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.

翻译：逆最优控制（IOC）能够从人体运动中恢复最优代价函数权重或行为参数。现有IOC文献采用的方法要么基于缓慢的双层优化过程，要么基于快速但对噪声敏感的最优性条件违反最小化。本文假设人体运动采用等式约束最优控制模型，提出了一种更快速且鲁棒的IOC求解方法，该方法通过对双层方法进行单层重构来实现，并能获得等效结果。通过仿真数值实验，我们使用近期研究中广泛采用的类人平面触达任务，分析了所提出的单层重构方法相对于双层IOC公式的噪声鲁棒性。该方法展现出对极高噪声水平的耐受性，与此任务上的经典双层实现相比，将IOC计算时间缩短了15倍。