  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.
