Energy-Based Continuous Inverse Optimal Control

• Main Authors: Baker, C. (Author), Wu, Y.N (Author), Xie, J. (Author), Xu, Y. (Author), Zhao, T. (Author), Zhao, Y. (Author)
• Format: Article
• Language: English
• Published: Institute of Electrical and Electronics Engineers Inc. 2022
• Subjects: Approximation algorithms; Autonomous vehicles; Autonomous Vehicles; Cooperative learning; Cost benefit analysis; Cost function; Cost functions; Cost-function; Costs; Energy-based model; Energy-based models; energy-based models (EBMs); Generator; Generators; Heuristic algorithms; Heuristics algorithm; Inverse optimal control; inverse optimal control (IOC); Inverse problems; Inverse-optimal control; Langevin dynamic.; Langevin dynamics; Langevin dynamics.; Maximum likelihood estimation; Maximum-likelihood estimation; Optimal control; Optimal control systems; Optimal controls; Probability density function; Trajectories; Trajectory
• Online Access: View Fulltext in Publisher

 The problem of continuous inverse optimal control (over finite time horizon) is to learn the unknown cost function over the sequence of continuous control variables from expert demonstrations. In this article, we study this fundamental problem in the framework of energy-based model (EBM), where the observed expert trajectories are assumed to be random samples from a probability density function defined as the exponential of the negative cost function up to a normalizing constant. The parameters of the cost function are learned by maximum likelihood via an ``analysis by synthesis'' scheme, which iterates: 1) synthesis step: sample the synthesized trajectories from the current probability density using the Langevin dynamics via backpropagation through time and 2) analysis step: update the model parameters based on the statistical difference between the synthesized trajectories and the observed trajectories. Given the fact that an efficient optimization algorithm is usually available for an optimal control problem, we also consider a convenient approximation of the above learning method, where we replace the sampling in the synthesis step by optimization. Moreover, to make the sampling or optimization more efficient, we propose to train the EBM simultaneously with a top-down trajectory generator via cooperative learning, where the trajectory generator is used to fast initialize the synthesis step of the EBM. We demonstrate the proposed methods on autonomous driving tasks and show that they can learn suitable cost functions for optimal control. IEEE