能进行了严格研究。正如预期的那样，反馈在模拟和硬件中都提高了性能。然而，即使存在高达100毫秒的感官延迟以及在经历大量接触碰撞时，我们也能看到这些改进。重要的是，反馈通过为系统提供更多相关数据进行训练，加速了学习并增强了G2P对初始逆映射的持续优化。这使得系统即使在仅进行60秒的初始运动咿呀学语后也能表现良好。
