下肢外骨骼的多目标人在回路贝叶斯优化
Multi-Objective Human-in-the-Loop Bayesian Optimization of a Lower-Limb Exoskeleton
- Georgia Institute of Technology（佐治亚理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出多目标人在回路贝叶斯优化（MO-HILBO），用于下肢外骨骼控制，同时优化代谢成本与舒适度，发现帕累托最优控制器，并开源mohilo包。
AI中文摘要：
人在回路优化（HILO）是一种常见的辅助设备控制优化方法，旨在考虑穿戴者独特的生物力学和主观偏好。然而，尽管研究表明，一个人对目标的优先级可能因环境、情绪或能量水平等时变因素而有所不同，现有的HILO方法仅考虑单一目标或对一组目标施加固定权重。这两种方法都无法表示个体对目标的偏好。在这项工作中，我们提出了多目标人在回路贝叶斯优化（MO-HILBO），该方法基于显式多目标贝叶斯优化，高效地推断个性化的帕累托最优控制器集合。我们将我们的方法与现有的多目标HILO方法进行比较，并在下肢外骨骼上针对两个目标进行实验验证：代谢成本（效率）和有序人类反馈（舒适度）。我们发现MO-HILBO（1）能够发现帕累托最优控制器，并且（2）帕累托前沿上点的成对排序与验证试验一致。最后，我们开源了mohilo，一个用于在可穿戴设备上运行HILO和MO-HILBO的Python包：此https URL。
英文摘要：
Human-in-the-loop optimization (HILO) is a common approach for optimizing the control of assistive devices to account for the wearer's unique biomechanics and subjective preferences. However, despite research suggesting that a person may have a different prioritization of objectives depending on time-varying factors such as the environment, their mood, or energy levels, existing HILO approaches only consider a single objective or enforce a fixed weighting on a set of objectives. Neither approach is capable of representing an individual's preferences over objectives. In this work, we propose Multi-Objective Human-in-the-loop Bayesian Optimization (MO-HILBO), which builds on explicit multi-objective Bayesian optimization to efficiently infer a personalized set of Pareto-optimal controllers. We compare our approach with an existing multi-objective HILO method and experimentally demonstrate MO-HILBO on a lower-limb exoskeleton across two objectives: metabolic cost (efficiency) and ordinal human feedback (comfort). We find that MO-HILBO (1) discovers Pareto-optimal controllers, and (2) that the pairwise ordering of points on the Pareto front itself is consistent with validation trials. Lastly, we open-source mohilo, a Python package for running both HILO and MO-HILBO on wearable devices: https://dynamicmobility.github.io/mohilo/.
