提出一种更快速且抗噪的逆最优控制方法，提升人体运动建模效率。
Reliability of Single-Level Equality-Constrained Inverse Optimal Control
- 将双层优化重构为单层问题，加速求解过程。
- 在高噪声下仍保持精度，计算速度提升15倍。
- 适合需要高效建模人类运动的仿真与机器人研究者。
逆最优控制（IOC）可从人类运动中恢复最优代价函数权重或行为参数。现有方法要么采用耗时的双层优化，要么快速但对噪声敏感。本文针对人体运动的等式约束最优控制模型，提出一种基于单层重构的更快且鲁棒的IOC方法，结果与传统双层法相当。通过模拟实验，在近期研究广泛使用的类人平面伸手任务上分析了该方法对噪声的鲁棒性。结果显示，该方法在极高噪声条件下仍具稳定性，相较经典双层实现，计算时间减少15倍。
原文摘要 · Abstract (English)
Inverse optimal control (IOC) allows the retrieval of optimal cost function weights, or behavioral parameters, from human motion. The literature on IOC uses methods that are either based on a slow bilevel process or a fast but noise-sensitive minimization of optimality condition violation. Assuming equality-constrained optimal control models of human motion, this article presents a faster but robust approach to solving IOC using a single-level reformulation of the bilevel method and yields equivalent results. Through numerical experiments in simulation, we analyze the robustness to noise of the proposed single-level reformulation to the bilevel IOC formulation with a human-like planar reaching task that is used across recent studies. The approach shows resilience to very large levels of noise and reduces the computation time of the IOC on this task by a factor of 15 when compared to a classical bilevel implementation.
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