让外骨骼自动识别异常动作并停用，提升真实环境安全性
Uncertainty-Aware Ankle Exoskeleton Control
- 用不确定性估计判断动作是否在训练数据范围内
- 在线测试中动作识别F1达89.2，可精准开关助力
- 适合希望外骨骼自主适应复杂场景的开发者
下肢外骨骼有望辅助人类运动，但现有控制器仅适用于预设动作和受控环境，限制了实际应用。本文提出一种不确定性感知控制框架，使踝关节外骨骼能在多种场景下安全运行，当遇到未见过的动作时自动断开助力。该方法通过不确定性估计器将动作分类为与训练数据相似（分布内）或不同（分布外）。我们在离线数据集上评估了三种架构（模型集成、自编码器、生成对抗网络），并在线测试表现最优的架构——步态相位估计器集成。在线实验表明，该不确定性估计器能随用户在分布内与分布外任务间切换，准确开启或关闭助力（F1: 89.2）。此框架为外骨骼在非结构化日常环境中安全自主支持人类运动提供了新路径。
原文摘要 · Abstract (English)
Lower limb exoskeletons show promise to assist human movement, but their utility is limited by controllers designed for discrete, predefined actions in controlled environments, restricting their real-world applicability. We present an uncertainty-aware control framework that enables ankle exoskeletons to operate safely across diverse scenarios by automatically disengaging when encountering unfamiliar movements. Our approach uses an uncertainty estimator to classify movements as similar (in-distribution) or different (out-of-distribution) relative to actions in the training set. We evaluated three architectures (model ensembles, autoencoders, and generative adversarial networks) on an offline dataset and tested the strongest performing architecture (ensemble of gait phase estimators) online. The online test demonstrated the ability of our uncertainty estimator to turn assistance on and off as the user transitioned between in-distribution and out-of-distribution tasks (F1: 89.2). This new framework provides a path for exoskeletons to safely and autonomously support human movement in unstructured, everyday environments.
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