Maximum likelihood constraint inference is a powerful technique for identifying unmodeled constraints that affect the behavior of a demonstrator acting under a known objective function. However, it was originally formulated only for discrete state-action spaces. Continuous dynamics are more useful for modeling many real-world systems of interest, including the movements of humans and robots. We present a method to generate a tabular state-action space that approximates continuous dynamics and can be used for constraint inference on demonstrations that obey the true system dynamics. We then demonstrate accurate constraint inference on nonlinear pendulum systems with 2- and 4-dimensional state spaces, and show that performance is robust to a range of hyperparameters. The demonstrations are not required to be fully optimal with respect to the objective, and the most likely constraints can be identified even when demonstrations cover only a small portion of the state space. For these reasons, the proposed approach may be especially useful for inferring constraints on human demonstrators, which has important applications in human-robot interaction and biomechanical medicine.

翻译：最大可能性限制推断是确定影响在已知客观功能下行事的演示人行为的未建模限制的有力技术。 但是,它最初只针对离散的状态行动空间而设计。 连续动态对于模拟许多真实世界感兴趣的系统(包括人类和机器人的移动)更为有用。 我们提出了一个生成表列状态行动空间的方法,该表列状态行动空间与连续动态相近,并可用于限制符合真实系统动态的演示。 然后,我们展示出精确的约束性约束性推论,即具有2和4维状态空间的非线性钟摆设系统,并显示性能强于一系列的超参数。 演示不需要在目标方面完全优化,即使演示只覆盖国家空间的一小部分,也能够确定最可能存在的制约。 出于这些原因,拟议方法对于推断人类示威者所受的限制可能特别有用,这些限制在人体-机器人相互作用和生物机械医学中具有重要用途。