Robotic lower-limb exoskeletons can augment human mobility, but current systems require extensive, context-specific considerations, limiting their real-world viability. Here, we present a unified exoskeleton control framework that autonomously adapts assistance on the basis of instantaneous user joint moment estimates from a temporal convolutional network (TCN). When deployed on our hip exoskeleton, the TCN achieved an average root mean square error of 0.142 newton-meters per kilogram across 35 ambulatory conditions without any user-specific calibration. Further, the unified controller significantly reduced user metabolic cost and lower-limb positive work during level-ground and incline walking compared with walking without wearing the exoskeleton. This advancement bridges the gap between in-lab exoskeleton technology and real-world human ambulation, making exoskeleton control technology viable for a broad community.

中文翻译：

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估计人体关节力矩统一外骨骼控制，减少用户的工作量

机器人下肢外骨骼可以增强人类的活动能力，但当前的系统需要广泛的、特定于环境的考虑，限制了它们在现实世界中的可行性。在这里，我们提出了一个统一的外骨骼控制框架，该框架可以根据时间卷积网络（TCN）的瞬时用户关节力矩估计自动调整辅助。当部署在我们的髋部外骨骼上时，TCN 在 35 种动态条件下实现了每公斤 0.142 牛顿米的平均均方根误差，无需任何用户特定的校准。此外，与不佩戴外骨骼行走相比，统一控制器显着降低了用户在平地和倾斜行走期间的代谢成本和下肢积极工作。这一进步弥合了实验室外骨骼技术与现实世界人类行走之间的差距，使外骨骼控制技术在广泛的社区中可行。