Bio-Inspired Robotics Laboratory, Department of Engineering, University of Cambridge, Cambridge, United Kingdom.
Neuro-Robotics Laboratory, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai 980-8579, Japan.
Bioinspir Biomim. 2024 Jun 10;19(4). doi: 10.1088/1748-3190/ad5129.
Vertebrates possess a biomechanical structure with redundant muscles, enabling adaptability in uncertain and complex environments. Harnessing this inspiration, musculoskeletal systems offer advantages like variable stiffness and resilience to actuator failure and fatigue. Despite their potential, the complex structure presents modelling challenges that are difficult to explicitly formulate and control. This difficulty arises from the need for comprehensive knowledge of the musculoskeletal system, including details such as muscle arrangement, and fully accessible muscle and joint states. Whilst existing model-free methods do not need explicit formulations, they also underutilise the benefits of muscle redundancy. Consequently, they necessitate retraining in the event of muscle failure and require manual tuning of parameters to control joint stiffness limiting their applications under unknown payloads. Presented here is a model-free local inverse statics controller for musculoskeletal systems, employing a feedforward neural network trained on motor babbling data. Experiments with a musculoskeletal leg model showcase the controller's adaptability to complex structures, including mono and bi-articulate muscles. The controller can compensate for changes such as weight variations, muscle failures, and environmental interactions, retaining reasonable accuracy without the need for any additional retraining.
脊椎动物拥有具有冗余肌肉的生物力学结构，使其能够适应不确定和复杂的环境。受此启发，肌肉骨骼系统具有可变刚度和对执行器故障和疲劳的弹性等优势。尽管它们具有潜力，但复杂的结构带来了建模挑战，这些挑战难以明确制定和控制。这种困难源于对肌肉骨骼系统的全面了解的需要，包括肌肉排列等细节，以及对肌肉和关节状态的完全访问。虽然现有的无模型方法不需要显式公式，但它们也没有充分利用肌肉冗余的优势。因此，如果发生肌肉故障，它们需要重新训练，并且需要手动调整参数来控制关节刚度，这限制了它们在未知有效载荷下的应用。本文提出了一种用于肌肉骨骼系统的无模型局部逆静力学控制器，该控制器使用基于电机喋喋不休数据训练的前馈神经网络。使用肌肉骨骼腿模型进行的实验展示了控制器对复杂结构的适应性，包括单关节和双关节肌肉。该控制器可以补偿体重变化、肌肉故障和环境交互等变化，在不需要任何额外训练的情况下保持合理的准确性。
