论文标题
基于人类偏好的学习，用于对外骨骼步行步态的高维优化
Human Preference-Based Learning for High-dimensional Optimization of Exoskeleton Walking Gaits
论文作者
论文摘要
优化较低体外的外骨骼步行步态以进行用户舒适性，需要了解用户对高维步态参数空间的喜好。但是，现有的基于偏好的学习方法仅由于计算限制而探索了低维域。为了学习高维度的用户偏好，这项工作提出了Linecospar，这是一种基于人类的基于人类的偏好框架，可以通过迭代探索一维子空间来优化许多参数。此外，这项工作确定了跨用户更广泛偏好的步态属性。在模拟和人类试验中，我们从经验上验证了linecospar是一种高维偏好优化的样本效率方法。我们对实验数据的分析揭示了人类偏好与动态性的客观度量之间的对应关系，同时也突出了个体用户步态偏好的效用功能差异。该结果对外骨骼步态合成具有影响，这是一个活跃的领域，该领域适用于临床使用和患者康复。
Optimizing lower-body exoskeleton walking gaits for user comfort requires understanding users' preferences over a high-dimensional gait parameter space. However, existing preference-based learning methods have only explored low-dimensional domains due to computational limitations. To learn user preferences in high dimensions, this work presents LineCoSpar, a human-in-the-loop preference-based framework that enables optimization over many parameters by iteratively exploring one-dimensional subspaces. Additionally, this work identifies gait attributes that characterize broader preferences across users. In simulations and human trials, we empirically verify that LineCoSpar is a sample-efficient approach for high-dimensional preference optimization. Our analysis of the experimental data reveals a correspondence between human preferences and objective measures of dynamicity, while also highlighting differences in the utility functions underlying individual users' gait preferences. This result has implications for exoskeleton gait synthesis, an active field with applications to clinical use and patient rehabilitation.
