A Robustness Analysis of Inverse Optimal Control of Bipedal Walking

TL;DR

This paper develops a method to infer cost functions underlying bipedal walking behaviors using inverse optimal control, tested on simulated models, with robustness to noise and model uncertainties, aiming to understand and replicate human-like motion.

Contribution

The paper introduces a novel IOC method for characterizing legged locomotion cost functions, capable of recovering known costs from simulated walking data despite noise and model uncertainties.

Findings

Successfully recovers known cost functions from simulated gait data.

Demonstrates robustness of IOC to sensor noise and model inaccuracies.

Provides a foundation for inferring human motion preferences.

Abstract

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.