Methods in Large Scale Inverse Optimal Control

As our technology continues to evolve, so does the complexity of the problems that we expect our systems to solve. The challenge is that these problems come at increasing scales that require innovative solutions in order to be tackled efficiently. The key idea behind Inverse Optimal Control (IOC) is that we can learn to emulate how a human completes these complex tasks by modeling the observed decision process. This thesis presents algorithms that extend the state-of-the art in IOC in order to efficiently learn complex models of human behavior. We explore the use of an admissible heuristic in estimating path distributions through weighted graphs. This includes a modified version of the softened policy iteration method used in Maximum Entropy Inverse Optimal Control and present the SoftStar algorithm which merges ideas from Maximum Entropy IOC and A* Search for an efficient probabilistic search method that estimates path distributions through weighted graphs with approximation guarantees. We then explore IOC methods for prediction and planning in problems with linear dynamics that require real-time solutions. This includes an inverse linear quadratic regulation (LQR) method for efficiently predicting intent in 3-dimensional space and a discrete-continuous hybrid version of inverse LQR that uses discrete waypoints to guide the continuous LQR distribution. The presented techniques are evaluated on a number of different problem settings including planning trajectories of handwritten characters, modeling the ball-handler decision process in professional soccer, predicting intent in completing household tasks, and planning robotic motion trajectories through a cluttered workspace.