让仿生机器人在肌肉断裂后仍能稳定运动，靠的是在线学习感知网络。
Robust Continuous Motion Strategy Against Muscle Rupture using Online Learning of Redundant Intersensory Networks for Musculoskeletal Humanoids
- 构建神经网络建模身体传感器间关系，实现在线学习。
- 检测肌肉断裂并动态更新感知关系，保持运动控制。
- 适合研究仿生机器人容错与鲁棒运动控制的学者。
仿生肌肉机器人具有多种类生物优势，其中冗余肌肉结构是关键特征之一。该结构支持可变刚度控制，并能在某一冗余肌肉断裂时仍维持关节运动，但这一特性尚未被充分探索。本研究构建了神经网络，用于表征仿生人形机器人柔性、难建模躯体中各传感器之间的关系，并通过学习该网络实现精确运动。为利用肌肉冗余性，本文探讨了基于该网络的肌肉断裂检测、考虑断裂情况下的传感器关系在线更新，以及结合断裂信息进行的机体控制与状态估计。研究提出一种方法，使仿生机器人在单个肌肉断裂后仍能持续运动并完成任务，具备强鲁棒性。
原文摘要 · Abstract (English)
Musculoskeletal humanoids have various biomimetic advantages, of which redundant muscle arrangement is one of the most important features. This feature enables variable stiffness control and allows the robot to keep moving its joints even if one of the redundant muscles breaks, but this has been rarely explored. In this study, we construct a neural network that represents the relationship among sensors in the flexible and difficult-to-modelize body of the musculoskeletal humanoid, and by learning this neural network, accurate motions can be achieved. In order to take advantage of the redundancy of muscles, we discuss the use of this network for muscle rupture detection, online update of the intersensory relationship considering the muscle rupture, and body control and state estimation using the muscle rupture information. This study explains a method of constructing a musculoskeletal humanoid that continues to move and perform tasks robustly even when one muscle breaks.
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