  Cost functions have the potential to provide compact and understandable
generalizations of motion. The goal of Inverse Optimal Control (IOC) is to
analyze an observed behavior which is assumed to be optimal with respect to an
unknown cost function, and infer this cost function. Here we develop a method
for characterizing cost functions of legged locomotion, with the goal of
representing complex humanoid behavior with simple models. To test this
methodology we simulate walking gaits of a simple 5 link planar walking model
which optimize known cost functions, and assess the ability of our IOC method
to recover them. In particular, the IOC method uses an iterative trajectory
optimization process to infer cost function weightings consistent with those
used to generate a single demonstrated optimal trial. We also explore
sensitivity of the IOC to sensor noise in the observed trajectory, imperfect
knowledge of the model or task, as well as uncertainty in the components of the
cost function used. With appropriate modeling, these methods may help infer
cost functions from human data, yielding a compact and generalizable
representation of human-like motion for use in humanoid robot controllers, as
well as providing a new tool for experimentally exploring human preferences.
